sentiment analysis in market research

Sentiment analysis, or opinion mining, is the process of analyzing large volumes of text to determine whether it expresses a positive sentiment, a negative sentiment or a neutral sentiment.

Companies now have access to more data about their customers than ever before, presenting both an opportunity and a challenge: analyzing the vast amounts of textual data available and extracting meaningful insights to guide their business decisions.

From emails and tweets to online survey responses, chats with customer service representatives and reviews, the sources available to gauge customer sentiment are seemingly endless. Sentiment analysis systems help companies better understand their customers, deliver stronger customer experiences and improve their brand reputation.

Read why IBM was named a leader in the 2023 Gartner® Magic Quadrant™ for Cloud AI Developer Services report.

Register for the white paper on AI governance

With more ways than ever for people to express their feelings online, organizations need powerful tools to monitor what’s being said about them and their products and services in near real time. As companies adopt sentiment analysis and begin using it to analyze more conversations and interactions, it will become easier to identify customer friction points at every stage of the customer journey.

Deliver more objective results from customer reviews

The latest artificial intelligence (AI) sentiment analysis tools help companies filter reviews and net promoter scores (NPS) for personal bias and get more objective opinions about their brand, products and services. For example, if a customer expresses a negative opinion along with a positive opinion in a review, a human assessing the review might label it negative before reaching the positive words. AI-enhanced sentiment classification helps sort and classify text in an objective manner, so this doesn’t happen, and both sentiments are reflected.  

Achieve greater scalability of business intelligence programs

Sentiment analysis enables companies with vast troves of unstructured data to analyze and extract meaningful insights from it quickly and efficiently. With the amount of text generated by customers across digital channels, it’s easy for human teams to get overwhelmed with information. Strong, cloud-based, AI-enhanced customer sentiment analysis tools help organizations deliver business intelligence from their customer data at scale, without expending unnecessary resources.

Perform real-time brand reputation monitoring

Modern enterprises need to respond quickly in a crisis. Opinions expressed on social media, whether true or not, can destroy a brand reputation that took years to build. Robust, AI-enhanced sentiment analysis tools help executives monitor the overall sentiment surrounding their brand so they can spot potential problems and address them swiftly.

Sentiment analysis uses natural language processing (NLP) and machine learning (ML) technologies to train computer software to analyze and interpret text in a way similar to humans. The software uses one of two approaches, rule-based or ML—or a combination of the two known as hybrid. Each approach has its strengths and weaknesses; while a rule-based approach can deliver results in near real-time, ML based approaches are more adaptable and can typically handle more complex scenarios.

Rule-based sentiment analysis

In the rule-based approach, software is trained to classify certain keywords in a block of text based on groups of words, or lexicons, that describe the author’s intent. For example, words in a positive lexicon might include “affordable,” “fast” and “well-made,” while words in a negative lexicon might feature “expensive,” “slow” and “poorly made”. The software then scans the classifier for the words in either the positive or negative lexicon and tallies up a total sentiment score based on the volume of words used and the sentiment score of each category.

Machine learning sentiment analysis

With a machine learning (ML) approach, an algorithm is used to train software to gauge sentiment in a block of text using words that appear in the text as well as the order in which they appear. Developers use sentiment analysis algorithms to teach software how to identify emotion in text similarly to the way humans do. ML models continue to “learn” from the data they are fed, hence the name “machine learning”. Here are a few of the most commonly used classification algorithms:

Linear regression: A statistics algorithm that describes a value (Y) based on a set of features (X).

Naive Bayes: An algorithm that uses Bayes’ theorem to categorize words in a block of text.

Support vector machines: A fast and efficient classification algorithm used to solve two-group classification problems.

Deep learning (DL): Also known as an artificial neural network, deep learning is an advanced machine learning technique that links together multiple algorithms to mimic human brain function.

The hybrid approach

A hybrid approach to text analysis combines both ML and rule-based capabilities to optimize accuracy and speed. While highly accurate, this approach requires more resources, such as time and technical capacity, than the other two.

In addition to the different approaches used to build sentiment analysis tools, there are also different types of sentiment analysis that organizations turn to depending on their needs. The three most popular types, emotion based, fine-grained and aspect-based sentiment analysis (ABSA) all rely on the underlying software’s capacity to gauge something called polarity, the overall feeling that is conveyed by a piece of text.

Generally speaking, a text’s polarity can be described as either positive, negative or neutral, but by categorizing the text even further, for example into subgroups such as “extremely positive” or “extremely negative,” some sentiment analysis models can identify more subtle and complex emotions. The polarity of a text is the most commonly used metric for gauging textual emotion and is expressed by the software as a numerical rating on a scale of one to 100. Zero represents a neutral sentiment and 100 represents the most extreme sentiment.

Here are the three most widely used types of sentiment analysis:

Fine-grained (graded)

Fine-grained, or graded, sentiment analysis is a type of sentiment analysis that groups text into different emotions and the level of emotion being expressed. The emotion is then graded on a scale of zero to 100, similar to the way consumer websites deploy star-ratings to measure customer satisfaction.

Aspect-based (ABSA)

Aspect based sentiment analysis (ABSA) narrows the scope of what’s being examined in a body of text to a singular aspect of a product, service or customer experience a business wishes to analyze. For example, a budget travel app might use ABSA to understand how intuitive a new user interface is or to gauge the effectiveness of a customer service chatbot. ABSA can help organizations better understand how their products are succeeding or falling short of customer expectations.

Emotional detection

Emotional detection sentiment analysis seeks to understand the psychological state of the individual behind a body of text, including their frame of mind when they were writing it and their intentions. It is more complex than either fine-grained or ABSA and is typically used to gain a deeper understanding of a person’s motivation or emotional state. Rather than using polarities, like positive, negative or neutral, emotional detection can identify specific emotions in a body of text such as frustration, indifference, restlessness and shock.

Organizations conduct sentiment analysis for a variety of reasons. Here are some of the most popular use cases.  

Support teams use sentiment analysis to deliver more personalized responses to customers that accurately reflect the mood of an interaction. AI-based chatbots that use sentiment analysis can spot problems that need to be escalated quickly and prioritize customers in need of urgent attention. ML algorithms deployed on customer support forums help rank topics by level-of-urgency and can even identify customer feedback that indicates frustration with a particular product or feature. These capabilities help customer support teams process requests faster and more efficiently and improve customer experience.

By using sentiment analysis to conduct social media monitoring brands can better understand what is being said about them online and why. For example, is a new product launch going well? Monitoring sales is one way to know, but will only show stakeholders part of the picture. Using sentiment analysis on customer review sites and social media to identify the emotions being expressed about the product will enable a far deeper understanding of how it is landing with customers.

By turning sentiment analysis tools on the market in general and not just on their own products, organizations can spot trends and identify new opportunities for growth. Maybe a competitor’s new campaign isn’t connecting with its audience the way they expected, or perhaps someone famous has used a product in a social media post increasing demand. Sentiment analysis tools can help spot trends in news articles, online reviews and on social media platforms, and alert decision makers in real time so they can take action.

While sentiment analysis and the technologies underpinning it are growing rapidly, it is still a relatively new field. According to “Sentiment Analysis,” by Liu Bing (2020) the term has only been widely used since 2003. 1 There is still much to be learned and refined, here are some of the most common drawbacks and challenges.

Lack of context

Context is a critical component for understanding what emotion is being expressed in a block of text and one that frequently causes sentiment analysis tools to make mistakes. On a customer survey, for example, a customer might give two answers to the question: “What did you like about our app?” The first answer might be “functionality” and the second, “UX”. If the question being asked was different, for example, “What didn’t you like about our app?” it changes the meaning of the customer’s response without changing the words themselves. To correct this problem, the algorithm would need to be given the original context of the question the customer was responding to, a time-consuming tactic known as pre or post  processing.

Use of irony and sarcasm

Regardless of the level or extent of its training, software has a hard time correctly identifying irony and sarcasm in a body of text. This is because often when someone is being sarcastic or ironic it’s conveyed through their tone of voice or facial expression and there is no discernable difference in the words they’re using. For example, when analyzing the phrase, “Awesome, another thousand-dollar parking ticket—just what I need,” a sentiment analysis tool would likely mistake the nature of the emotion being expressed and label it as positive because of the use of the word “awesome”.

Negation is when a negative word is used to convey a reversal of meaning in a sentence. For example, consider the sentence, “I wouldn’t say the shoes were cheap." What’s being expressed, is that the shoes were probably expensive, or at least moderately priced, but a sentiment analysis tool would likely miss this subtlety.  

Idiomatic language

Idiomatic language, such as the use of—for example—common English phrases like “Let’s not beat around the bush,” or “Break a leg ,” frequently confounds sentiment analysis tools and the ML algorithms that they’re built on. When human language phrases like the ones above are used on social media channels or in product reviews, sentiment analysis tools will either incorrectly identify them—the “break a leg” example could be incorrectly identified as something painful or sad, for example—or miss them completely.

Organizations who decide they want to deploy sentiment analysis to better understand their customers have two options for how they can go about it: either purchase an existing tool or build one of their own.

Businesses opting to build their own tool typically use an open-source library in a common coding language such as Python or Java. These libraries are useful because their communities are steeped in data science. Still, organizations looking to take this approach will need to make a considerable investment in hiring a team of engineers and data scientists.

Acquiring an existing software as a service (SaaS) sentiment analysis tool requires less initial investment and allows businesses to deploy a pre-trained machine learning model rather than create one from scratch. SaaS sentiment analysis tools can be up and running with just a few simple steps and are a good option for businesses who aren’t ready to make the investment necessary to build their own.

Today’s most effective customer support sentiment analysis solutions use the power of AI and ML to improve customer experiences. IBM watsonx Assistant is a market leading, conversational artificial intelligence platform powered by large language models (LLMs) that enables organizations to build AI-powered voice agents and chatbots that deliver superior automated self-service support to their customers on a simple, easy-to-use interface.

Discover how artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

Gain a deeper understanding of machine learning along with important definitions, applications and concerns within businesses today.

Learn about the importance of mitigating bias in sentiment analysis and see how AI is being trained to be more neutral, unbiased and unwavering.

IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

1 “Sentiment Analysis (Second edition),"  (link resides outside ibm.com), Liu, Bing, Cambridge University Press, September 23, 2020

What is Sentiment Analysis? Guide, Tools, Uses, Examples

Appinio Research · 12.03.2024 · 30min read

Have you ever wondered how businesses can understand what people really think about their products or services just by analyzing online reviews and social media comments? Sentiment analysis, also known as opinion mining, is the key to unlocking these insights. It's like having a superpower to decipher whether people are happy, frustrated, or indifferent from the words they write. Sentiment analysis uses algorithms to analyze text data and determine the sentiment or opinion expressed within it. From understanding customer feedback to tracking brand perception and even predicting election outcomes, sentiment analysis plays a crucial role in today's data-driven world. In this guide, we'll explore everything you need to get started with sentiment analysis. Whether you're a business looking to improve customer satisfaction or a researcher diving into the world of natural language processing, we will equip you with the knowledge and tools to effectively harness the power of sentiment analysis.

What is Sentiment Analysis?

Sentiment analysis, also known as opinion mining, is the process of analyzing text to determine the sentiment or opinion expressed within it. Whether it's understanding customer feedback , tracking brand perception on social media , or analyzing public sentiment toward political candidates, sentiment analysis helps uncover valuable insights from textual data.

Importance of Sentiment Analysis

• Informed Decision Making : By analyzing sentiment, businesses can make data-driven decisions, identify trends, and adapt strategies accordingly.
• Customer Satisfaction : Understanding customer sentiment allows businesses to address concerns, improve products/services, and enhance overall customer satisfaction.
• Brand Monitoring : Sentiment analysis enables organizations to monitor brand perception, identify potential reputation risks, and engage with customers proactively.
• Market Intelligence : Analyzing sentiment in market research helps businesses understand consumer preferences, competitive landscapes, and emerging trends.

Sentiment Analysis Challenges and Limitations

• Ambiguity and Context : Sentiment analysis algorithms may struggle to understand nuances such as sarcasm, irony, and cultural context.
• Data Quality : Poorly structured or noisy data can impact the accuracy of sentiment analysis results.
• Subjectivity : Sentiment interpretation can be subjective and context-dependent, leading to variability in results.
• Language and Cultural Differences : Sentiment analysis models may perform differently across languages and cultures due to linguistic variations and cultural nuances.

Fundamentals of Sentiment Analysis

Sentiment analysis relies on several fundamental techniques and concepts to accurately analyze text data and extract meaningful insights about sentiment and emotions.

Text Preprocessing Techniques

Text preprocessing is a crucial step in sentiment analysis, as it involves cleaning and transforming raw text data into a format suitable for analysis. Here are some standard text preprocessing techniques:

• Tokenization : Tokenization involves breaking down text into individual words or tokens. This process is essential for further analysis as it allows the model to understand the structure of the text.
• Normalization : Normalization techniques ensure consistency in text data by converting all words to lowercase and removing punctuation marks. This prevents the model from treating the same word with different capitalizations as different entities.
• Stopword Removal : Stopwords are common words that do not carry significant meaning, such as "and", "the", and "is". Removing stopwords helps reduce noise in the data and improves the efficiency of sentiment analysis algorithms.
• Stemming and Lemmatization : Stemming and lemmatization are techniques used to reduce words to their root form. Stemming involves removing prefixes and suffixes from words to obtain their root, while lemmatization maps words to their base or dictionary form. This process helps reduce the dimensionality of the feature space and improve model performance.

Feature Extraction Methods

Feature extraction is the process of transforming raw text data into numerical features that can be used as input to machine learning models. Some common feature extraction methods used in sentiment analysis include:

• Bag-of-Words (BoW) : The bag-of-words model represents text as a sparse matrix of word frequencies. Each document is represented as a vector where each element corresponds to the frequency of a particular word in the document. While simple and easy to implement, BoW does not consider the order of words in the text.
• Term Frequency-Inverse Document Frequency (TF-IDF) : TF-IDF is a statistical measure used to evaluate the importance of a word in a document relative to a corpus. It assigns higher weights to words that are frequent in a document but rare in the overall corpus, making it helpful in identifying important keywords.
• Word Embeddings : Word embeddings are dense, low-dimensional representations of words learned from large text corpora using techniques like Word2Vec, GloVe, or FastText. Word embeddings capture semantic relationships between words, allowing models to generalize unseen data better and improve performance in sentiment analysis tasks.

Types of Sentiment Analysis

Sentiment analysis can be performed at different levels of granularity, depending on the scope of analysis and the specific objectives. Some common types of sentiment analysis include:

• Document-level Sentiment Analysis : In document-level sentiment analysis, the sentiment of an entire document, such as a review, article, or tweet, is analyzed as a whole. This approach provides an overall assessment of sentiment but may overlook nuances present at the sentence or phrase level.
• Sentence-level Sentiment Analysis : Sentence-level sentiment analysis focuses on analyzing the sentiment expressed within individual sentences. This approach allows for more fine-grained analysis and can capture variations in sentiment within a document.
• Aspect-based Sentiment Analysis : Aspect-based sentiment analysis aims to identify the sentiment associated with specific aspects or features of a product, service, or topic. This approach is instrumental in product reviews, where different aspects (e.g., performance, design, price) may elicit different sentiments.

Supervised vs. Unsupervised Learning Approaches

In sentiment analysis, machine learning algorithms can be categorized into supervised and unsupervised learning approaches based on the availability of labeled training data.

• Supervised Learning : In supervised learning, sentiment analysis models are trained on labeled datasets where each text sample is associated with a sentiment label (e.g., positive, negative, neutral). Supervised learning approaches require a significant amount of labeled data for training but often yield more accurate results, especially in well-defined sentiment classification tasks.
• Unsupervised Learning : Unsupervised learning approaches do not rely on labeled data for training. Instead, these models use techniques like clustering or dimensionality reduction to identify patterns and structures in the data without explicit supervision. Unsupervised learning can be useful in scenarios where labeled data is scarce or expensive to obtain, but it may require additional effort in interpreting the results and tuning parameters.

Understanding these fundamental concepts is essential for building effective sentiment analysis models and extracting meaningful insights from text data.

Data Collection for Sentiment Analysis

Before diving into sentiment analysis, you must ensure your data is well-prepared and structured for analysis.

Sourcing Data

Sourcing data is the first step in any data-driven analysis, including sentiment analysis. Depending on your specific application, you may collect data from various sources such as social media platforms, review websites, surveys , or feedback forms.

• Data Relevance : Ensure that the data you collect is relevant to your analysis objectives. For example, if you're analyzing sentiment towards a particular product, collect data from sources where users discuss or review that product.
• Data Volume : Aim to collect a sufficient volume of data to train robust sentiment analysis models. Larger datasets often result in more accurate and generalizable models.
• Data Quality : Pay attention to the quality of the data you collect. Noisy or unstructured data can adversely affect the performance of sentiment analysis models. Consider using data validation techniques to ensure data quality.

To unlock the full potential of sentiment analysis, it's crucial to lay a solid foundation with well-prepared data. By sourcing relevant, high-quality data and following best data collection and preparation practices, you set the stage for more accurate and insightful sentiment analysis outcomes. Appinio empowers you to seamlessly collect and analyze real-time consumer insights, providing the data-driven foundation you need to make informed decisions.   Ready to experience the power of Appinio? Book a demo today and discover how our platform can supercharge your sentiment analysis efforts!

Book a Demo

Data Cleaning and Labeling

Once you've collected your data, the next step is to clean and label it for analysis. Data cleaning involves preprocessing the raw text data to remove noise and irrelevant information, while labeling involves assigning sentiment labels to the data samples. Here are some data cleaning and labeling techniques:

• Text Preprocessing : Apply text preprocessing techniques such as tokenization, normalization, stopword removal, and stemming/lemmatization to clean the text data and standardize its format.
• Noise Removal : Eliminate irrelevant information from the text data, such as HTML tags, special characters, or non-textual content like emojis or symbols.
• Labeling Guidelines : Define clear guidelines for labeling sentiment in your data. Decide on the sentiment categories (e.g., positive, negative, neutral) and establish criteria for assigning labels to data samples.
• Manual Labeling : In cases where sentiment labels cannot be inferred automatically, manually label the data by human annotators. Ensure inter-annotator agreement and consistency in labeling to maintain data quality.

Handling Imbalanced Data

Imbalanced datasets, where one sentiment class is significantly more prevalent than others, are common in sentiment analysis tasks. Imbalanced data can bias model predictions towards the majority class and lead to suboptimal performance. Here are some strategies for handling imbalanced data:

• Resampling Techniques : Balance the dataset by either oversampling the minority class or undersampling the majority class. Oversampling involves duplicating samples from the minority class, while undersampling involves randomly removing samples from the majority class.
• Synthetic Minority Over-sampling Technique (SMOTE) : SMOTE is a popular oversampling technique that generates synthetic samples from the minority class using interpolation.
• Class Weighting : Adjust the class weights during model training to penalize misclassifications in the minority class more heavily. This helps the model prioritize learning from the minority class examples.
• Ensemble Methods : Ensemble methods like bagging and boosting can improve model performance on imbalanced data by combining predictions from multiple base models trained on different subsets of the data.

Splitting Data for Training and Testing

To evaluate the performance of sentiment analysis models, splitting the data into separate training and testing sets is essential. The training set is used to train the model, while the testing set is used to evaluate its performance on unseen data.

• Training-Testing Split Ratio : Determine the ratio of data to allocate to the training and testing sets. A common split is 80% for training and 20% for testing, but this can vary depending on the size of your dataset and the complexity of your model.
• Cross-Validation : Consider using cross-validation techniques such as k-fold cross-validation to assess model performance more robustly. Cross-validation involves partitioning the data into multiple subsets (folds) and training/testing the model on different combinations of these subsets.

By effectively sourcing, cleaning, labeling, and preparing your data, you'll set a solid foundation for building accurate and reliable sentiment analysis models.

Machine Learning Models for Sentiment Analysis

When it comes to sentiment analysis, a variety of machine learning models can be employed to analyze text data and extract sentiment. We'll explore some of the most popular models, techniques, and metrics commonly used in sentiment analysis tasks.

Traditional Models

Traditional machine learning models have been widely used in sentiment analysis tasks, offering simplicity and interpretability. Some common traditional models include:

• Naive Bayes : Naive Bayes is a probabilistic classifier based on Bayes' theorem, often used in text classification tasks including sentiment analysis. Despite its simplicity, Naive Bayes can achieve competitive performance, especially with large datasets.
• Support Vector Machines (SVM) : SVM is a supervised learning algorithm that aims to find the optimal hyperplane to separate different classes. SVMs are particularly effective in high-dimensional spaces, making them suitable for sentiment analysis tasks with large feature spaces.

Deep Learning Models

Deep learning models have revolutionized sentiment analysis by leveraging the power of neural networks to learn complex patterns from data. Some popular deep learning models for sentiment analysis include:

• Recurrent Neural Networks (RNNs) : RNNs are designed to process sequential data and are well-suited for analyzing text data. They can capture dependencies between words in a sequence, making them effective for tasks like sentiment analysis. However, RNNs suffer from the vanishing gradient problem and struggle to capture long-range dependencies.
• Convolutional Neural Networks (CNNs) : CNNs excel at capturing local patterns in data through convolutional filters. In sentiment analysis, CNNs can learn to extract relevant features from text data, such as n-grams or word sequences, and classify sentiment based on these features.

Transfer Learning Techniques

Transfer learning techniques have gained popularity in sentiment analysis, allowing models to leverage pre-trained representations of language from large corpora. Some transfer learning techniques for sentiment analysis include:

• BERT (Bidirectional Encoder Representations from Transformers) : BERT is a pre-trained language model that has been fine-tuned for various NLP tasks, including sentiment analysis. By fine-tuning BERT on task-specific data, you can leverage its contextual understanding of language to improve sentiment analysis performance.
• GPT (Generative Pre-trained Transformer) : GPT is another pre-trained transformer-based model that can be fine-tuned for sentiment analysis tasks. GPT generates text by predicting the next word in a sequence, making it well-suited for generating text-based sentiment predictions.

Evaluation Metrics

To assess the performance of sentiment analysis models, various evaluation metrics can be used to measure their accuracy and effectiveness. These evaluation metrics include:

• Accuracy : Accuracy measures the proportion of correctly classified samples out of the total samples in the dataset. While accuracy provides a general indication of model performance, it may not be suitable for imbalanced datasets.
• Precision : Precision measures the proportion of true positive predictions among all positive predictions made by the model. It indicates the model's ability to avoid false positives.
• Recall : Recall measures the proportion of true positive predictions among all actual positive samples in the dataset. It indicates the model's ability to capture all positive instances in the dataset.
• F1 Score : The F1 score is the harmonic mean of precision and recall, providing a balanced measure of a model's performance. It considers both false positives and false negatives and is particularly useful for imbalanced datasets.

By understanding and selecting appropriate machine learning models and evaluation metrics, you can build robust sentiment analysis systems that accurately capture and analyze sentiment from text data.

Sentiment Analysis Uses and Applications

Here are some examples showcasing the versatility and effectiveness of sentiment analysis. They help provide concrete illustrations of how sentiment analysis is applied in various contexts, shedding light on its practical implications and potential benefits.

Customer Feedback Analysis

Imagine you're a business owner receiving numerous online reviews about your products or services. By applying sentiment analysis to these reviews, you can categorize them into positive, negative, or neutral sentiments.

For instance, positive reviews might highlight aspects like excellent customer service or product quality, while negative reviews could point out areas for improvement, such as shipping delays or product defects. Analyzing the sentiment distribution over time allows you to track trends, identify recurring issues, and proactively address customer concerns, ultimately enhancing customer satisfaction and loyalty .

Social Media Monitoring

Social media platforms like Twitter, Facebook, and Instagram are rich sources of user-generated content, offering valuable insights into public sentiment and opinion. Sentiment analysis enables organizations to monitor real-time brand mentions, hashtags, and comments, gauging public sentiment toward their brand, products, or campaigns.

For example, during a product launch, sentiment analysis can help assess the initial reception, identify influencers or brand advocates, and respond promptly to any negative feedback or issues users raise. By actively engaging with the online community and addressing concerns, businesses can build trust, foster positive relationships, and cultivate a strong brand reputation.

Market Research and Competitive Analysis

In market research , sentiment analysis is a powerful tool for understanding consumer preferences, market trends, and competitive landscapes. For instance, analyzing sentiment in product reviews and online forums can reveal emerging trends, feature preferences, and competitor strengths and weaknesses.

By identifying market gaps and customer pain points, businesses can tailor their offerings to meet consumer needs more effectively, gain a competitive edge, and capitalize on new opportunities. Moreover, sentiment analysis can aid in benchmarking against competitors , providing valuable insights into how your brand stacks up in terms of customer sentiment and satisfaction.

Political Sentiment Analysis

During elections or political campaigns, sentiment analysis is pivotal in gauging public sentiment toward candidates, parties, and policy issues. Political analysts can track sentiment trends, identify key influencers, and assess voter sentiment in real time by analyzing social media conversations , news articles, and public forums. This information can inform campaign strategies, messaging tactics, and policy priorities, helping political candidates and parties connect with voters, address concerns, and shape public perception effectively.

These examples demonstrate the diverse applications and benefits of sentiment analysis across various domains, highlighting its potential to drive informed decision-making, enhance customer experiences, and foster positive outcomes. By leveraging sentiment analysis effectively, organizations can gain valuable insights, mitigate risks, and stay ahead in today's data-driven world.

Advanced Topics in Sentiment Analysis

As sentiment analysis continues to evolve, researchers and practitioners explore advanced topics and techniques to enhance the accuracy and effectiveness of sentiment analysis systems.

Domain Adaptation

Domain adaptation refers to the process of adapting sentiment analysis models to new domains or contexts where labeled data may be scarce or non-representative . In real-world applications, sentiment analysis models trained on one domain may not perform well when applied to another domain due to differences in vocabulary, style, or sentiment expressions.

Domain adaptation techniques aim to mitigate this domain shift by leveraging transfer learning or domain-specific features to adapt the model to the target domain. Examples of domain adaptation techniques include unsupervised domain adaptation, where the model learns domain-invariant features, and adversarial training, where the model is trained to discriminate between source and target domains.

Multimodal Sentiment Analysis

Multimodal sentiment analysis integrates information from multiple modalities, such as text, images, audio, and video, to enhance the understanding of sentiment. In many real-world scenarios, sentiment is expressed not only through text but also through visual and auditory cues.

For example, in product reviews , sentiment can be conveyed through the tone of voice in videos or the facial expressions in images. Multimodal sentiment analysis techniques leverage deep learning architectures capable of processing multiple modalities simultaneously, enabling more comprehensive sentiment analysis. By integrating information from diverse modalities, multimodal sentiment analysis can provide richer and more nuanced insights into sentiment expressions.

Sentiment Analysis in Social Media

Social media platforms have become rich sources of user-generated content, making them valuable for sentiment analysis. However, sentiment analysis in social media poses unique challenges due to the informal language, short text lengths, and the prevalence of sarcasm and irony. Social media sentiment analysis techniques often involve preprocessing steps tailored to handle noisy and informal text, such as hashtags, emojis, and slang.

Additionally, sentiment analysis models for social media may incorporate user-specific features, such as user profiles and social connections, to improve sentiment prediction accuracy. Sentiment analysis in social media enables organizations to monitor brand perception, track public sentiment toward specific topics or events, and identify emerging trends and influencers.

Handling Context and Sarcasm

Understanding context and sarcasm is essential for accurate sentiment analysis, as text often contains subtle nuances that influence sentiment interpretation. Contextual sentiment analysis techniques aim to capture the context surrounding text snippets to infer the intended sentiment accurately. This may involve analyzing preceding or succeeding text segments, identifying sentiment modifiers, or considering the broader context of the conversation.

Sarcasm detection in sentiment analysis presents a particularly challenging task, as sarcastic expressions often convey sentiments opposite to their literal meaning. Sarcasm detection techniques leverage linguistic cues, such as lexical ambiguity, incongruity, and sentiment reversals, to identify sarcastic utterances. By effectively handling context and sarcasm, sentiment analysis systems can provide more accurate and contextually relevant sentiment predictions, leading to better decision-making and insight extraction.

Sentiment Analysis Tools

When it comes to sentiment analysis, many tools and libraries are available to streamline the development process and empower analysts and developers to build robust sentiment analysis systems. Let's explore some of the key tools, libraries, and considerations for sentiment analysis.

Popular Sentiment Analysis Libraries

Several libraries and frameworks have gained popularity for their effectiveness and ease of use in sentiment analysis tasks:

• NLTK (Natural Language Toolkit) : NLTK is a comprehensive library for natural language processing tasks, including sentiment analysis. It provides various tools and resources for text processing, such as tokenization, stemming, and part-of-speech tagging.
• SpaCy : SpaCy is a fast and efficient natural language processing library known for its performance and ease of use. It offers pre-trained models for tasks like part-of-speech tagging, named entity recognition, and dependency parsing, making it suitable for sentiment analysis tasks.
• TensorFlow : TensorFlow is an open-source machine learning framework developed by Google for building and training deep learning models. It offers high-level APIs like Keras for building neural networks, making it suitable for sentiment analysis tasks involving deep learning architectures.
• PyTorch : PyTorch is another popular deep learning framework known for its flexibility and dynamic computation graph. It provides a Pythonic interface for building and training neural networks, making it suitable for sentiment analysis tasks requiring flexibility and customization.

Sentiment Analysis APIs

For developers looking to integrate sentiment analysis into their applications quickly, sentiment analysis APIs offer a convenient solution. These APIs provide pre-trained sentiment analysis models accessible via simple HTTP requests, allowing developers to analyze text data with minimal setup. Some popular sentiment analysis APIs include:

• Google Cloud Natural Language API : offers sentiment analysis capabilities, along with other natural language processing features such as entity recognition and syntax analysis.
• Microsoft Azure Text Analytics API : provides sentiment analysis, key phrase extraction, and language detection capabilities, enabling developers to extract insights from text data effortlessly.
• IBM Watson Natural Language Understanding : offers sentiment analysis, emotion detection, and entity extraction capabilities, empowering developers to analyze text data comprehensively.

Custom Implementation Considerations

While pre-built libraries and APIs offer convenience, custom implementation of sentiment analysis models provides flexibility and control over the entire pipeline. Here are some key factors to consider:

• Data Availability : Ensure you have access to sufficient labeled data for training your custom sentiment analysis model. High-quality labeled data is crucial for building accurate and robust models.
• Model Selection : Choose the appropriate machine learning or deep learning model based on your data characteristics, task requirements, and computational resources. Experiment with different architectures and hyperparameters to find the optimal model for your sentiment analysis task.
• Feature Engineering : Explore various feature extraction techniques to represent text data effectively for sentiment analysis. Consider using word embeddings, TF-IDF vectors, or domain-specific features to capture meaningful information from text.
• Model Evaluation : Evaluate your custom sentiment analysis model using appropriate evaluation metrics, such as accuracy, precision, recall, and F1 score. Validate the model's performance on unseen data to ensure its generalization ability.

By leveraging these tools, libraries, and considerations, you can develop robust sentiment analysis systems tailored to your specific needs and requirements, whether through pre-built solutions or custom implementations.

Sentiment Analysis Best Practices

Effective sentiment analysis requires careful consideration of various factors, from data preprocessing to model selection and evaluation. Here are some best practices and tips to enhance the accuracy and reliability of your sentiment analysis systems:

• Define Clear Objectives : Clearly define the objectives and scope of your sentiment analysis project. Determine the specific sentiment categories of interest (e.g., positive, negative, neutral) and the target audience or domain.
• Preprocess Text Data : Invest time in preprocessing your text data to clean and standardize it. Apply techniques like tokenization, normalization, and stopword removal to prepare the data for analysis. Consider language-specific preprocessing steps based on the characteristics of your text.
• Collect Diverse Data: Ensure that your dataset includes diverse samples representing different demographics , geographic regions, and user segments , and leverage data collection tools like Appinio to simplify and automate the process. This helps to capture a comprehensive range of sentiments and avoids biases in the analysis .
• Leverage Domain Knowledge : Gain domain knowledge relevant to your sentiment analysis task. Understand the context in which sentiment is expressed, including industry-specific terminology, slang, and cultural nuances. Domain knowledge can help improve the accuracy and relevance of sentiment analysis results.
• Explore Feature Engineering : Experiment with different feature engineering techniques to represent text data effectively for sentiment analysis. Consider using word embeddings, TF-IDF vectors, or domain-specific features to capture meaningful information from text and improve model performance.
• Select Appropriate Models : Choose the appropriate machine learning or deep learning models based on your data characteristics, task requirements, and computational resources. Consider factors such as model complexity, interpretability, and scalability when selecting models for sentiment analysis.
• Evaluate Model Performance : Evaluate the performance of your sentiment analysis models using appropriate evaluation metrics, such as accuracy, precision, recall, and F1 score. Validate the models on diverse datasets and consider conducting cross-validation to assess robustness.
• Address Bias and Fairness : Be mindful of bias and fairness considerations in sentiment analysis. Evaluate models for bias across different demographic groups and take steps to mitigate bias in training data and model predictions. Consider incorporating fairness-aware techniques into your sentiment analysis pipeline.
• Monitor Model Performance : Continuously monitor the performance of your sentiment analysis models in real-world applications. Track changes in model performance over time and collect feedback from end-users to identify areas for improvement. Update models regularly to adapt to evolving language patterns and user preferences.
• Document and Share Insights : Document your sentiment analysis workflow, including data preprocessing steps, model selection criteria, and evaluation results. Share insights and findings with stakeholders to foster collaboration and informed decision-making. Consider creating visualizations or dashboards to communicate sentiment analysis results effectively.

Following these best practices and tips, you can develop robust and reliable sentiment analysis systems that provide valuable insights into the attitudes, emotions, and opinions expressed in text data.

Conclusion for Sentiment Analysis

Sentiment analysis offers a powerful tool for understanding and interpreting human emotions and opinions expressed in text data. By leveraging advanced algorithms and techniques, businesses, researchers, and individuals can gain valuable insights into customer preferences, brand perception, market trends, and more. From improving product offerings to enhancing customer satisfaction and informing strategic decision-making, sentiment analysis has the potential to drive positive outcomes across various domains. As technology continues to evolve, sentiment analysis will remain a vital component of the data analytics toolkit, empowering organizations to stay competitive, responsive, and attuned to the needs and sentiments of their stakeholders. However, it's essential to acknowledge the challenges and limitations inherent in sentiment analysis, such as the ambiguity of language, cultural differences, and the subjective nature of sentiment interpretation. Despite these challenges, continuous advancements in machine learning, natural language processing, and data analytics hold promise for overcoming these obstacles and improving the accuracy and reliability of sentiment analysis systems.

How to Conduct Sentiment Analysis in Minutes?

Introducing Appinio , the real-time market research platform revolutionizing sentiment analysis. With Appinio, companies can effortlessly collect real-time consumer insights to fuel their data-driven decisions. Say goodbye to tedious research processes and hello to instant insights that drive business success.

Here's why Appinio is the ultimate tool for conducting sentiment analysis:

• Get from questions to insights in minutes:  With our intuitive platform, you can design and deploy surveys in no time, allowing you to gather valuable sentiment data at lightning speed.
• No research degree required:  Appinio's user-friendly interface makes it easy for anyone to conduct market research, regardless of their background. You don't need a PhD in research to navigate our platform and extract meaningful insights.
• Reach your target audience quickly and accurately:  With access to over 1,200 characteristics and the ability to survey respondents in over 90 countries, you can precisely define your target group and gather sentiment data from the right audience.

Get free access to the platform!

Join the loop 💌

Be the first to hear about new updates, product news, and data insights. We'll send it all straight to your inbox.

Get the latest market research news straight to your inbox! 💌

Wait, there's more

25.04.2024 | 37min read

Targeted Advertising: Definition, Benefits, Examples

17.04.2024 | 25min read

Quota Sampling: Definition, Types, Methods, Examples

15.04.2024 | 34min read

What is Market Share? Definition, Formula, Examples

A Complete Guide to Sentiment Analysis

“That movie was a colossal disaster… I absolutely hated it! Waste of time and money #skipit”

“Have you seen the new season of XYZ? It is so good!”

“You should really check out this new app, it’s awesome! And it makes your life so convenient.”

By reading these comments, can you figure out what the emotions behind them are?

They may seem obvious to you because we, as humans, are capable of discerning the complex emotional sentiments behind the text.

Not only have we been educated to understand the meanings, intentions, and grammar behind each of these particular sentences, but we’ve also personally felt many of these emotions before and, from our own experiences, can conjure up the deeper meaning behind these words.

Moreover, we’re also extremely familiar with the real-world objects that the text is referring to.

This doesn’t apply to machines, but they do have other ways of determining positive and negative sentiments! How do they do this, exactly? By using sentiment analysis. In this article, we will discuss how a computer can decipher emotions by using sentiment analysis methods, and what the implications of this can be. If you want to skip ahead to a certain section, simply use the clickable menu:

• What is sentiment analysis?
• How does sentiment analysis work?
• Sentiment analysis use cases
• Machine learning and sentiment analysis
• Advantages of sentiment analysis
• Disadvantages of sentiment analysis
• Key takeaways and next steps

1. What is sentiment analysis?

With computers getting smarter and smarter, surely they’re able to decipher and discern between the wide range of different human emotions, right?

Wrong—while they are intelligent machines, computers can neither see nor feel any emotions, with the only input they receive being in the form of zeros and ones—or what’s more commonly known as binary code.

However, on the other hand, computers excel at the one thing that humans struggle with: processing large amounts of data quickly and effectively. So, theoretically, if we could teach machines how to identify the sentiments behind the plain text, we could analyze and evaluate the emotional response to a certain product by analyzing hundreds of thousands of reviews or tweets.

This would, in turn, provide companies with invaluable feedback and help them tailor their next product to better suit the market’s needs. So, what kind of process is this? Sentiment analysis!

Sentiment analysis, also known as opinion mining , is the process of determining the emotions behind a piece of text. Sentiment analysis aims to categorize the given text as positive, negative, or neutral.

Furthermore, it then identifies and quantifies subjective information about those texts with the help of:

• natural language processing (NLP)
• text analysis
• computational linguistics
• machine learning

2. How does sentiment analysis work?

There are two main methods for sentiment analysis: machine learning and lexicon-based.

The machine learning method leverages human-labeled data to train the text classifier, making it a supervised learning method.

The lexicon-based approach breaks down a sentence into words and scores each word’s semantic orientation based on a dictionary. It then adds up the various scores to arrive at a conclusion.

In this example, we will look at how sentiment analysis works using a simple lexicon-based approach. We’ll take the following comment as our test data:

Step 1: Cleaning

The initial step is to remove special characters and numbers from the text. In our example, we’ll remove the exclamation marks and commas from the comment above.

That movie was a colossal disaster I absolutely hated it Waste of time and money skipit

Step 2: Tokenization

Tokenization is the process of breaking down a text into smaller chunks called tokens, which are either individual words or short sentences.

Breaking down a paragraph into sentences is known as sentence tokenization , and breaking down a sentence into words is known as word tokenization .

[ ‘That’, ‘movie’, ‘was’, ‘a’, ‘colossal’, ‘disaster’, ‘I’, ‘absolutely’, ‘hated’, ‘it’,  ‘Waste’, ‘of’, ‘time’, ‘and’, ‘money’, ‘skipit’ ]

Step 3: Part-of-speech (POS) tagging

Part-of-speech tagging is the process of tagging each word with its grammatical group, categorizing it as either a noun, pronoun, adjective, or adverb—depending on its context.

This transforms each token into a tuple of the form (word, tag). POS tagging is used to preserve the context of a word.

[ (‘That’, ‘DT’), 

  (‘movie’, ‘NN’), 

  (‘was’, ‘VBD’),  

  (‘a’, ‘DT’) 

  (‘colossal’, ‘JJ’), 

  (‘disaster’, ‘NN’),  

  (‘I’, ‘PRP’), 

  (‘absolutely’, ‘RB’), 

  (‘hated’, ‘VBD’), 

  (‘it’, ‘PRP’),  

  (‘Waste’, ‘NN’) , 

  (‘of’, ‘IN’), 

  (‘time’, ‘NN’), 

  (‘and’, ‘CC’),

  (‘money’, ‘NN’),  

  (‘skipit’, ‘NN’) ]

Step 4: Removing stop words

Stop words are words like ‘have,’ ‘but,’ ‘we,’ ‘he,’ ‘into,’ ‘just,’ and so on. These words carry information of little value, andare generally considered noise, so they are removed from the data.

[ ‘movie’, ‘colossal’, ‘disaster’, ‘absolutely’, ‘hated’, Waste’, ‘time’, ‘money’, ‘skipit’ ]

Step 5: Stemming

Stemming is a process of linguistic normalization which removes the suffix of each of these words and reduces them to their base word. For example, loved is reduced to love, wasted is reduced to waste. Here, hated is reduced to hate.

[ ‘movie’, ‘colossal’, ‘disaster’, ‘absolutely’, ‘hate’, ‘Waste’, ‘time’, ‘money’, ‘skipit’ ]

Step 6: Final Analysis

In a lexicon-based approach, the remaining words are compared against the sentiment libraries, and the scores obtained for each token are added or averaged.

Sentiment libraries are a list of predefined words and phrases which are manually scored by humans. For example, ‘worst’ is scored -3, and ‘amazing’ is scored +3. 

With a basic dictionary, our example comment will be turned into:

movie= 0, colossal= 0, disaster= -2,  absolutely=0, hate=-2, waste= -1, time= 0, money= 0, skipit= 0

This makes the overall score of the comment -5 , classifying the comment as negative.

3. Sentiment analysis use cases

Sentiment analysis is used to swiftly glean insights from enormous amounts of text data, with its applications ranging from politics, finance, retail, hospitality, and healthcare. For instance, consider its usefulness in the following scenarios:

• Brand reputation management:  Sentiment analysis allows you to track all the online chatter about your brand and spot potential PR disasters before they become major concerns. 
• Voice of the customer: The “voice of the customer” refers to the feedback and opinions you get from your clients all over the world. You can improve your product and meet your clients’ needs with the help of this feedback and sentiment analysis.
• Voice of the employee:   Employee satisfaction can be measured for your company by analyzing reviews on sites like Glassdoor, allowing you to determine how to improve the work environment you have created.
• Market research: You can analyze and monitor internet reviews of your products and those of your competitors to see how the public differentiates between them, helping you glean indispensable feedback and refine your products and marketing strategies accordingly. Furthermore, sentiment analysis in market research can also anticipate future trends and thus have a first-mover advantage.

Other applications for sentiment analysis could include:

• Customer support
• Social media monitoring
• Voice assistants & chatbots
• Election polls
• Customer experience about a product
• Stock market sentiment and market movement
• Analyzing movie reviews

4. Machine learning and sentiment analysis

Sentiment analysis tasks are typically treated as classification problems in the machine learning approach.

Data analysts use historical textual data—which is manually labeled as positive, negative, or neutral—as the training set. They then complete feature extraction on this labeled dataset, using this initial data to train the model to recognize the relevant patterns. Next, they can accurately predict the sentiment of a fresh piece of text using our trained model.

Naive Bayes, logistic regression, support vector machines, and neural networks are some of the classification algorithms commonly used in sentiment analysis tasks. The high accuracy of prediction is one of the key advantages of the machine learning approach.

5. Advantages of sentiment analysis

Considering large amounts of data on the internet are entirely unstructured, data analysts need a way to evaluate this data.

With regards to sentiment analysis, data analysts want to extract and identify emotions, attitudes, and opinions from our sample sets. Reading and assigning a rating to a large number of reviews, tweets, and comments is not an easy task, but with the help of sentiment analysis, this can be accomplished quickly.

Another unparalleled feature of sentiment analysis is its ability to quickly analyze data such as new product launches or new policy proposals in real time. Thus, sentiment analysis can be a cost-effective and efficient way to gauge and accordingly manage public opinion.

6. Disadvantages of sentiment analysis

Sentiment analysis, as fascinating as it is, is not without its flaws.

Human language is nuanced and often far from straightforward. Machines might struggle to identify the emotions behind an individual piece of text despite their extensive grasp of past data. Some situations where sentiment analysis might fail are:

• Sarcasm, jokes, irony. These things generally don’t follow a fixed set of rules, so they might not be correctly classified by sentiment analytics systems.
• Nuance. Words can have multiple meanings and connotations, which are entirely subject to the context they occur in.
• Multipolarity. When the given text is positive in some parts and negative in others.
• Negation detection. It can be challenging for the machine because the function and the scope of the word ‘not’ in a sentence is not definite; moreover, suffixes and prefixes such as ‘non-,’ ‘dis-,’ ‘-less’ etc. can change the meaning of a text.

7. Key takeaways and next steps

In this article, we examined the science and nuances of sentiment analysis. While sentimental analysis is a method that’s nowhere near perfect, as more data is generated and fed into machines, they will continue to get smarter and improve the accuracy with which they process that data. 

All in all, sentimental analysis has a large use case and is an indispensable tool for companies that hope to leverage the power of data to make optimal decisions.

For those who believe in the power of data science and want to learn more, we recommend taking this free, 5-day introductory course in data analytics . You could also read more about related topics by reading any of the following articles:

• The Best Data Books for Aspiring Data Analysts
• PyTorch vs TensorFlow: What Are They And Which Should You Use?
• These Are the Best Data Bootcamps for Learning Python

Sentiment Analysis: Decoding Emotions for Research

Introduction

What is sentiment analysis, what is an example of sentiment analysis, why is sentiment analysis important, how do you collect sentiments, how do you analyze sentiments, what are the current challenges for sentiment analysis.

Sentiment analysis is the process of determining whether textual data contains a positive sentiment or a negative sentiment. Researchers use sentiment analysis tools to provide additional clarity and context to the messages conveyed in words to deliver more meaningful insights.

In this article, we'll look at the importance of sentiments, how researchers analyze sentiments, and what strategies and tools can help you in your research .

Sentiment analysis is a subset of natural language processing (NLP) that focuses on extracting and understanding the emotional content from data . The primary objective is to classify the polarity of a text as positive, negative, or neutral. This classification is essential for understanding customer sentiment, gauging public opinion, and conducting in-depth research on various topics.

At its core, a sentiment analysis system employs machine learning techniques and algorithms to dissect the language used in text data from many sources, such as:

• written feedback
• news articles
• survey records
• social media posts

One of the most refined forms of this method is aspect-based sentiment analysis. Rather than merely classifying the overall sentiment of a document, this kind of analysis pinpoints specific topics or aspects within the text and evaluates the sentiment towards each. Such sentiment analysis technologies with natural language processing can also be used for opinion mining.

A simple example

Consider a product review that states, "The camera on this phone is excellent, but the battery life is short." A sentiment analysis model would recognize the positive sentiment towards the camera and the negative sentiment towards the battery life, rather than giving a blanket sentiment score.

Sentiment analysis tools are varied, ranging from simple models that identify positive and negative terms to sophisticated sentiment analysis models that rely on machine learning and data scientists for insightful sentiment analysis. Such tools work by assigning a sentiment score to words or phrases, often based on their context. The result? A sentiment analysis solution that deciphers the nuances of human language, turning unstructured data into actionable insights.

Ultimately, an accurate sentiment analysis bridges the gap between the vast world of text-based data and the need to understand the underlying emotions and opinions it contains. Whether you're a researcher looking to perform sentiment analysis on news articles or a business keen on understanding customer feedback, sentiment analysis is a pivotal tool in today's data-driven world.

For deeper insights, turn to ATLAS.ti

Make the most of your data with the most comprehensive qualitative data analysis available. Download a free trial today.

Sentiment analysis offers tangible examples of its applications across diverse fields. From businesses striving to enhance their products to researchers aiming to grasp public sentiment on various issues, the power of sentiment analysis is evident.

By examining specific sectors, we can better understand the profound impact this analysis has on our decision-making processes and the vast potential it holds in shaping perceptions.

Market research

Conducting market research often consists of analyzing sentiment to gauge public reactions to a product or service. Using sentiment analysis tools, companies can sift through survey responses and online reviews, identifying patterns that might not be immediately apparent.

For example, if a new beverage receives predominantly positive reviews for its taste but negative comments about its packaging, this analytical approach can highlight these specific sentiments, guiding the company in refining its offering.

Customer feedback

Customer feedback is a goldmine of sentiment analysis datasets for businesses aiming to improve their services. By implementing a sentiment analysis system, companies can categorize feedback as positive, negative, or neutral, making it easier to prioritize areas for improvement.

Suppose a hotel chain discovers that a significant number of negative words in customer reviews pertain to room cleanliness. In that case, they can take immediate measures to address this concern, enhancing the overall guest experience.

Social media platforms

Social media is awash with opinions and feedback. By employing models for the analysis of sentiments, businesses and researchers can tap into real-time feelings of the masses.

For instance, if a celebrity endorses a brand and sentiment analysis reflects a surge in positive words associated with that brand, it can be concluded that the endorsement had a favorable impact. Conversely, if a political figure makes a statement and the analysis indicates a spike in negative words related to the topic, it provides insights into public opinion.

Sentiment analysis has rapidly become a crucial tool in today's digital age, helping businesses, researchers, and individuals decode the emotions hidden within vast amounts of data. But why has it garnered such significance?

The reasons are manifold, but they all converge on the idea that understanding sentiment offers a deeper, more nuanced view of human reactions and opinions.

Sentiment analysis use cases & applications

The applications of sentiment analysis are diverse and expansive. For instance, in the realm of politics, sentiment analysis can be used to gauge public opinion on policies or candidates, offering insights that can guide campaign strategies.

In the healthcare sector, sentiment analysis can capture patient feedback, allowing providers to fine-tune their services and improve patient experiences.

Moreover, educators can use sentiment analysis to understand student feedback, making curriculum adjustments that align with student needs and preferences.

Benefits of sentiment analysis

Beyond its various applications, the benefits of sentiment analysis are profound. Firstly, it offers an efficient way to process large volumes of unstructured data , turning it into actionable insights. Businesses, for example, can use sentiment analysis to get ahead of potential public relations crises by identifying negative sentiments early.

Furthermore, it provides rule-based systems that can circumvent the time-consuming task of manually reviewing each piece of feedback. This not only saves time but also reduces the risk of human bias.

Most significantly, by understanding both positive and negative phrases and their context, organizations can better align their strategies and offerings with their audience's true feelings and needs.

Collecting sentiments involves gathering data from various sources to be analyzed for emotional content. This task, while seemingly straightforward, requires a systematic approach to ensure that the data obtained is both relevant and of high quality.

One of the primary sources for sentiment collection is social media platforms. Platforms like Twitter, Facebook, and Instagram are brimming with user-generated content that reflects public opinion on a vast array of topics. By utilizing specialized web scraping tools or APIs provided by these platforms, one can amass large datasets of posts, comments, and reviews to analyze.

Customer reviews on e-commerce websites, such as Amazon or Yelp, are another treasure trove of sentiments. These reviews often provide detailed insights into customer sentiment about products, services, and overall brand perception. Similarly, survey responses, when designed with open-ended questions, can provide valuable data that captures the sentiments of the respondents.

In the news and media sector, news articles and op-eds are rich sources of sentiment. Collecting sentiments from these sources can help gauge public sentiment on current events, governmental decisions, or societal issues.

Forums and online communities, like Reddit or specialized industry forums, offer another avenue. Here, users often engage in in-depth discussions, providing nuanced views that are ripe for sentiment analysis.

However, while collecting sentiments, it's essential to consider privacy and ethical guidelines. Ensuring that data is anonymized and devoid of personally identifiable information is crucial. Moreover, always be aware of terms of service when extracting data from online platforms, as some might have restrictions on data scraping.

Analyzing sentiments is a multifaceted process that goes beyond merely identifying positive or negative words. It examines the context, nuances, and the intricate elements of human language. With advancements in machine learning and data science, this analysis has become more refined and precise.

Sentiment scores

At the foundation of this analytical approach lies the sentiment score. This score is usually a numerical value assigned to a piece of text, indicating its overall sentiment. For instance, a system to analyze sentiment might assign values on a scale from -1 (negative) to 1 (positive), with 0 representing a neutral sentiment. Sentiment scores provide a quick overview, enabling researchers and businesses to categorize large datasets swiftly.

Sentiment analysis algorithms

A machine learning algorithm, natural language toolkit, or artificial neural networks can power sentiment analysis work. These range from simple rule-based algorithms, which identify sentiments based on predefined lists of positive and negative words, to more complex machine learning techniques. Machine learning-based sentiment analysis models, especially those utilizing deep learning, consider the broader context in which words are used, leading to more advanced sentiment analysis.

Sentiment analysis tools

There's a plethora of tools available, each tailored for different requirements. Some tools are designed for specific industries, while others are more general-purpose. Many of these tools leverage advanced models, making it easier for users without a deep technical background to extract meaningful insights from textual data. The qualitative data analysis software ATLAS.ti, for example, includes a sentiment analysis tool to automatically code data .

Sentiment analysis, despite its transformative potential and growing adoption, is not without its share of challenges. The intricacies of language and emotion often pose complexities that even the most advanced systems can find challenging to navigate.

Sarcasm and irony : One of the most significant challenges is detecting sarcasm and irony. A statement like "Oh, great! Another flat tire!" may be classified as positive by rudimentary analysis models because of the word "great." However, the context clearly indicates a negative sentiment.

Cultural nuances : Cultural and regional variations in language can affect sentiment interpretation. A word or phrase that's considered positive in one culture might be neutral or even negative in another. Without a culturally-aware model, these nuances can easily be missed.

Short and ambiguous texts : Platforms like Twitter, with their character limitations, often contain short and sometimes ambiguous messages. Without ample context, determining the sentiment of such messages can be tricky.

Polysemy : Words with multiple meanings, based on context, can pose challenges. For instance, the word "light" can be positive when referring to a "light meal" but negative when talking about "light rain" during a planned outdoor event.

Emotionally complex statements : Some statements might contain mixed emotions, making them hard to classify. For example, "I love how this camera captures colors, but its weight is a bit much for me." This statement contains both positive and negative sentiments about the same product.

Evolution of language : Language is dynamic. New words, slang, and expressions constantly emerge, especially on digital platforms. Keeping sentiment analysis tools updated to recognize and correctly interpret these new terms is a continual challenge.

Addressing these challenges requires a combination of improved algorithms, larger and more diverse training datasets, and a deeper understanding of linguistics and cultural contexts. As technology advances and sentiment analysis solutions become more sophisticated, the hope is that these challenges will diminish, leading to even more accurate and insightful outcomes.

Make ATLAS.ti your own sentiment analysis solution

Powerful auto-coding tools for sentiment analysis and opinion mining are at your fingertips, starting with a free trial.

How to Use Sentiment Analysis in Marketing

Sentiment analysis can do wonders for any marketer. By understanding what your target audience is thinking on a scale that only sentiment analysis can achieve, you can tweak a product, campaign, and more, to meet their needs and let your customers know you’re listening

Sentiment analysis is an artificial intelligence technique that uses machine learning and natural language processing (NLP) to analyze text for polarity of opinion (positive to negative). It’s one of the hardest tasks of natural language processing but, with the right tools, you can gain in-depth insights from social media conversations, online reviews, emails, customer service tickets, and more.

Here’s an example of how a pre-trained sentiment analyzer might sort online mentions about a particular marketing campaign:

The model easily categorizes this comment as ‘Positive’ with near 100% accuracy. But, with powerful machine learning algorithms and models custom-trained to your specific needs and criteria, sentiment analysis can go far beyond simply positive, negative, and neutral, to read for context, misspelled and misused words, slang, even sarcasm.

Sentiment analysis has become an essential tool for marketing campaigns because you’re able to automatically analyze data on a scale far beyond what manual human analysis could do, with unsurpassed accuracy, and in real time. It allows you to get into the minds of your customers and the public at large to make data-driven decisions. 

You can even analyze customer sentiment of your company and compare it against your competition, or follow market trends and emerging topics. Check out your brand perception in new potential markets. The public offers millions of opinions about brands and products on a daily basis, on social media and beyond. 

Traditional metrics, like views, clicks, comments, and shares just aren’t enough anymore – they don’t tell the whole story. Some of those reactions could actually be negative . 

You need to know exactly what they are saying, then you can figure out why . And machine learning allows you to perform it automatically and on a regular basis, with almost no human interaction needed. With constant, real-time sentiment analysis , you’ll always be prepared to make quick decisions and pivot when necessary.

Sentiment Analysis Marketing Applications

Below are some of the top applications to help increase customer acquisition, improve customer service, and keep your clientele happy:

Social media monitoring

Analyze marketing campaign success, gauge consumer sentiment around a new product launch, keep an eye on your competition, prevent pr crises, market research.

• Identify influencers

Social media monitoring, or social listening , can often uncover the truest customer opinions because users feel freest to react quickly and emotionally to what they see and hear. They are simply compelled to tell the world how they feel.  

Furthermore, it’s estimated that 83% of users who make a comment or complaint on social media expect a response the same day and 18% want it immediately . Imagine all the customers you could lose if you’re not paying attention to social media mentions, minute-to-minute.

Oftentimes, the best course of action can be to simply show your customers that you’re listening. Or use a positive comment to your benefit, for the world to see:

Sentiment analysis of social data will keep an eye on customer opinion, 24/7. Use it to target marketing campaigns directly and follow the response, or use it as a marketing device, itself, to find out what language and what social interactions receive the most positive feedback.

Follow your marketing campaigns right as they launch, in real time, on social media or in news articles, forums, or targeted surveys. Track the sentiment of your customers to find out what’s resonating and what’s not, on a macro level or down to individual word usage. 

Watch your campaigns as they rise or fall, and find out why it may be happening. If your campaigns are working better within certain demographics or geographic regions, you can tweak or automate your marketing to target them more thoroughly. And you can compare a current campaign against historical data to make sure you’re always improving your messaging.

Similarly, you can track sentiment of new product launches, from wherever the comments and reviews may appear. 

Use aspect-based sentiment analysis to organize individual ideas or “opinion units” about your product by “aspect” or category, like Reliability, Usability, Pricing , then perform sentiment analysis. This will give you a sentiment rating of the major aspects of your new product, so you can see how customers feel and where you might need to make changes.

Sentiment analysis also allows you to get proactive with your message to reach out to customers directly when they may have an issue.

Monitor more than just your own brand and products and keep an eye on your competition. Find out where their marketing campaigns may be succeeding with customers and what keywords and tactics work best. Are they marketing to particular groups or on certain media that you may not have thought of?

Furthermore, when you see that their campaigns are scoring negatively, you can take advantage of the situation to shine a light on your brand. Or, if a certain aspect of a competitor’s new product receives a large number of negative reactions, you can use it as an opportunity to improve on it with your own release.

Tweets and Facebook comments can travel around the world in just a few minutes. This is when it’s particularly useful to monitor your brand and marketing efforts on social media in real time. If negative sentiment suddenly spikes, you can tackle the problem right away, before it grows into a serious issue. Filter your social listening by sentiment and aspect to always be at the ready.  

Find new markets and new demographics where your brand is likely to succeed. Analyze successful campaigns and brands to discover what messaging works best. Target market surveys can be particularly helpful in this situation, and sentiment analysis allows you to analyze open-ended surveys – to dig into quantitative data and find out the emotions and opinions of respondents. You can analyze thousands of text-heavy surveys and reviews in just minutes.

Identify Influencers

Although sometimes seemingly overused, social media influencers are here to stay in the years to come. And they can provide real results – oftentimes micro-influencers have even more sway over their followers, and they cost quite a bit less.

With the help of sentiment analysis, it can be easy to locate the most appropriate influencers for your product. Simply identify keywords that are important to your business. If you’re a vegan-friendly snack food company, for example, you could search social media for keywords, like “vegan,” “plant-based,” etc. 

Once you’ve located users with the appropriate number of followers that use your keywords frequently (in a positive light, of course), use sentiment analysis again to make sure their interactions with followers are also positive, and you’ve found your candidates.

Sentiment Analysis Marketing Tools

There are several useful and dynamic sentiment analysis tools out there that can make sentiment marketing easy and cost-effective.

MonkeyLearn

Social searcher.

Best for: Companies that want an all-in-one interface, with easily customizable tools and simple integrations.

MonkeyLearn is a powerful SaaS platform that allows you to train your own sentiment analyzers with ease, and offers many other user-friendly, customizable text analysis tools, like the keyword extractor , survey feedback classifier , and intent and email classifier .

MonkeyLearn has simple integrations with other applications you already use, like Excel, Google Sheets, Zendesk, Zapier, and more. You can even upload Twitter data directly in the app or use low-code APIs to connect all other social media platforms (like the Graph API for pulling data from Facebook).

Best of all, with MonkeyLearn Studio , you can perform all your analyses and see them immediately visualized in striking charts and graphs in one simple interface. 

MonkeyLearn pricing 

Best for: Companies that want access to huge data backlogs.

Brandwatch’s Consumer Research platform boasts “the industry’s largest archive of consumer opinions” offering data from 100 million sources and 1.3 trillion individual posts. They focus on social listening and social influencer strategies to get a thorough picture of sentiment marketing and brand strategy.

Brandwatch Pricing

Best for: Companies that want a marketing and PR partner.

Meltwater offers social listening and social media solutions with guided management for clients that don’t want to oversee analysis themselves. Meltwater began as a web scouring company in 2001, an early entrant into the online media monitoring landscape. 

While they have extended their services into brand management, PR, and crisis communications, they are still one of the best in the business with huge news and social media databases that can help guide marketing campaigns with the aid of history. 

Meltwater Pricing

Best for: Companies that want to take social listening and brand monitoring out for a test drive.

Social Searcher is a free social media search engine that offers real-time access to follow brands and products across all major social media. It’s perfect if you’re just learning about social media monitoring, because it’s easy to use and offers a number of more advanced options, like sentiment analysis, and beyond. 

It doesn’t integrate as easily with other applications, however, so the analysis can be a little clunky if you want to get highly advanced.

Social Searcher Pricing

Best for: Companies with some coding ability to access APIs and train advanced analysis models.

Repustate’s main focus is on training sentiment analysis models to industry-specific language for advanced data mining, and their algorithms use a number of different techniques for comprehensive results.

They offer multilingual analysis, with access to news outlets and social media from around the globe, which is particularly helpful if you do business in multiple countries or are looking to expand into new markets.

Repustate Pricing

Best for: Companies that want to track real-time conversations happening across multiple social media platforms.

Hootsuite integrates easily with all major social networks: Twitter, Instagram, Facebook, LinkedIn, WordPress, Foursquare, and Google+, and more. Set up unlimited social streams and assign actions and responses simply to coworkers for immediate execution.

Filter marketing and brand mentions by location, language, tone, gender, and more. And set up immediate notifications when influencers make a negative comment directed at your brand or product.

Hootsuite Pricing

Whether you want to use sentiment analysis to gauge the success of your marketing campaigns, as a targeted marketing tool, or to pit your brand against your competition, there’s no doubt that it has become a necessary tool for any marketing strategy.

Try out MonkeyLearn’s pre-trained sentiment analyzer now to see how it works. Once you’ve integrated sentiment analysis into your marketing efforts, you’ll be amazed at how easy it is to use and how quickly you’ll get real results.

Request a demo to find out more.

Rachel Wolff

September 21st, 2020

Posts you might like...

Learn How To Use Sentiment Analysis Tools in Zendesk

Customer service help desks take a lot of heat. The sheer volume of customer queries that flood in is overwhelming.  Although many of these…

Analyze Sentiment in Real-Time with AI

There are constant discussions happening about your brand online, 24/7. On social media, in forums, news articles, online reviews, and more…

What Is Opinion Mining & Why Is It Essential?

One of the key aspects of any successful business is knowing how your customers feel about your brand and your products. They often freely…

Text Analysis with Machine Learning

Turn tweets, emails, documents, webpages and more into actionable data. Automate business processes and save hours of manual data processing.

What is sentiment analysis in marketing?

Last updated

5 September 2023

Reviewed by

Miroslav Damyanov

Sentiment analysis is as useful for digital marketers as it is for stock market investors and other finance-oriented professionals. The growth and popularity of machine-learning algorithms have made this form of analysis more accurate than ever, and it’s available to anyone with any budget.

This article discusses what sentiment analysis marketing is, why it’s useful, ways to use it, and the various tools it applies.

• What is sentiment analysis?

When relying on sentiment analysis, you can categorize individual interactions as negative, positive, or neutral. When you determine and track these categories, you can use this data for different marketing purposes, including your online strategy.

Sentiment analysis is incredibly useful because more interactions do not always equate to better results. For instance, a social media post that receives ten positive replies is better than a post that receives 100 replies, ten of which are positive.

Ultimately, the main goal of sentiment analysis is to offer constructive feedback.

• Why use sentiment analysis in marketing?

Sentiment analysis is essential because it offers insightful feedback on how customers perceive your products or services. Here are some of its tangible benefits:

Understanding your audience and defining your niche

With sentiment analysis, you can get a more granular perspective when examining your audience. This can help you quickly identify a market niche that suits your company’s products and services.

When you understand what your brand means to your current customers, you can swiftly increase its market share. For instance, a struggling ice cream parlor can learn about the types of flavors that people like or dislike. The business can then improve profits by heavily marketing popular flavors and lowering operating costs by discounting those that are less popular.

Managing PR issues and improving customer support

Many businesses rely on social media channels to offer customer support since these platforms are more personalized and immediate. Customer support staff can respond quickly to negative comments and help de-escalate situations before they grow and become less manageable.

For instance, if someone tags your brand in a post discussing their frustration about a defective item, you can offer a public apology. Then, you can follow up with them privately to further show your commitment to quality.

Handling negative sentiments effectively in public also shows other customers that your brand has great service policies.

Adjusting messaging and product development

When you know the aspects of your product or service your customers value most, you know what to emphasize in your adverts.

For instance, if one of your products suddenly experiences an unexplained spike in sales, you can examine your positive replies to get insights.

Identifying influencers

A sentiment analysis system can help you identify micro-influencers. These are social media personalities with a relatively small number of followers that are very active.

These micro-influencers are typically the most beneficial to your brand since they interact with their followers directly. You could find that one of your customers closely follows a podcast with about 20,000 subscribers and an audience that intersects with your own. You could then contact the podcaster and ask if they want to be sponsored.

Monitoring competitors

Both positive and negative comments can reveal valuable information about your company’s competitors. Evaluating how customers perceive your services and products compared to others can help you develop promotional materials that highlight your brand’s advantages.

For example, customers could post on social media that they love the discount codes and coupons a competing brand offers. This information could encourage marketers to put out adverts that emphasize more competitive sales offers.

Researching your target market

When planning a new marketing campaign around a controversial topic, it’s important to assess the emotions around it and ensure you want to be included in the discussion.

Sentiment analysis can also help inform your decisions about releasing a new product or entering a fresh market.

Supporting employer branding

Opinions about your business greatly influence your recruitment costs and effectiveness. By analyzing the emotions related to your brand from your applicants or on opinion forums, you can know how well your online presence complements your recruitment campaigns. For instance, you might discover that potential applicants are discouraged by what they read about your brand online.

• Different types of sentiment analysis

Sentiment analysis doesn’t always work in the same way. Several different approaches use distinct algorithms and mechanisms, depending on the desired outcome and context.

The major subtypes of sentiment analysis are:

Fine-grained sentiment analysis : when measuring polarity precision, you may decide to use clearer polarity categories. For instance, you might have “highly negative,” “slightly neutral,” or “somewhat positive” categories.

Emotion detection : this form of sentiment analysis helps detect emotions such as sadness, anger, frustration, and happiness. Most emotion-detection systems rely on complex machine-learning algorithms or lexicons.

Aspect-based sentiment analysis : this form of sentiment analysis is helpful when you want to examine sentiments of texts (such as product reviews) to know the various features or aspects of your product that people are mentioning in a negative, neutral, or positive way.

Multilingual sentiment analysis : this is a highly complex form of sentiment analysis, as it involves a great deal of pre-processing and resources. You can access most of these resources, like lexicons, online. Others have to be created, such as noise-detection algorithms or translated corpora.

• How sentiment analysis marketing works

Sentiment analysis marketing relies on natural language processing (NLP), statistics, and machine learning to assess how people think and feel on a larger scale.

These tools process written content to assess its negativity or positivity. They mine content from various sources, including social media platforms, blog posts, chatbot conversations, third-party websites, emails, and support tickets.

• Which sources should you analyze?

Sentiment analysis is all about evaluating customer feedback via your product’s or brand’s perception in the media. The three major source types you should focus on are:

Social media

Most people turn to social media to express their feedback and talk about their experience with a particular brand or product. Therefore, these platforms are some of the best sources of discussion to assess.

Conversations with your customers 

Wherever you engage with customers is a great source of sentiment analysis. For instance, you might interact with customers using a customer relationship management (CRM) system, calls, emails, or even Facebook Messenger. Archive all these engagements and examine their sentiment to get a full view of your audience’s emotions.

Other media

While getting textual data from other media (such as newspapers, TV, blogs, and online forums) could be more difficult, the quality of data you obtain could be higher.

• What are the challenges of sentiment analysis in marketing?

The major challenges of sentiment analysis include the following:

Determining the tone of comments can be difficult, particularly when neutral content doesn’t offer much information. The algorithm might categorize a comment as either positive or negative or just leave it out altogether. For example, when someone comments that “the hotel is situated in a busy area,” it can be difficult to understand whether “busy” is negative or positive.

Algorithms could mistake sarcasm for positivity or negativity. For instance, someone could criticize a marketing tactic using a sarcastic comment like, “What a great idea!”

Technology may run into problems when tackling idioms, emojis, or even multiple languages. For example, when customers use slang or figurative language in their comments about your brand’s newest product, the technology might not be able to decipher the sentiments.

What are sentiments in sentiment analysis?

In sentiment analysis, sentiments refer to the feelings, emotions, attitudes, or opinions expressed in a certain comment. The sentiment could be negative, positive, or neutral.

What is an example of market sentiment?

“I absolutely loved the hotel we stayed at during our honeymoon. The rooms were exceptional, and the ambiance was perfect.”

This is a positive sentiment because it contains words like “loved,” “exceptional,” and “perfect.”

Which companies use sentiment analysis?

Numerous companies across various industries use sentiment analysis to gain valuable insights from vast amounts of textual data and improve their decision-making processes. These include companies in the following industries: finance and investment, e-commerce, market research, healthcare and medical, and travel and hospitality.

Get started today

Go from raw data to valuable insights with a flexible research platform

Editor’s picks

Last updated: 22 April 2023

Last updated: 8 May 2023

Last updated: 16 August 2023

Last updated: 18 July 2023

Last updated: 9 July 2023

Last updated: 20 March 2024

Last updated: 21 March 2024

Last updated: 5 April 2023

Last updated: 22 May 2023

Last updated: 29 May 2023

Last updated: 23 March 2024

Last updated: 18 April 2024

Last updated: 25 June 2023

Latest articles

Related topics, log in or sign up.

Get started for free

To read this content please select one of the options below:

Please note you do not have access to teaching notes, online sentiment analysis in marketing research: a review.

Journal of Research in Interactive Marketing

ISSN : 2040-7122

Article publication date: 31 January 2018

Issue publication date: 25 May 2018

The explosion of internet-generated content, coupled with methodologies such as sentiment analysis, present exciting opportunities for marketers to generate market intelligence on consumer attitudes and brand opinions. The purpose of this paper is to review the marketing literature on online sentiment analysis and examines the application of sentiment analysis from three main perspectives: the unit of analysis, sampling design and methods used in sentiment detection and statistical analysis.

Design/methodology/approach

The paper reviews the prior literature on the application of online sentiment analysis published in marketing journals over the period 2008-2016.

The findings highlight the uniqueness of online sentiment analysis in action-oriented marketing research and examine the technical, practical and ethical challenges faced by researchers.

Practical implications

The paper discusses the application of sentiment analysis in marketing research and offers recommendations to address the challenges researchers confront in using this technique.

Originality/value

This study provides academics and practitioners with a comprehensive review of the application of online sentiment analysis within the marketing discipline. The paper focuses attention on the limitations surrounding the utilization of this technique and provides suggestions for mitigating these challenges.

• Online marketing
• Qualitative research
• Quantitative research
• Methodology
• Text mining
• High technology marketing

Rambocas, M. and Pacheco, B.G. (2018), "Online sentiment analysis in marketing research: a review", Journal of Research in Interactive Marketing , Vol. 12 No. 2, pp. 146-163. https://doi.org/10.1108/JRIM-05-2017-0030

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

Related articles

We’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

Sentiment Analysis: Understanding Perception for Better Marketing

Home Blog Media Monitoring Sentiment Analysis: Understanding Perception for Better Marketing

Posted on November 3rd 2023

Radostin Anastasov | 13 min read

In our hyper-connected and data-driven world, understanding the minds and emotions of customers is of paramount importance for businesses.

Given the speed with which online trends come and go as well as the growing number of internet users, marketers need to constantly monitor the media to be able to create strategies that convert users into customers.

One important step when doing media monitoring is to analyze sentiment. Sentiment analysis offers a dynamic lens through which marketers can gain invaluable insights into the thoughts, attitudes, and emotions of their customer base.

Often referred to as opinion mining, sentiment analysis is a computational technique that involves the extraction of subjective information from textual or verbal data .

There are many reasons why marketers should add sentiment analysis to their tools for marketing analytics. By analyzing sentiment, they can:

• enhance consumer understanding and swiftly adapt strategies,
• improve the products and services of their business,
• benchmark against competitors,
• understand individual preferences and create personalized marketing campaigns,
• detect negative coverage in real-time and protect their brand’s reputation by mitigating PR crises, and much, much more.

Table of contents :

Understanding Sentiment Analysis

Importance of sentiment analysis in marketing, utilizing sentiment analysis, sentiment analysis in market research, challenges and limitations of sentiment analysis, benefits of sentiment analysis, the importance of sentiment analysis.

Sentiment analysis goes beyond traditional market research. It offers real-time insights into customer perceptions and feelings, and it operates on the premise that words and phrases carry emotional weight and meaning.

It discerns sentiment, emotion, or opinion expressed by individuals in various forms of communication, such as social media posts, product reviews, customer feedback, and more.

The process begins with text preprocessing, where raw text is cleaned, tokenized, and transformed into a format suitable for analysis. Then it utilizes natural language processing (NLP) algorithms that recognize and interpret linguistic nuances, context, and emotional cues within the text and categorize this data as positive, negative, or neutral.

Sentiment analysis algorithms can be rule-based, automatic, and hybrid. Rule-based systems perform sentiment analysis based on manually set rules.

Automatic systems rely on machine learning techniques that take large amounts of training data with an existing sentiment label, identify patterns, and then apply the sentiment to new unlabeled data based on the training data, they analyzed.

Finally, hybrid systems combine both rule-based and automatic techniques with the aim of improving the accuracy of sentiment prediction.

Usually, machine learning models are employed to train the system to classify sentiments accurately. These models learn from vast datasets, enabling them to recognize patterns and sentiments in real-world text.

The synergy of text processing and machine learning ensures that sentiment analysis can effectively decipher the complex tapestry of human emotions and opinions, making it an indispensable tool for businesses, researchers, and decision-makers alike.

The insights that marketers gain from sentiment analysis can be used to refine marketing strategies, strengthen brand loyalty, and ultimately drive business success in an ever-evolving digital landscape. More often than not, emotions are the driving force behind purchase decisions, holding the key to consumer behavior .

By employing sentiment analysis, marketers can tap into this emotional undercurrent and gain a profound insight into the sentiments that shape buying choices. Furthermore, integrating opinion mining into their customer success strategy empowers marketers to proactively address customer needs and build lasting relationships.

Sentiment analysis also acts as a barometer for gauging customer satisfaction and brand perception. It provides a window into how customers perceive your products or services, helping you identify areas of improvement and fortify brand loyalty. The true power of sentiment analysis, however, lies in its ability to transform raw data into actionable insights.

Marketers who have a robust media monitoring process in place know how fast people’s opinions can change. That is why sentiment analysis should be done as part of a continuous monitoring process where marketers can get regular data updates that will provide insights into how their target audiences’ feelings and behaviors change over time.

By harnessing these insights, marketers can tailor their strategies to resonate with their target audience, create personalized campaigns, and ultimately drive business success in an increasingly competitive market.

It can be applied in various facets of modern marketing. Given that social media is often the first place where people turn to express their opinions, notably their discontent, analyzing sentiment on social media should be a marketer’s priority .

There are a lot of platforms on the market that can help you monitor social media activity such as Mention , Talkwalker, or Brandwatch, and most of them will contain a tool for sentiment analysis.

When analyzing social media content, it is important to remain consistent, which means monitoring mentions continuously. By doing so, marketers can:

• detect emerging trends
• assess the impact of marketing campaigns
• engage with their audience more effectively

Moreover, marketers whose current lead generation strategy is based on telemarketing or telesales, which require TCPA compliance , can significantly increase their number of quality leads if they know which types of narratives generate positive emotions in their target audiences online.

Also, sentiment analysis is a cornerstone in monitoring customer reviews and feedback. It allows businesses to dissect the opinions expressed in product reviews, customer surveys, polls from live or on-demand webinars , and feedback forms, offering a wealth of information on what customers appreciate and where improvements are needed.

This proactive approach to customer satisfaction enables organizations to make data-backed decisions, refine their offerings, and enhance the overall customer experience.

In market research, sentiment analysis is especially useful for:

• extracting consumer insights
• benchmarking against competitors
• identifying trends and patterns

Extracting Consumer Insights

Sentiment analysis dives deep into the vast sea of textual data generated by consumers across various platforms. By meticulously scrutinizing product reviews, social media conversations, and customer feedback, businesses can gain a profound understanding of not just what their consumers are saying, but also how they feel .

This provides a nuanced perspective on consumer preferences, pain points, and evolving expectations. This information can then be used to show customers that the business is listening to them and improving their products or services.

Benchmarking Against Competitors

As previously mentioned, there are a lot of media monitoring platforms available online that can help marketers monitor brand mentions in real time. The good thing about these platforms is that marketers can also set up strings or queries to monitor public mentions of their competitors and gauge the public’s sentiment toward them.

This type of competitor benchmarking is very useful when doing market research because marketers can learn how to attract their competitors’ consumers .

For example, imagine that you are comparing the performance of your brand against four competitors and your sentiment analysis shows you that Brand F received the highest number of positive mentions in your monitored period.

This will show you that you need to dig deeper into what Brand F’s customers are saying about them, so you can learn why it is receiving positive coverage.

This can help you gain insights into the type of features customers prefer in your competitors’ offerings, as well as identify gaps in the market by reading about the types of features customers would like certain products or services to have.

With this information, you can create targeted marketing campaigns that will promote these improvements to your products or services, as well as upgrades or new releases.

Identifying Trends and Patterns

Sentiment analysis is also very useful when looking to identify trends and patterns that might otherwise remain hidden because it can detect subtle shifts in consumer sentiment over time.

In the fashion industry, for example, customer purchasing behaviors have started shifting due to increasing discourse around the negative effects of fast fashion on the environment and labor practices.

Fashion brands can use sentiment analysis to analyze these conversations and learn how they can improve their production processes, change their supply chains, and adapt their brand messages, so customers can see them as more sustainable and environmentally friendly.

By monitoring unfolding narratives and following the changes in sentiment, marketers can spot emerging market opportunities, monitor the impact of marketing campaigns (both their own and those of their competitors), and adjust their strategies proactively.

This level of insight is invaluable for staying ahead of the competition and ensuring that products and services remain relevant and appealing in a dynamic marketplace.

However advanced, sentiment analysis algorithms may misinterpret nuances, sarcasm, irony, or cultural references, potentially leading to inaccuracies in sentiment classification. For example, a user might write “wow…well isn’t that lovely,” in a sarcastic manner, but the algorithm might register it as positive because of the word “lovely”.

Understanding context and capturing the real sentiment behind complex emotions is not easy for machine models, which is why having a human in the loop helps.

Different languages, dialects, and cultural contexts can profoundly affect the perception of sentiments. What may be seen as a positive sentiment in one culture might register as neutral or even negative in another.

Businesses operating in multiple markets must address this concern by investing in localization services to ensure their sentiment analysis tools accurately identify different languages and cultural aspects. This enhances the accuracy of their analyses and demonstrates a commitment to ethical data practices.

Moreover, sentiment analysis models are trained by humans, and although there are efforts to eliminate inherent biases, those efforts are not always fruitful.

Language used in training data or online content may contain stereotypes related to race, ethnicity, or social class. Sentiment analysis models may inadvertently learn and perpetuate these stereotypes, leading to biased results.

Understanding these limitations is paramount, as overreliance on sentiment analysis results can yield misleading conclusions. By acknowledging these ethical considerations, marketers can harness its potential while upholding cultural sensitivity and the diversity of human expression in our interconnected global landscape.

Let us summarize the five main benefits of sentiment analysis:

1. Informing Market Research

By analyzing vast amounts of textual data from sources like social media, product reviews, and surveys, sentiment analysis can help businesses refine their market research strategies, gain a competitive edge, and stay attuned to evolving consumer demands.

They can understand what customers like or dislike about their products or services, and use this information to improve their market offerings.

2. Enhancing Customer Service

By monitoring sentiment in real-time, companies can swiftly respond to customer feedback , improve service quality, and enhance overall customer satisfaction. Customers nowadays expect brands to be very responsive and instantly solve their grievances.

Marketers can fine-tune their approaches and tailor their messaging to resonate with their audience, ensuring their products and services remain relevant.

3. Managing Brand Reputation

Keeping a vigilant eye on brand sentiment is crucial in today’s digital age. One negative social media post can spiral and cause a full-blown PR crisis. Sentiment analysis helps businesses manage their brand reputation by identifying and addressing negative sentiment promptly.

It enables them to protect their image and build stronger, more trusted relationships with their customer base.

4. Using Data to Inform Decision-making

It transforms raw data into actionable insights. Combined with analytics in marketing reports , it equips decision-makers and marketers with the data-driven intelligence needed to make informed choices regarding marketing strategies, product development, and customer engagement, leading to more effective decision-making.

5. Adapting to Changing Spending Habits

Consumer spending habits evolve, and businesses need to stay agile. People might like a product today, but start hating it tomorrow. Sentiment analysis aids in tracking these shifts, enabling companies to pivot their strategies and offerings to meet new consumer preferences and demands effectively.

6. Analyzing competitors

Sentiment analysis aids businesses in benchmarking against their competitors and gaining valuable insights into their strengths and weaknesses from a customer’s perspective.

By conducting competitor analysis , you can pinpoint areas where your brand excels and identify opportunities for improvement. This information serves as a guide for your strategic decisions, enabling you to distinguish yourself in a crowded marketplace.

In essence, sentiment analysis is the compass that guides marketers toward understanding, connecting with, and satisfying the emotional needs of their customers, thereby paving the way for effective marketing strategies that stand the test of time.

By leveraging sentiment analysis, businesses can harness the power of data to remain agile, responsive, and aligned with their customers’ ever-evolving expectations.

This data-driven approach not only helps marketers enhance customer satisfaction but also propels businesses toward sustainable growth and success in an increasingly competitive landscape.

Radostin Anastasov is Content Specialist at SERanking. He has acquired his marketing and PR experience across various industries, such as AI, finance, and communications. When he’s not busy writing, he plays basketball to rock music.

Content Marketer @SERanking

Get the latest and greatest digital marketing + social media tips every week!

Table of Contents

• Privacy Policy

Home » Sentiment Analysis – Tools, Techniques and Examples

Sentiment Analysis – Tools, Techniques and Examples

Table of Contents

Sentiment Analysis

Sentiment analysis, also referred to as opinion mining, is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

In other words, it’s the process of determining the emotional tone or subjective opinion within large amounts of text. This could be used on social media posts, customer reviews, or any other text data where people are expressing their opinions or feelings.

Sentiment analysis can classify text as being positive, negative, or neutral. More advanced sentiment analysis methods can also categorize text into more specific emotional states like “happy,” “frustrated,” “excited,” etc.

Sentiment Analysis Methods

Sentiment analysis methods can be broadly divided into three categories: rule-based, machine learning-based, and hybrid.

Rule-based methods

This approach uses a set of manually crafted rules to identify sentiment. This often involves creating or using a sentiment lexicon—a list of words and phrases each assigned a sentiment score (positive, negative, or neutral). The overall sentiment of a text is then determined based on the scores of the individual words or phrases it contains. Rule-based methods might also take into account more complex linguistic features, like negations (“not good”) and intensifiers (“very good”).

Machine learning-based methods

These methods involve training a machine learning model on a dataset of text where each piece of text is labeled with its sentiment. The model learns to associate features of the text (like the words it contains, the order of the words, etc.) with the sentiment. When given new, unlabeled text, it can then predict the sentiment based on these learned associations. The machine learning model used could be a traditional algorithm like Naive Bayes, Support Vector Machines, or a Decision Tree, or a more complex neural network model like a Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), or a transformer model like BERT or GPT.

Hybrid methods

As the name suggests, hybrid methods combine rule-based and machine-learning-based approaches. They might use a rule-based method to generate features that are fed into a machine-learning model, or use a machine-learning model to predict sentiment, which is then refined using a set of rules. The idea is to try to get the best of both worlds—the linguistic knowledge encapsulated in the rules, and the ability of the machine learning model to learn complex patterns in the data .

How does sentiment analysis work?

The overall process can be divided into several steps:

• Data Collection : The first step involves collecting data for sentiment analysis. This could be social media posts, customer reviews, survey responses, or any other text data where people are expressing their opinions or feelings.
• Text Preprocessing : The collected raw text data often needs to be cleaned and standardized before analysis. This could involve removing irrelevant data (like HTML tags or URLs), converting all text to lowercase, correcting spelling mistakes, removing stop words (commonly used words like “is”, “and”, “the”, etc. that don’t carry much meaning), and other techniques to make the data more uniform. This stage often also involves tokenization (breaking the text into individual words or tokens), and sometimes lemmatization (reducing words to their base or root form).
• Feature Extraction : In this stage, meaningful features are extracted from the preprocessed text data. The simplest approach is the bag of words model, where the text is represented as a set of its words, disregarding grammar and word order but keeping the frequency of each word. More complex approaches could involve word embeddings (where each word is represented as a vector in multi-dimensional space), or even sentence or paragraph embeddings.
• Model Training (Machine Learning Approach) : If using a machine learning approach, the next step is to use the features extracted from the text to train a model. The model learns to associate certain features with positive, negative, or neutral sentiments based on the training data. Different algorithms could be used in this stage, including logistic regression, support vector machines, decision trees, or even deep learning models like recurrent neural networks or transformers.
• Sentiment Classification (Rule-Based Approach) : If using a rule-based approach, instead of training a model, a set of manually crafted rules are used to determine the sentiment of the text. For example, the text might be classified as positive if it contains more positive words than negative.
• Evaluation : The final step involves evaluating the performance of the sentiment analysis system, usually by comparing its predictions to a set of manually labeled data.

Sentiment Analysis Techniques

Sentiment Analysis Techniques are as follows:

Lexicon-based Sentiment Analysis

This is a simple and straightforward technique in which the sentiment of a text is determined by the words it contains. A sentiment lexicon is a list of lexical features (e.g., words) which are labeled according to their semantic orientation as either positive, negative, or neutral. In this technique, the sentiment of a text is calculated by identifying the sentiment words and the way they’re combined.

Machine Learning-based Sentiment Analysis

This technique uses machine learning algorithms to classify text as positive, negative, or neutral. This is usually done by training a model on a pre-labeled dataset and then using this model to classify new, unseen data. Algorithms used could be traditional ones like Naive Bayes, Support Vector Machines, Decision Trees, or more complex methods like Neural Networks and Deep Learning techniques (e.g., Convolutional Neural Networks, Recurrent Neural Networks, or Transformer-based models like BERT and GPT).

Hybrid Sentiment Analysis

This method combines the lexicon and machine learning-based approaches. For example, it might use a lexicon-based approach to help label data for machine learning, or use machine learning to automate and improve lexicon-based sentiment analysis.

Aspect-Based Sentiment Analysis (ABSA)

In ABSA, the goal is not only to understand the sentiment of the text but also to understand the specific aspects or features that the sentiment is associated with. For example, in a product review, the user might express positive sentiment about the battery life of a phone (aspect: battery life) but negative sentiment about its weight (aspect: weight).

Emotion Detection

This goes beyond basic sentiment analysis and aims to detect specific emotions expressed in a text, such as happiness, anger, sadness, etc. This could be done using emotion-specific lexicons or more complex machine learning models.

Social Media Sentiment Analysis

Social media platforms like Twitter and Facebook provide a rich source of text for sentiment analysis. Special techniques might be needed to handle the short, informal, and often misspelled or abbreviated text common on these platforms.

Multilingual Sentiment Analysis

This is the application of sentiment analysis techniques to text in multiple languages. This often requires language-specific resources like lexicons and labeled data, as well as techniques for handling translation and cultural differences in how sentiment is expressed.

Sarcasm Detection

This is a particularly challenging area of sentiment analysis, as sarcastic comments often say the opposite of what they mean, making them difficult to interpret correctly. Techniques for sarcasm detection often rely on context and common patterns in the way sarcasm is used.

Sentiment Analysis Tool

There are numerous sentiment analysis tools and libraries available that cater to different needs. Some are designed for researchers and data scientists and require programming skills, while others are commercial platforms designed for businesses. Here are a few examples:

• Natural Language Toolkit (NLTK) : A popular Python library for natural language processing. It includes functionality for sentiment analysis, along with many other NLP tasks.
• TextBlob : Another Python library that provides a simple API for diving into common NLP tasks such as part-of-speech tagging, noun phrase extraction, and sentiment analysis.
• VADER Sentiment Analysis : VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that’s specifically attuned to sentiments expressed in social media.
• Stanford CoreNLP : A Java-based toolkit providing various NLP tools that support many languages. It includes a sentiment analysis tool.
• Spacy : A Python NLP library that can be extended with separate machine learning models allowing more complex sentiment analysis.
• Google Cloud Natural Language API : A cloud-based tool that uses machine learning to analyze text. It provides sentiment analysis, entity analysis, entity sentiment analysis, and more.
• IBM Watson Tone Analyzer : It provides sentiment analysis and also detects seven tones in written text: anger, fear, joy, sadness, confident, analytical, and tentative.
• Microsoft Azure Text Analytics API : Part of Azure’s Cognitive Services, it provides sentiment analysis, key phrase extraction, and language detection.
• Amazon Comprehend : This is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It provides sentiment analysis functionality and supports multiple languages.
• MonkeyLearn : An AI platform that allows you to classify and extract actionable data from raw text. It has pre-trained models for sentiment analysis and also allows you to train custom models.

What are the challenges in sentiment analysis?

Sentiment analysis, while a powerful tool, is not without its challenges. Some of the main challenges include:

• Sarcasm and Irony : Sarcasm and irony involve saying something but meaning the opposite, which can be very difficult for sentiment analysis tools to correctly interpret. For example, a statement like “Oh great, just what I needed” might be labeled as positive by a sentiment analysis tool, while a human would recognize the sarcasm and label it as negative.
• Contextual Ambiguity : The sentiment of a word or phrase can depend heavily on its context. For example, “unpredictable” might be negative when used to describe a car’s handling but positive when used to describe a book’s plot.
• Domain-Specific Language : Different fields or industries might use language in unique ways, including jargon and slang. A word that’s positive in one domain might be negative in another, and a sentiment analysis tool trained on general language data might not be able to accurately analyze text from a specific domain.
• Negations and Double Negatives : Phrases with negations or double negatives can be tricky for sentiment analysis. For example, “not bad” is a positive sentiment, and “not uninteresting” is also generally positive.
• Emotionally Complex Statements : Sentiment analysis often classifies text as positive, negative, or neutral, but human emotions are more complex. A text could contain multiple emotions, or emotions that don’t fit neatly into the positive-negative scale, and a simple sentiment analysis might not capture this complexity.
• Language and Cultural Differences : Sentiment can be expressed differently in different languages and cultures. For example, the same phrase translated into different languages might have different sentiment due to cultural nuances. A sentiment analysis tool trained on English-language data might not work as well on other languages.
• Lack of Labeled Data : Machine learning-based sentiment analysis tools require large amounts of labeled data for training, and it can be time-consuming and expensive to create this data. There might also be a lack of labeled data in specific domains or languages.

Applications of Sentiment Analysis

sentiment analysis is used in a variety of fields and for a wide range of applications, leveraging the fact that much of our communication and expression is now in digital text form. Here are some notable applications:

• Business and Customer Insights : Companies can use sentiment analysis to monitor customer reviews of their products and services on different platforms to understand what their customers like or dislike. This can guide improvements and innovation.
• Social Media Monitoring : Sentiment analysis can help monitor social media platforms to understand public sentiment about a brand, a product, or a service. This can also help in crisis management, as spikes in negative sentiment can be early indicators of a problem.
• Market Research and Analysis : By gauging public sentiment on social media and other online platforms, companies can gain insights into market trends and consumer behaviors, helping them strategically plan their marketing efforts.
• Political Campaigns and Polls : Politicians and political parties can use sentiment analysis to understand public opinion about them or about key issues, allowing them to adjust their campaigns or policies accordingly.
• Financial Market Analysis : Some traders use sentiment analysis to predict market trends. For example, negative sentiments from company reports, financial news, or social media discussions could potentially signal a fall in stock prices.
• Healthcare and Public Health : Sentiment analysis can be used to understand public sentiment about health interventions, disease outbreaks, or health behaviors, which can inform public health efforts.
• Product Analytics : Sentiment analysis can be used to analyze user reviews and feedback about software products. It can help to identify common pain points or highly appreciated features, guiding product development.
• Human Resources and Employee Feedback : Sentiment analysis can be used to analyze employee feedback or comments, helping HR identify common themes, improve employee satisfaction, and reduce churn.
• Entertainment Industry : Sentiment analysis can be used to gauge public opinion about movies, music, games, and other entertainment products. For example, movie producers can use sentiment analysis to predict how well a movie will be received.
• Automated Customer Service : Sentiment analysis can be used in chatbots and other automated systems to detect the sentiment of user inputs and adjust responses accordingly.

Advantages of Sentiment Analysis

Sentiment analysis offers several key benefits, especially in our digitally connected world where vast amounts of textual data are generated every day. Here are some of the primary advantages:

• Customer Insights : Sentiment analysis allows companies to gain a deeper understanding of their customers’ perceptions, opinions, and feelings about their products or services. This information can inform business strategies, guide product improvements, and enhance overall customer experience.
• Brand Monitoring : Companies can use sentiment analysis to keep track of their brand’s reputation in real-time. By analyzing sentiments in social media posts, reviews, and comments, companies can detect shifts in public opinion and respond proactively.
• Competitive Analysis : By applying sentiment analysis on social media conversations or product reviews related to competitors, companies can gain insights into strengths and weaknesses of competitors’ offerings, helping in strategic decision-making.
• Crisis Management : Sentiment analysis can help in identifying negative sentiments in real-time, which can act as an early warning system for crises or issues that need immediate attention.
• Market Research : Sentiment analysis can be used to gauge public opinion on a large scale, which is invaluable for market research. Companies can get insights into consumer reactions towards product launches, marketing campaigns, or events.
• Improved Customer Service : By integrating sentiment analysis in customer service, companies can prioritize responses based on sentiment scores. Customers with negative sentiments can be prioritized to improve their experience and mitigate potential churn.
• Efficient and Scalable : Manual analysis of textual data can be incredibly time-consuming, particularly when dealing with large volumes of data. Sentiment analysis automates this process, making it more efficient and scalable.
• Enhanced Employee Feedback Analysis : Organizations can use sentiment analysis to understand employee feedback, identify areas of improvement, and foster a better workplace environment.

Disadvantages of Sentiment Analysis

Despite its many advantages, sentiment analysis also has its limitations. Here are some of the key disadvantages or challenges:

• Difficulty with Sarcasm and Irony : Automated sentiment analysis systems can struggle with understanding sarcasm and irony, which often involve saying one thing but meaning the opposite. This can lead to misinterpretation of sentiments.
• Understanding Context : The sentiment value of certain phrases may change based on the context in which they’re used. Sentiment analysis algorithms may find it challenging to correctly interpret such context-dependent phrases.
• Handling Negations : Sentiment analysis systems might struggle with phrases that include negations. For example, the phrase “not great” is negative, but a simplistic sentiment analysis algorithm might interpret it as positive because it contains the word “great”.
• Lack of Nuance : Most sentiment analysis tools categorize text into positive, negative, or neutral sentiments. Human emotions, however, are far more complex and nuanced. As a result, such tools might oversimplify the sentiment.
• Cultural and Linguistic Differences : Sentiment analysis tools might struggle with slang, idioms, and language-specific expressions. In addition, they might not account for cultural differences in expressing sentiments.
• Need for Large Amounts of Labeled Data : Machine learning-based sentiment analysis tools require large amounts of labeled data for training. Creating this data can be time-consuming and expensive, and there might be a lack of labeled data in specific domains or languages.
• Accuracy : The accuracy of sentiment analysis can vary depending on the complexity of the text and the quality of the tool used. Misinterpretations can lead to misleading conclusions.
• Spam and Bots : In areas like social media analysis, it’s often difficult to distinguish between genuine user content and content generated by spam or bots. This can influence the sentiment analysis results.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Digital Ethnography – Types, Methods and Examples

Predictive Analytics – Techniques, Tools and...

Big Data Analytics -Types, Tools and Methods

Diagnostic Analytics – Methods, Tools and...

Blockchain Research – Methods, Types and Examples

Social Network Analysis – Types, Tools and...

Send us an email

Top 15 sentiment analysis tools to consider in 2024

Written by by Mahnoor Sheikh

Published on  January 16, 2024

Reading time  6 minutes

Just like non-verbal cues in face-to-face communication, there’s human emotion weaved into the language your customers are using online.

Decoding those emotions and understanding how customers truly feel about your brand is what sentiment analysis is all about.

But tracking sentiment is no piece of cake.

We’re talking about analyzing thousands of conversations, brand mentions and reviews spread across multiple websites and platforms—some of them happening in real-time.

You need a sentiment analysis tool for the job.

In this post, you’ll find some of the best sentiment analysis tools to help you monitor and analyze customer sentiment around your brand.

What is a sentiment analysis tool?

Applications of a sentiment analysis tool, top 15 sentiment analysis tools to consider.

• Use sentiment analysis tools to make data-driven decisions back by AI

A sentiment analysis tool uses artificial intelligence (AI) to analyze textual data and pick up on the emotions people are expressing, like joy, frustration or disappointment.

It leverages natural language processing (NLP) to understand the context behind social media posts, reviews and feedback—much like a human but at a much faster rate and larger scale.

Then, it calculates the average sentiment around your brand, classifying it as positive, negative or neutral. Some tools also help you monitor your competitors’ customer sentiment score.

Some sentiment analysis tools can also analyze video content and identify expressions by using facial and object recognition technology.

In the context of AI marketing , sentiment analysis tools help businesses gain insight into public perception, identify emerging trends, improve customer care and experience, and craft more targeted campaigns that resonate with buyers and drive business growth.

Sentiment analysis tools are revolutionizing how businesses understand and respond to customers. Here are some specific ways brands can benefit from these tools:

• Social Listening : Keep an eye on customer opinions and reactions to brands, products, services, campaigns, events and trends on social media.
• Review Management : Analyze customer feedback across multiple platforms and respond promptly and empathetically to improve customer satisfaction.
• Competitive Analysis : Compare sentiment towards your brand with competitors to understand where you stand in terms of positioning and public perception.
• Brand Insights: Gather and interpret data on brand reputation, customer experience , and product strengths and weaknesses to develop a solid brand strategy.
• Opinion Mining: Analyze both customer and employee feedback to get a clear picture of your company’s performance and identify areas for improvement.

Full stack sentiment analysis tools

These tools can pull information from multiple sources and employ techniques like linear regression to detect fraud and authenticate data. They also run on proprietary AI technology, which makes them powerful, flexible and scalable for all kinds of businesses.

1. Sprout Social

Sprout Social offers all-in-one social media management solutions, including AI-powered listening and granular sentiment analysis.

Monitor millions of conversations happening in your industry across multiple platforms. Sprout’s AI can detect sentiment in complex sentences and even emojis, giving you an accurate picture of how customers truly think and feel about specific topics or brands.

View the average customer sentiment around your brand and track sentiment trends over time. Filter individual messages and posts by sentiment to respond quickly and effectively.

Sprout also supports multilingual sentiment analysis, which helps you understand and resonate with a diverse, international customer base. Access a full scope of data tagged and filtered by smart category, without changing your query, whether by people, place, product or more. Furthermore, our Queries by AI Assist feature generates keyword suggestions for Listening queries that can further enhance your analysis landscape

2. InMoment (Lexalytics)

InMoment is a customer experience platform that uses Lexalytics’ AI to analyze text from multiple sources and translate it into meaningful insights.

It supports over 30 languages and dialects, and can dig deep into surveys and reviews to find the sentiment, intent, effort and emotion behind the words.

3. Medallia

Medallia’s experience management platform offers powerful listening features that can pinpoint sentiment in text, speech and even video.

The platform excels in collecting and analyzing real-time feedback from multiple sources, including social media, surveys, reviews, SMS, emails, voice conversations and more.

4. Qualtrics (Clarabridge)

Qualtrics is an experience management platform that offers Text iQ—a sentiment analysis tool that leverages advanced NLP technology to analyze unstructured data from various sources, including social media, surveys and customer support interactions.

The tool can automatically categorize feedback into themes, making it easier to identify common trends and issues. It can also assign sentiment scores to quantifies emotions and and analyze text in multiple languages.

Social media sentiment analysis tools

Focusing specifically on social media platforms, these tools are designed to analyze sentiment expressed in tweets, posts and comments. They help businesses better understand their social media presence and how their audience feels about their brand.

5. Brandwatch

Brandwatch offers a suite of tools for social media research and management. Their listening tool helps you analyze sentiment along with tracking brand mentions and conversations across various social media platforms.

Classify sentiment in messages and posts as positive, negative or neutral, track changes in sentiment over time and view the overall sentiment score on your dashboard.

Buffer offers easy-to-use social media management tools that help with publishing, analyzing performance and engagement.

One of the tool’s features is tagging the sentiment in posts as ‘negative, ‘question’ or ‘order’ so brands can sort through conversations, and plan and prioritize their responses.

7. Agorapulse

Agorapulse is another social media management software that specializes in publishing and organizing your inbox.

It offers basic sentiment analysis capabilities in that it lets you add labels like “positive” and “negative” to inbox items that contain specific words, such as “happy”, “great”, “bad” or “awful.”

Add labels to messages manually or use the Inbox Assistant to automatically go through your messages and label all relevant items that contain the specified keywords.

Awario is a specialized brand monitoring tool that helps you track mentions across various social media platforms and identify the sentiment in each comment, post or review.

You can track sentiment over time, prevent crises from escalating by prioritizing mentions with negative sentiment, compare sentiment with competitors and analyze reactions to campaigns.

News sentiment analysis tools

These tools specialize in monitoring and analyzing sentiment in news content. They use News APIs to mine data and provide insights into how the media portrays a brand or topic.

9. Aylien (Quantexa)

Aylien uses AI to monitor, organize and analyze sentiment in news content. This makes it a valuable tool for PR and communications teams to keep an eye on trends and monitor public opinion and perception about brands and topics.

A key feature of the tool is entity-level sentiment analysis, which determines the sentiment behind each individual entity discussed in a single news piece.

10. Cision Communication Cloud

Cision is an AI-powered PR platform with robust media monitoring capabilities.

Its features include sentiment analysis of news stories pulled from over 100 million sources in 96 languages, including global, national, regional, local, print and paywalled publications.

11. Meltwater

Meltwater’s AI-powered tools help you monitor trends and public opinion about your brand. Their sentiment analysis feature breaks down the tone of news content into positive, negative or neutral using deep-learning technology.

The tool can handle 242 languages, offering detailed sentiment analysis for 218 of them. This makes it versatile and useful for tracking global news sentiment.

Text sentiment analysis tools

These tools run on proprietary AI technology but don’t have a built-in source of data tapped via direct APIs, such as through partnerships with social media or news platforms.

12. MonkeyLearn

MonkeyLearn is a simple, straightforward text analysis tool that lets you organize, label and visualize data like customer feedback, surveys and more.

The tool uses AI to detect, categorize and track sentiment over time. You can use ready-made machine learning models or build and train your own without coding. MonkeyLearn also connects easily to apps and BI tools using SQL, API and native integrations.

13. Google NLP API

Google NLP API is a text analysis tool designed to extract insights and opinions from various documents, including emails, chats and social media, through entity and sentiment analysis.

It supports multimedia content by integrating with Speech-to-Text and Vision APIs to analyze audio files and scanned documents. Plus, its Translation API can analyze sentiment across multiple languages.

14. Amazon Comprehend

Amazon’s text analysis tool goes through documents, emails, social media and customer support tickets to uncover insights. It identifies key elements such as phrases, sentiment and topics, and even lets businesses train models to classify documents.

Moreover, it helps maintain data privacy and protects sensitive information by identifying and redacting Personally Identifiable Information (PII).

15. Microsoft Azure

Azure AI Language lets you build natural language processing applications with minimal machine learning expertise. Pinpoint key terms, analyze sentiment, summarize text and develop conversational interfaces.

The platform offers multilingual models that adapt across languages. Azure also maintains strict privacy standards by using text inputs exclusively for training models.

Use sentiment analysis tools to make data-driven decisions backed by AI

AI-powered sentiment analysis tools make it incredibly easy for businesses to understand and respond effectively to customer emotions and opinions.

While there are dozens of tools out there, Sprout Social stands out with its proprietary AI and advanced sentiment analysis and listening features. Try it for yourself with a free 30-day trial and transform customer sentiment into actionable insights for your brand.

• Social Media Content

Post Performance Report: Brands creating AI-generated content with intention

AI Bootcamp: Supercharge Your Social Workflow Today

• Technology & Software

How AI insights improve decision making

• Digital Transformation

11 AI marketing tools your team should be using in 2024

• Now on slide

Build and grow stronger relationships on social

Sprout Social helps you understand and reach your audience, engage your community and measure performance with the only all-in-one social media management platform built for connection.

Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis

• Original Research
• Published: 18 April 2024
• Volume 5 , article number  446 , ( 2024 )

Cite this article

• Georgios-Markos Chatziloizos 1 ,
• Dimitrios Gunopulos 2 &
• Konstantinos Konstantinou 2  

38 Accesses

Explore all metrics

Machine learning and deep learning techniques are applied by researchers with a background in both economics and computer science, to predict stock prices and trends. These techniques are particularly attractive as an alternative to existing models and methodologies because of their ability to extract abstract features from data. Most existing research approaches are based on using either numerical/economical data or textual/sentimental data. In this article, we use cutting-edge deep learning/machine learning approaches on both numerical/economical data and textual/sentimental data in order not only to predict stock market prices and trends based on combined data but also to understand how a stock's Technical Analysis can be strengthened by using Sentiment Analysis. Using the four tickers AAPL, GOOG, NVDA and S&P 500 Information Technology, we collected historical financial data and historical textual data and we used each type of data individually and in unison, to display in which case the results were more accurate and more profitable. We describe in detail how we analyzed each type of data, and how we used it to come up with our results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

A survey on sentiment analysis methods, applications, and challenges

Machine learning and deep learning

Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review

Data availability.

The economic data utilized in this study was sourced from Yahoo Finance. Economic data used in this research is publicly available and can be accessed through Yahoo Finance's platform. The financial_phrasebank dataset referenced in this study was also utilized. The dataset is publicly available.

Liberti JM, Petersen M. Information: hard and soft. Rev Corp Finance Stud. 2019;8(1):1–41.

Article   Google Scholar  

Chong E, Han C, Park FC. Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl. 2017;83:187–205.

Fischer T, Krauss C. Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res. 2018;270(2):654–69.

Article   MathSciNet   Google Scholar  

Long W, Lu Z, Cui L. Deep learning-based feature engineering for stock price movement prediction. Knowl Based Syst. 2019;164:163–73.

Zhong X, Enke D. Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financ Innov. 2019;5(1):1–20.

Vignesh CK. Applying machine learning models in stock market prediction. EPRA Int J Res Dev. 2020;5(4):395–8.

Google Scholar  

Nabipour M, Nayyeri P, Jabani H, Mosavi A, Salwana E. Deep learning for stock market prediction. Entropy. 2020;22(8):840.

Ferreira F, Gandomi A, Cardoso R. Artificial intelligence applied to stock market trading: a review. IEEE Access. 2021;9:30898–917.

Sun A, Lachanski M, Fabozzi F. Trade the tweet: social media text mining and sparse matrix factorization for stock market prediction. Int Rev Financ Anal. 2016;48:272–81.

Shapiro AH, Sudhof M, Wilson D. Measuring news sentiment, Federal Reserve Bank of San Francisco Working Paper 2017-01. 2017. https://doi.org/10.24148/wp2017-01 .

Pagolu VS, Reddy KN, Panda G, Majhi B (2016) Sentiment analysis of twitter data for predicting stock market movements. 2016 International Conference on Signal Processing, Communication, Power and Embedded System, Paralakhemundi, India, 3–5 October 2016, pp 1345–1350. https://doi.org/10.1109/SCOPES.2016.7955659

Batra R, Daudpota SM. Integrating StockTwits with sentiment analysis for better prediction of stock price movement. In: Proceedings of International Conference on Computing, Mathematics and Engineering Technologies; 2018. pp. 1–5.

Tabari N, Seyeditabari A, Peddi T, Hadzikadic M, Zadrozny W. A comparison of neural network methods for accurate sentiment analysis of Stock Market Tweets. In: ECML PKDD 2018 Workshops. MIDAS 2018, PAP 2018. LNCS, vol 11054. 2019; Springer.

Chatziloizos G, Gunopulos D, Konstantinou K. Forecasting stock market trends using deep learning on financial and textual data. Proceedings of the 10th International Conference on Data Science, Technology and Applications (DATA 2021). SciTePress. pp. 105–114.

Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press; 2016.

Ussama Y, Soon C, Vijayalakshmi A, Jaideep V. Sentiment-based analysis of tweets during the US Presidential Elections. 2017. pp. 1–10. https://doi.org/10.1145/3085228.3085285 .

Rao T, Srivastava S. Analyzing stock market movements using twitter sentiment analysis. Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM); 2012. pp. 119–123 .

Lounnapha S, Zhongdong W, Sookasame C. Research on stock price prediction method based on convolutional neural network. Proceedings of the international conference on virtual reality and intelligent systems (ICVRIS). IEEE; 2019. pp. 173–6.

Yan X, Zhao J. Application of improved convolution neural network in financial forecasting. Proceedings of the 4th IEEE international conference on cloud computing and big data analytics. 2019. pp. 321–6.

Cao J, Wang J. Stock price forecasting model based on modified convolution neural network and financial time series analysis. Int J Commun Syst. 2019;32:e3987.

Malo P, Sinha A, Korhonen P, Wallenius J, Takala P. Good debt or bad debt. J Assoc Inf Sci Technol. 2014;65:782–96. https://doi.org/10.1002/asi.23062 .

Hutto CJ, Gilbert E. VADER: a parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the 8th international conference on weblogs and social media. ICWSM; 2015.

Loughran T, McDonald B. When is a liability not a liability? textual analysis, dictionaries and 10-Ks. J Finance. 2011;66:35–65.

Fiol-Roig G, Miró-Julià M, Isern-Deyà AP. Applying data mining techniques to stock market analysis. In: Trends in practical applications of agents and multiagent systems. Advances in intelligent and soft computing, vol 71. Springer; 2010.

chart-formations.com. http://www.chart-formations.com/indicators/atr.aspx?cat=volatility

Larry Williams CTI Publishing. https://williamspercentr.com/the-original-percent-r

Pedregosa F, et al. Scikit-learn: machine learning in Python. JMLR. 2011;12:2825–30.

MathSciNet   Google Scholar  

Kontopoulos E, Berberidis C, Dergiades T, Bassiliades N. Ontology-based sentiment analysis of twitter posts. Expert Syst Appl. 2013;40(10):4065–74.

Download references

This study has received no funding.

Author information

Authors and affiliations.

PSL Research University, Paris, France

Georgios-Markos Chatziloizos

Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece

Dimitrios Gunopulos & Konstantinos Konstantinou

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Georgios-Markos Chatziloizos .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Data Science, Technology and Applications” guest edited by Slimane Hammoudi and Christoph Quix.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Chatziloizos, GM., Gunopulos, D. & Konstantinou, K. Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis. SN COMPUT. SCI. 5 , 446 (2024). https://doi.org/10.1007/s42979-024-02651-5

Download citation

Received : 11 March 2022

Accepted : 24 January 2024

Published : 18 April 2024

DOI : https://doi.org/10.1007/s42979-024-02651-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Technical analysis
• Sentiment analysis
• Machine learning
• Stock market
• Deep learning

Advertisement

• Find a journal
• Publish with us
• Track your research

You are using an outdated browser. Please upgrade your browser to improve your experience.

Global Alternative Data Market: Analysis By Type (Credit & Debit Card Transactions, Social & Sentiment Data, Web Scraped Data, Web Traffic, Mobile Application Usage, Satellite & Weather Data, Geo-location Records, Email Receipts and Other Data Types), By

• Table of Contents
• Description
• Search Inside Report
• Related Reports

Global Alternative Data Market: Analysis By Type (Credit & Debit Card Transactions, Social & Sentiment Data, Web Scraped Data, Web Traffic, Mobile Application Usage, Satellite & Weather Data, Geo-location Records, Email Receipts and Other Data Types), By Industry (BFSI, IT &, Telecommunication, Retail, Automotive & Transport, Media & Entertainment, Energy, Industrial and Others), By End User (Hedge Fund Operators, Investment Institutions, Retail Companies and Other), By Region Size and Trends with Impact of COVID-19 and Forecast up to 2029

1. Executive Summary 2. Introduction 2.1 Alternative Data: An Overview 2.1.1 Introduction to Alternative Data 2.1.2 Advantages of Alternative Data Table 1: Advantages of Alternative Data 2.2 Alternative Data Segmentation: An Overview 2.2.1 Alternative Data Segmentation Table 2: Alternative Data Segmentation 3. Global Market Analysis 3.1 Global Alternative Data Market: An Analysis 3.1.1 Global Alternative Data Market: An Overview 3.1.2 Global Alternative Data Market by Value Table 3: Global Alternative Data Market by Value; 2019-2023 (US$ Billion) Table 4: Global Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.1.3 Global Alternative Data Market by Type (Credit & Debit Card Transactions, Social & Sentiment Data, Web Scraped Data, Web Traffic, Mobile Application Usage, Satellite & Weather Data, Geo-location Records, Email Receipts and Other Data Types) Table 5: Global Alternative Data Market by Type; 2023 (Percentage, %) 3.1.4 Global Alternative Data Market by Industry (BFSI, IT & Telecommunication, Retail, Automotive & Transport, Media & Entertainment, Energy, Industrial and Others) Table 6: Global Alternative Data Market by Industry ; 2023 (Percentage, %) 3.1.5 Global Alternative Data Market by End User (Hedge Fund Operators, Investment Institutions, Retail Companies, and Other) Table 7: Global Alternative Data Market by End User; 2023 (Percentage, %) 3.1.6 Global Alternative Data Market by Region (North America, Europe, Asia Pacific, Middle East and Africa, and Latin America) Table 8: Global Alternative Data Market by Region; 2023 (Percentage, %) 3.2 Global Alternative Data Market: Type Analysis 3.2.1 Global Alternative Data Market by Type: An Overview 3.2.2 Global Credit & Debit Card Transactions Grade Alternative Data Market by Value Table 9: Global Credit & Debit Card Transactions Grade Alternative Data Market by Value; 2019-2023 (US$ Million) Table 10: Global Credit & Debit Card Transactions Grade Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.3 Global Social & Sentiment Data Grade Alternative Data Market by Value Table 11: Global Social & Sentiment Data Grade Alternative Data Market by Value; 2019-2023 (US$ Million) Table 12: Global Social & Sentiment Data Grade Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.4 Global Web Scraped Data Alternative Data Market by Value Table 13: Global Web Scraped Data Alternative Data Market by Value; 2019-2023 (US$ Million) Table 14: Global Web Scraped Data Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.5 Global Web Traffic Alternative Data Market by Value Table 15: Global Web Traffic Alternative Data Market by Value; 2019-2023 (US$ Million) Table 16: Global Web Traffic Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.6 Global Mobile Application Usage Alternative Data Market by Value Table 17: Global Mobile Application Usage Alternative Data Market by Value; 2019-2023 (US$ Million) Table 18: Global Mobile Application Usage Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.7 Global Satellite & Weather Data Alternative Data Market by Value Table 19: Global Satellite & Weather Data Alternative Data Market by Value; 2019-2023 (US$ Million) Table 20: Global Satellite & Weather Data Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.8 Global Geo-location Records Alternative Data Market by Value Table 21: Global Geo-location Records Alternative Data Market by Value; 2019-2023 (US$ Million) Table 22: Global Geo-location Records Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.9 Global Email Receipts Alternative Data Market by Value Table 23: Global Email Receipts Alternative Data Market by Value; 2019-2023 (US$ Million) Table 24: Global Email Receipts Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.2.10 Global Others Alternative Data Market by Value Table 25: Global Other Alternative Data Market by Value; 2019-2023 (US$ Million) Table 26: Global Other Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.3 Global Alternative Data Market: Industry Analysis 3.3.1 Global Alternative Data Market by Industry : An Overview 3.3.2 Global BFSI Alternative Data Market by Value Table 27: Global BFSI Alternative Data Market by Value; 2019-2023 (US$ Million) Table 28: Global BFSI Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.3.3 Global IT & Telecommunication Alternative Data Market by Value Table 29: Global IT & Telecommunication Alternative Data Market by Value; 2019-2023 (US$ Million) Table 30: Global IT & Telecommunication Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.3.4 Global Retail Alternative Data Market by Value Table 31: Global Retail Alternative Data Market by Value; 2019-2023 (US$ Million) Table 32: Global Retail Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.3.5 Global Automotive & Transport Alternative Data Market by Value Table 33: Global Automotive & Transport Alternative Data Market by Value; 2019-2023 (US$ Million) Table 34: Global Automotive & Transport Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.3.6 Global Media & Entertainment Alternative Data Market by Value Table 35: Global Media & Entertainment Alternative Data Market by Value; 2019-2023 (US$ Million) Table 36: Global Media & Entertainment Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.3.7 Global Energy Alternative Data Market by Value Table 37: Global Energy Alternative Data Market by Value; 2019-2023 (US$ Million) Table 38: Global Energy Alternative Data Market by Value; 2024-2029 (US$ Million) 3.3.8 Global Industrial Alternative Data Market by Value Table 39: Global Industrial Alternative Data Market by Value; 2019-2023 (US$ Million) Table 40: Global Industrial Alternative Data Market by Value; 2024-2029 (US$ Million) 3.3.9 Global Others Alternative Data Market by Value Table 41: Global Others Alternative Data Market by Value; 2019-2023 (US$ Million) Table 42: Global Others Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.4 Global Alternative Data Market: End User Analysis 3.4.1 Global Alternative Data Market by End User : An Overview 3.4.2 Global Hedge Fund Operators Alternative Data Market by Value Table 43: Global Hedge Fund Operators Alternative Data Market by Value; 2019-2023 (US$ Billion) Table 44: Global Hedge Fund Operators Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.4.3 Global Investment Institutions Alternative Data Market by Value Table 45: Global Investment Institutions Alternative Data Market by Value; 2019-2023 (US$ Million) Table 46: Global Investment Institutions Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.4.4 Global Retail Companies Alternative Data Market by Value Table 47: Global Retail Companies Alternative Data Market by Value; 2019-2023 (US$ Million) Table 48: Global Retail Companies Alternative Data Market by Value; 2024-2029 (US$ Billion) 3.4.5 Global Other Alternative Data Market by Value Table 49: Global Other Alternative Data Market by Value; 2019-2023 (US$ Million) Table 50: Global Other Alternative Data Market by Value; 2024-2029 (US$ Billion) 4. Regional Market Analysis 4.1 North America Alternative Data Market: An Analysis 4.1.1 North America Alternative Data Market: An Overview 4.1.2 North America Alternative Data Market by Value Table 51: North America Alternative Data Market by Value; 2019-2023 (US$ Billion) Table 52: North America Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.1.3 North America Alternative Data Market by Region (the US, Canada, and Mexico) Table 53: North America Alternative Data Market by Region; 2023 (Percentage, %) 4.1.4 The US Alternative Data Market by Value Table 54: The US Alternative Data Market by Value; 2019-2023 (US$ Billion) Table 55: The US Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.1.5 Canada Alternative Data Market by Value Table 56: Canada Alternative Data Market by Value; 2019-2023 (US$ Million) Table 57: Canada Alternative Data Market by Value; 2024-2029 (US$ Million) 4.1.6 Mexico Alternative Data Market by Value Table 58: Mexico Alternative Data Market by Value; 2019-2023 (US$ Million) Table 59: Mexico Alternative Data Market by Value; 2024-2029 (US$ Million) 4.2 Europe Alternative Data Market: An Analysis 4.2.1 Europe Alternative Data Market: An Overview 4.2.2 Europe Alternative Data Market by Value Table 60: Europe Alternative Data Market by Value; 2019-2023 (US$ Million) Table 61: Europe Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.2.3 Europe Alternative Data Market by Region (The UK, Germany, France, Italy, and Rest of the Europe) Table 62: Europe Alternative Data Market by Region; 2023 (Percentage, %)4.2.4 The UK Alternative Data Market by Value 4.2.4 The UK Alternative Data Market by Value Table 63: The UK Alternative Data Market by Value; 2019-2023 (US$ Million) Table 64: The UK Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.2.5 Germany Alternative Data Market by Value Table 65: Germany Alternative Data Market by Value; 2019-2023 (US$ Million) Table 66: Germany Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.2.6 France Alternative Data Market by Value Table 67: France Alternative Data Market by Value; 2019-2023 (US$ Million) Table 68: France Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.2.7 Italy Alternative Data Market by Value Table 69: Italy Alternative Data Market by Value; 2019-2023 (US$ Million) Table 70: Italy Alternative Data Market by Value; 2024-2029 (US$ Million) 4.2.8 Rest of Europe Alternative Data Market by Value Table 71: Rest of Europe Alternative Data Market by Value; 2019-2023 (US$ Million) Table 72: Rest of Europe Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.3 Asia Pacific Alternative Data Market: An Analysis 4.3.1 Asia Pacific Alternative Data Market: An Overview 4.3.2 Asia Pacific Alternative Data Market by Value Table 73: Asia Pacific Alternative Data Market by Value; 2019-2023 (US$ Million) Table 74: Asia Pacific Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.3.3 Asia Pacific Alternative Data Market by Region (China, Japan, India and, Rest of the Asia Pacific) Table 75: Asia Pacific Alternative Data Market by Region; 2023 (Percentage, %) 4.3.4 China Alternative Data Market by Value Table 76: China Alternative Data Market by Value; 2019-2023 (US$ Million) Table 77: China Alternative Data Market by Value; 2024-2029 (US$ Million) 4.3.5 Japan Alternative Data Market by Value Table 78: Japan Alternative Data Market by Value; 2019-2023 (US$ Million) Table 79: Japan Alternative Data Market by Value; 2024-2029 (US$ Million) 4.3.6 India Alternative Data Market by Value Table 80: India Alternative Data Market by Value; 2019-2023 (US$ Million) Table 81: India Alternative Data Market by Value; 2024-2029 (US$ Million) 4.3.7 Rest of Asia Pacific Alternative Data Market by Value Table 82: Rest of Asia Pacific Alternative Data Market by Value; 2019-2023 (US$ Million) Table 83: Rest of Asia Pacific Alternative Data Market by Value; 2024-2029 (US$ Million) 4.4 Middle East and Africa Alternative Data Market: An Analysis 4.4.1 Middle East and Africa Alternative Data Market: An Overview 4.4.2 Middle East and Africa Alternative Data Market by Value Table 84: Middle East and Africa Alternative Data Market by Value; 2019-2023 (US$ Million) Table 85: Middle East and Africa Alternative Data Market by Value; 2024-2029 (US$ Billion) 4.5 Latin America Alternative Data Market: An Analysis 4.5.1 Latin America Alternative Data Market: An Overview 4.5.2 Latin America Alternative Data Market by Value Table 86: Latin America Alternative Data Market by Value; 2019-2023 (US$ Million) Table 87: Latin America Alternative Data Market by Value; 2024-2029 (US$ Million) 5. Impact of COVID-19 5.1 Impact of COVID-19 on Alternative Data Market 5.2 Post COVID-19 Impact on Alternative Data Market 6. Market Dynamics 6.1 Growth Drivers 6.1.1 Proliferation of Credit and Debit Card Transaction Data Providers 6.1.2 Rising Demand From Hedge Funds Managers 6.1.3 Growing Digitalization 6.1.4 Surge in Data-Driven Decision-Making 6.1.5 Government Initiatives and Regulatory Environment 6.1.6 Integration of Unstructured Data 6.1.7 Globalization of Markets 6.1.8 Real-Time Insights 6.1.9 Increasing Penetration in Automotive Sector 6.1.10 Rising BFSI Industry 6.1.11 Rising Demand from Institutional and Retail Investors 6.2 Challenges 6.2.1 Lack Of Expert Personnel 6.2.2 Lack Of Data Accuracy 6.2.3 Data Privacy and Compliance Issues 6.3 Market Trends 6.3.1 Internet Penetration and 5G Adoption Table 88: Global Population Coverage by 5G Technology; 2023 and 2029 (Percentage, %) 6.3.2 Penetration of Internet of Things (IoT) Table 89: Global Active IoT Connections (Installed Base); 2015-2027 (Billion) 6.3.3 Advancements in AI, ML, and NLP Table 90: Global Artificial Intelligence Revenue; 2023-2030 (US$ Billion) 6.3.4 Expansion of Emerging Data Sources 6.3.5 Emergence of Generative AI 6.3.6 Growing Interest In Stock Market Trading 6.3.7 Rise of ESG Alternative Data 7. Competitive Landscape 7.1 Global Alternative Data Market Players: Competitive Landscape 8. Company Profiles 8.1 Factset Research Systems Inc. 8.1.1 Business Overview 8.1.2 Operating Segments Table 91: Factset Research Systems Inc. Revenues by Segment; 2023 (Percentage, %) 8.1.3 Business Strategy 8.2 Jefferies Financial Group Inc.( M Science) 8.2.1 Business Overview 8.2.2 Operating Segments Table 92: Jefferies Financial Group Inc. Net Revenues by Segment; 2023 (Percentage, %) 8.2.3 Business Strategy 8.3 London Stock Exchange Group Plc (LSEG Data & Analytics) 8.3.1 Business Overview 8.3.2 Operating Segments Table 93: London Stock Exchange Group Plc Revenue by Segment; 2023 (Percentage, %) 8.3.3 Business Strategy 8.4 Nasdaq, Inc. 8.4.1 Business Overview 8.4.2 Operating Segments Table 94: Nasdaq, Inc. Total Revenues by Segment; 2022 (Percentage, %) 8.4.3 Business Strategy 8.5 SymphonyAI, LLC (1010data, Inc.) 8.5.1 Business Overview 8.5.2 Business Strategy 8.6 Thinknum Alternative Data 8.6.1 Business Overview 8.6.2 Business Strategy 8.7 Dataminr, Inc. 8.7.1 Business Overview 8.7.2 Business Strategy 8.8 RavenPack International SL 8.8.1 Business Overview 8.8.2 Business Strategy 8.9 Preqin Ltd. 8.9.1 Business Overview 8.9.2 Business Strategy 8.10 Advan Research Corporation 8.10.1 Business Overview 8.10.2 Business Strategies 8.11 Eagle Alpha Ltd. 8.11.1 Business Overview 8.12 Earnest Analytics 8.12.1 Business Overview 8.13 YipitData Inc. 8.13.1 Business Overview

Alternative Data Market Forecasts to 2030 – Global Analysis By Data Type (Satellite & Weather Data, Web Scraped Data, Mobile Application Usage, Credit & Debit Card Transactions, Email Receipts, Geo-location (Foot Traffic) Records, Social & Sentiment Data, Web Traffic and Other Data Types), Technology, Application, End User and By Geography

Global alternative data market size study & forecast, by type (credit and debit card transactions, email receipts, geo-location records, mobile application usage, satellite and weather data, others), by industry (automotive, bfsi, energy, others), by end user (hedge fund operators, investment institutions, retail companies, others) and regional analysis, 2023-2030, alternative data market by type (credit & debit card transactions, email receipts, geo-location (foot traffic) records), industry (automotive, bfsi, energy) - global forecast 2024-2030, europe alternative data market forecast to 2030 - regional analysis - by data type [credit and debit card transactions, email receipts, geo-location (foot traffic) records, mobile application usage, satellite and weather data, and others] and industry (automotive, bfsi, energy industrial, it and telecommunications, media and entertainment, and others), middle east & africa alternative data market forecast to 2030 - regional analysis - by data type [credit and debit card transactions, email receipts, geo-location (foot traffic) records, mobile application usage, satellite and weather data, and others] and industry (automotive, bfsi, energy industrial, it and telecommunications, media and entertainment, and others), south & central america alternative data market forecast to 2030 - regional analysis - by data type [credit and debit card transactions, email receipts, geo-location (foot traffic) records, mobile application usage, satellite and weather data, and others] and industry (automotive, bfsi, energy industrial, it and telecommunications, media and entertainment, and others), research assistance.

Learn how to effectively navigate the market research process to help guide your organization on the journey to success.

Other tasks

• Printer format
• Order by fax
• Currency converter

Request a Free Sample

Samples provide examples of tables, charts, and topics included in this report. Your sample will be emailed to the address provided.

MarketResearch.com, Inc. is committed to protecting and respecting your privacy, and we’ll only use your personal information to administer your account and to provide the products and services you requested from us. From time to time, we would like to contact you about our products and services, as well as other content that may be of interest to you. If you consent to us contacting you for this purpose, please tick below to say how you would like us to contact you:

You can unsubscribe from these communications at any time. For more information on how to unsubscribe, our privacy practices, and how we are committed to protecting and respecting your privacy, please review our Privacy Policy.

By clicking submit below, you consent to allow MarketResearch.com, Inc. to store and process the personal information submitted above to provide you the content requested.

CoinMarketCap: Read what our contributors have to say. This content is provided by the community. DYOR!

Exploring Market Sentiment Indicators: Analysis Techniques And Varieties

Table of contents.

Crypto News

By Thecoinrepublic.com

See the latest updates from across the crypto universe

Thecoinrepublic.com

Other articles published on apr 30, 2024, new crypto sensation: raboo soars in presale as floki and bonk prices dip.

So far, 2024 has been a successful year for meme coins. From the beginning of the year, projects like FLOKI and BONK have performed better than most cryptos. This tilt to more meme-centric tokens s...

By CaptainAltcoin

Crypto Wars Ignite as Forbes Classifies Popular Blockchains as Zombie Chains, Lark Davis Advocate...

#Forbes reveals list of top 20 #blockchains that will one day go obsolete and #LarkDavis shares his take on the matter. #Crypto #ZombieChains Read More The post Crypto Wars Ignite as Forbes Classif...

By Crypto News Land

Hong Kong Spot Bitcoin And Ethereum ETFs Chalk Up $6.3M In Volume During Debut Morning Session

Hong Kong’s Ethereum and Bitcoin ETFs (exchange-traded funds) recorded over HK$49.4 million ($6.3 million) in trading volume during the morning session of their Apr. 30 debut on Hong Kong Exchanges...

By TheBitTimes

LBank Emerges as Premier Ecosystem Partner at Digital Dreamland: The Premier NFT & GameFi Thailan...

The future of technology integration how users benefit from aigata, ripple expands reach in japan: partners with hashkey dx for xrpl solutions.

In a significant move that underscores Ripple Labs‘ global expansion strategy, the popular American-based payment firm and Hashkey DX – a subsidiary of Hashkey Group have formed a strategic allianc...

By Bitcoinist

Join the thousands already learning crypto!

Join our free newsletter for daily crypto updates!

Benzinga (Video)

Bitcoin Plunges to $57,700 Amid Risk-Off Sentiment Ahead of Fed Rate Decision

Posted: May 1, 2024 | Last updated: May 1, 2024

Bitcoin fell to around $57,700 in early trading on Wednesday, as the crypto market dropped around 9%. The declines came amid risk-off sentiment ahead of the Fed's interest rate decision later today. Ether dropped below $3,000 while altcoins like Dogecoin and Solana recorded sizable losses. Bitcoin saw its worst monthly performance in April since November 2022. Analysis from 10x Research estimates further downside pressure toward $52,000 based on outflows from the U.S. spot exchange-traded funds.

More for You

Democrats win a New York special election, further narrowing the House GOP's majority

I’m a psychologist and mom—here are 3 phrases I wish more parents would say to their kids

A Little Girl Complained Of ‘Monsters’ In Her Bedroom, But It Was Actually 50,000 Bees

Alvin Bragg Comes Up Against 'The Biggest Problem' in Trump Case

Map reveals best places to live in the US if nuclear war breaks out

I’m a Bank Teller: 3 Times You Should Never Ask For $100 Bills at the Bank

The 20 best Disney villains

When is the student loan cancellation deadline and what do you need to do?

I became a millionaire at age 27—here are 4 'unpopular' rules rich people follow that most don't

Aileen Cannon Hands Jack Smith a Win

The 38 Most Valuable Toys From Your Childhood That Are Worth a Lot of Money Now

15 “As Seen On TV” Products That Are Well Worth The Money

7 Things To Know If You Withdraw More Than $10,000 From Your Checking Account

Kelce Brothers update, Mahomes & Coors Light drop ‘Dad Bod’ t-shirt for charity

Duane Eddy, 'the first rock 'n' roll guitar god', dies at 86

5 Most Powerful Japanese Motorcycle Engine In Production Today

Millions of Americans May Get Their Medicare Advantage Benefits Cut

Here Are The 12 Things Your Kids Really Want After You’re Gone

10 Countries To Live Outside the US That Are So Cheap You Could Quit Your Job

The Tattooist of Auschwitz Is Difficult But Necessary Viewing

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Employee Exit Interviews
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Market Research
• Artificial Intelligence
• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Sydney.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Survey Analysis & Reporting
• Sentiment Analysis

Try Qualtrics for free

What is sentiment analysis and how can users leverage it.

20 min read From survey results and customer reviews to social media mentions and chat conversations, today’s businesses have access to data from numerous sources. But how can teams turn all of that data into meaningful insights? Find out how sentiment analysis can help.

When it comes to branding, simply having a great product or service is not enough.  In order to determine the true impact of a brand, organisations must leverage data from across customer feedback channels to fully understand the market perception of their offerings.

Quantitative feedback available via metrics such as net promoter scores can provide some information about brand performance, but qualitative feedback in the form of unstructured data provides more nuanced insight into how people actually “feel” about your brand .

Sifting through textual data, however, can be extremely time-consuming. Whether analysing solicited feedback via channels such as surveys or examining unsolicited feedback found on social media, online forums, and more, it’s impossible to comprehensively identify and integrate data on brand sentiment when relying solely on manual processes.

Leveraging an omnichannel analytics platform allows teams to collect all of this information and aggregate it into a complete view. Once obtained, there are many ways to analyse and enrich the data, one of which involves conducting sentiment analysis. Sentiment analysis can be used to improve customer experience through direct and indirect interactions with your brand. Let’s consider the definition of sentiment analysis, how it works and when to use it.

Capture your customer feedback with a free Qualtrics account

What is sentiment analysis?

Sentiment refers to the positivity or negativity expressed in text. Sentiment analysis provides an effective way to evaluate written or spoken language to determine if the expression is favourable, unfavourable, or neutral, and to what degree. Because of this, it gives a useful indication of how the customer felt about their experience.

If you’ve ever left an online review, made a comment about a brand or product online, or answered a large-scale market research survey , there’s a chance your responses have been through sentiment analysis.

Sentiment analysis is part of the greater umbrella of text mining, also known as text analysis . This type of analysis extracts meaning from many sources of text, such as surveys , reviews, public social media, and even articles on the Web. A score is then assigned to each clause based on the sentiment expressed in the text. For example, -1 for negative sentiment and +1 for positive sentiment. This is done using natural language processing (NLP).

Today’s algorithm-based sentiment analysis tools can handle huge volumes of customer feedback consistently and accurately. A type of text analysis, sentiment analysis, reveals how positive or negative customers feel about topics ranging from your products and services to your location, your advertisements, or even your competitors.

Automatically uncover trends, problems, and opportunities within a single platform

What is sentiment analysis used for?

Accurate sentiment analysis can be difficult to conduct, what’s the benefit? Why do we use an AI-powered tool to categorise natural language feedback rather than our human brains?

Mostly, it’s a question of scale. Sentiment analysis is helpful when you have a large volume of text-based information that you need to generalise from.

For example, let’s say you work on the marketing team at a major motion picture studio, and you just released a trailer for a movie that got a huge volume of comments on Twitter.

You can read some – or even a lot – of the comments, but you won’t be able to get an accurate picture of how many people liked or disliked it unless you look at every last one and make a note of whether it was positive, negative or neutral. That would be prohibitively expensive and time-consuming, and the results would be prone to a degree of human error.

On top of that, you’d have a risk of bias coming from the person or people going through the comments. They might have certain views or perceptions that colour the way they interpret the data, and their judgment may change from time to time depending on their mood, energy levels, and other normal human variations.

On the other hand, sentiment analysis tools provide a comprehensive, consistent overall verdict with a simple button press.

From there, it’s up to the business to determine how they’ll put that sentiment into action .

Why is sentiment analysis important?

Sentiment analysis is critical because it helps provide insight into how customers perceive your brand .

Customer feedback – whether that’s via social media, the website, conversations with service agents, or any other source – contains a treasure trove of useful business information, but it isn’t enough to know what customers are talking about. Knowing how they feel will give you the most insight into how their experience was. Sentiment analysis is one way to understand those experiences.

Sometimes known as “opinion mining,” sentiment analysis can let you know if there has been a change in public opinion toward any aspect of your business. Peaks or valleys in sentiment scores give you a place to start if you want to make product improvements, train sales reps or customer care agents, or create new marketing campaigns.

Use cases for sentiment analysis

We live in a world where huge amounts of written information are produced and published every moment, thanks to the internet, news articles, social media, and digital communications. Sentiment analysis can help companies keep track of how their brands and products are perceived, both at key moments and over a period of time.

It can also be used in market research , PR, marketing analysis, reputation management , stock analysis and financial trading, customer experience , product design, and many more fields.

Here are a few scenarios where sentiment analysis can save time and add value:

• Social media listening – in day-to-day monitoring, or around a specific event such as a product launch
• Analysing survey responses for a large-scale research program
• Processing employee feedback in a large organisation
• Identifying very unhappy customers so you can offer closed-loop follow up
• See where sentiment trends are clustered in particular groups or regions
• Competitor research – checking your approval levels against comparable businesses

Types of sentiment analysis

Not all sentiment analysis is done the same way. There are different ways to approach it and a range of different algorithms and processes that can be used to do the job depending on the context of use and the desired outcome.

Basic sub-types of sentiment analysis include:

• Detecting sentiment This means parsing through text and sorting opinionated data (such as “I love this!”) from objective data (like “the restaurant is located downtown”).
• Categorising sentiment This means detecting whether the sentiment is positive, negative, or neutral. Your tools may also add weighting to these categories, e.g very positive, positive, neutral, somewhat negative, negative.
• Clause-level Analysis Sometimes, the text contains mixed or ambivalent opinions, for example, “staff was very friendly but we waited too long to be served”. Being able to score feedback at the clause level indicates when there are both good and bad opinions expressed in one place , and can be useful in case the positives and negatives within a text cancel each other out and return a misleading neutral sentiment

In addition, you can choose whether to view the results of sentiment analysis at:

• Document-level (useful for professional reviews or press coverage)
• Sentence level (for short comments and evaluations)
• Sub-sentence level (for picking out the meaning in phrases or short clauses within a sentence)

Pros and cons of using a sentiment analysis system

Sentiment analysis is a powerful tool that offers a number of advantages, but like any research method, it has some limitations.

Advantages of sentiment analysis:

• Accurate, unbiased results
• Enhanced insights
• More time and energy available for staff do to higher-level tasks
• Consistent measures you can use to track sentiment over time

Disadvantages of sentiment analysis:

• Best for large and numerous data sets. To get real value out of sentiment analysis tools, you need to be analysing large quantities of textual data on a regular basis.
• Sentiment analysis is still a developing field, and the results are not always perfect. You may still need to sense-check and manually correct results occasionally.

How does sentiment analysis work?

Sentiment analysis uses machine learning, statistics, and natural language processing (NLP) to find out how people think and feel on a macro scale. Sentiment analysis tools take written content and process it to unearth the positivity or negativity of the expression.

This is done in a couple of ways:

• Rule-based sentiment analysis This method uses a lexicon, or word-list, where each word is given a score for sentiment, for example “great” = 0.9, “lame” = -0.7, “okay” = 0.1 Sentences are assessed for overall positivity or negativity using these weightings. Rule-based systems usually require additional finessing to account for sarcasm, idioms, and other verbal anomalies.
• Machine learning-based sentiment analysis A computer model is given a training set of natural language feedback, manually tagged with sentiment labels. It learns which words and phrases have a positive sentiment or a negative sentiment. Once trained, it can then be used on new data sets.

In some cases, the best results come from combining the two methods.

Sentiment analysis challenges

Developing sentiment analysis tools is technically an impressive feat, since human language is grammatically intricate, heavily context-dependent, and varies a lot from person to person. If you say “I loved it,” another person might say “I’ve never seen better,” or “Leaves its rivals in the dust”. The challenge for an AI tool is to recognise that all these sentences mean the same thing.

Another challenge is to decide how language is interpreted since this is very subjective and varies between individuals. What sounds positive to one person might sound negative or even neutral to someone else. In designing algorithms for sentiment analysis, data scientists must think creatively in order to build useful and reliable tools.

Getting the correct sentiment classification

Sentiment classification requires your sentiment analysis tools to be sophisticated enough to understand not only when a data snippet is positive or negative, but how to extrapolate sentiment even when both positive and negative words are used. On top of that, it needs to be able to understand context and complications such as sarcasm or irony.

Human beings are complicated, and how we express ourselves can be similarly complex. Many types of sentiment analysis tools use a simple view of polarity (positive/neutral/negative), which means much of the meaning behind the data is lost.

Let’s see an example:

“I hated the setup process, but the product was easy to use so in the end, I think my purchase was worth it.”

A less sophisticated sentiment analysis tool might see the sentiment expressed here as “neutral” because the positive – “the product was easy to use so, in the end, I think my purchase was worth it” – and negative-tagged sentiments – “I hated the setup process” – cancel each other out.

However, polarity isn’t so cut-and-dry as being one or the other here. The final part – “in the end, I think my purchase was worth it” – means that as a human analysing the text, we can see that generally, this customer felt mostly positive about the experience. That’s why a scale from positive to negative is needed, and why a sentiment analysis tool adds weighting along a scale of 1-11.

Scores are assigned with attention to grammar, context, industry, and source, and Qualtrics gives users the ability to adjust the sentiment scores to be even more business-specific.

Better understand your customers with real time sentiment analysis

Understanding context

Context is key for a sentiment analysis model to be correct. This means you need to make sure that your sentiment scoring tool not only knows that “happy” is positive—and that “not happy” is not, but understands that certain words that are context-dependent are viewed correctly.

As human beings, we know customers are pleased when they mention how “thin” their new laptop is, but that they’re complaining when they talk about the “thin” walls in your hotel. We understand that context.

Obviously, a tool that flags “thin” as negative sentiment in all circumstances is going to lose accuracy in its sentiment scores. The context is important.

This is where training natural language processing (NLP) algorithms come in. Natural language processing is a way of mimicking the human understanding of language, meaning context becomes more readily understood by your sentiment analysis tool.

Sentiment analysis algorithms are trained using this system over time, using deep learning to understand instances with context and apply that learning to future data. This is why a sophisticated sentiment analysis tool can help you to not only analyse vast volumes of data more quickly but also discern what context is common or important to your customers .

Three places to analyse customer sentiment

In a world of endless opinions on the Web, how people “feel” about your brand can be important for measuring the customer experience .

Consumers desire likable brands that understand them; brands that provide memorable on-and-offline experiences. The more in-tune a consumer feels with your brand, the more likely they’ll share feedback, and the more likely they’ll buy from you too. According to our Consumer trends research , 62% of consumers said that businesses need to care more about them, and 60% would buy more as a result.

But the opposite is true as well. As a matter of fact, 71 percent of Twitter users will take to the social media platform to voice their frustrations with a brand.

These conversations, both positive and negative, should be captured and analysed to improve the customer experience. Sentiment analysis can help.

1. Text analysis for surveys

Surveys are a great way to connect with customers directly, and they’re also ripe with constructive feedback . The feedback within survey responses can be quickly analysed for sentiment scores.

For the survey itself, consider questions that will generate qualitative customer experience metrics, some examples include:

• What was your most recent experience like?
• How much better (or worse) was your experience compared to your expectations?
• What is something you would have changed about your experience?

Remember, the goal here is to acquire honest textual responses from your customers so the sentiment within them can be analysed. Another tip is to avoid close-ended questions that only generate “yes” or “no” responses. These types of questions won’t serve your analysis well.

Next, use a text analysis tool to break down the nuances of the responses. TextiQ is a tool that will not only provide sentiment scores but extract key themes from the responses.

After the sentiment is scored from survey responses, you’ll be able to address some of the more immediate concerns your customers have during their experiences.

Another great place to find text feedback is through customer reviews .

2. Text analysis for customer reviews

Did you know that 72 percent of customers will not take action until they’ve read reviews on a product or service? An astonishing 95 percent of customers read reviews prior to making a purchase. In today’s feedback-driven world, the power of customer reviews and peer insight is undeniable.

Review sites like G2 are common first-stops for customers looking for honest feedback on products and services. This feedback, like that in surveys, can be analysed.

The benefit of customer reviews compared to surveys is that they’re unsolicited, which often leads to more honest and in-depth feedback.

To improve the customer experience, you can take the sentiment scores from customer reviews – positive, negative, and neutral – and identify gaps and pain points that may have not been addressed in the surveys. Remember, negative feedback is just as (if not more) beneficial to your business than positive feedback.

3. Text analysis for social media

Another way to acquire textual data is through social media analysis.

Monitoring tools ingest publicly available social media data on platforms such as Twitter and Facebook for brand mentions and assign sentiment scores accordingly. This has its upsides as well considering users are highly likely to take their uninhibited feedback to social media.

Regardless, a staggering 70 percent of brands don’t bother with feedback on social media. Because social media is an ocean of big data just waiting to be analysed, brands could be missing out on some important information.

Free eBook:  The Secret to Digital Experience? Think like a Human

Sentiment analysis tools

When choosing sentiment analysis technologies, bear in mind how you will use them. There are a number of options out there, from open-source solutions to in-built features within social listening tools. Some of them are limited in scope, while others are more powerful but require a high level of user knowledge.

Text iQ is a natural language processing tool within the Experience Management Platform™ that allows you to carry out sentiment analysis online using just your browser. It’s fully integrated, meaning that you can view and analyse your sentiment analysis results in the context of other data and metrics, including those from third-party platforms.

Like all our tools, it’s designed to be straightforward, clear, and accessible to those without specialised skills or experience, so there’s no barrier between you and the results you want to achieve.

Analysing customer sentiment, creating better experiences

When it comes to understanding the customer experience, the key is to always be on the lookout for customer feedback. Sentiment analysis is not a one-and-done effort and requires continuous monitoring. By reviewing your customers’ feedback on your business regularly, you can proactively get ahead of emerging trends and fix problems before it’s too late.  Acquiring feedback and analysing sentiment can provide businesses with a deep understanding of how customers truly “feel” about their brand. When you’re able to understand your customers, you’re able to provide a more robust customer experience.

Automatically uncover trends, problems and opportunities with TextiQ

Related resources

Analysis & Reporting

Report on Survey Findings 3 min read

Thematic analysis 11 min read, predictive analytics 19 min read, statistical significance calculator: tool & complete guide 18 min read, data analysis 32 min read, regression analysis 19 min read, social media analytics 13 min read, request demo.

Ready to learn more about Qualtrics?

KPMG Personalisation

• Mid-Market Pre-Budget Pulse Check 2024

Prior to the 2024 Australian Federal budget, KPMG surveyed private, mid-market and family businesses to gauge economic sentiment and budget concerns.

• Share Share close
• Download Mid-Market Pre-Budget Pulse Check 2024 pdf Opens in a new window
• 1000 Save this article to my library
• Go to bottom of page
• Home ›
• Insights ›

Key findings | Current and emerging perceptions |  Download report  |  Research methodology  

The annual pre-Budget survey of our mid-market client base by KPMG Enterprise this year took place just two months before the Federal Budget which will be handed down on Tuesday, 14 May 2024.

Our survey of 100 leaders in mid-tier Australian businesses – ranging from listed companies just outside the ASX 200, to private companies and family businesses – reveals a mood of cautious optimism with more than a half treading carefully and suggesting growth of around 2-3% over the next 12 months, but with a doubling of that figure over the following two years.

The two top challenges for Mid-Market businesses in the next 1-3 years remains like prior years as cost and margin pressures and the recruitment and retention of skilled staff. 

The survey themes highlight the shift in perceptions and the current and future challenges and opportunities faced by Mid-Market businesses in Australia, including economic conditions, business growth forecasts, wage expectations. Current and emerging perceptions include the impact of cost of living, tax reforms, AI implementation, work environment changes, and Superannuation Legislation.

Mid Market Pre-Budget Pulse Check 2024

Survey findings of mid-market, private and family businesses ahead of the Federal Budget announcement.

(PDF 391KB)

Key shifts in perception since 2022/23

Mid-market business forecasting and growth.

Optimism about business growth has remained relatively stable among Mid-Market businesses, with 80% forecasting growth over the next few years. The level of pessimism has remained stable in the last 12 months with 17% of businesses who are not forecasting any growth for this year and the near future.

Wage growth expectations

There has been a continued decrease in expected wage growth above 4% in the last 2 years. Most businesses are expecting a moderate increase in wages of 2-4% per cent.

Challenges for mid-market businesses

The two top challenges for Mid-Market businesses in the next 1-3 years remains like prior years as cost and margin pressures and the recruitment and retention of skilled staff. There are significant sector differences in the challenges that are most impacting businesses.

Current and emerging perceptions in 2024

Impact of cost of living.

Majority of Mid-Market businesses believe the cost of living is challenging their ability to grow the business and generate demand. More than half feel it's having a moderate impact, while for nearly a quarter, it's having a significant impact.

Tax reforms and labour training

Close to half of Mid-Market businesses believe sector growth can be boosted by major tax reforms and greater investment in labour training programs.

Work environment changes

Most businesses (59%) do not anticipate any change in working arrangements while 41% will look to increase presence in the office, especially for publicly listed companies with some planning to mandate a minimum of days in the office.

Artificial Intelligence (AI) implementation

The widespread rise of AI is evident in Mid-Market businesses, with 42% of businesses having implemented AI in their business for specific applications. Among those who have yet to implement AI, there is a plan by two-fifths to do so in the next 2 years.

ATO compliance activity

The ATO has increased its compliance activity in recent years with a focus on targeting the use of trusts and tax planning. This is a concern for a third of businesses, but most businesses are not concerned or do not have trusts.

Superannuation legislation

More than half (54%) of mid-market businesses are in support of this legislation, to a certain extent, that will result in an additional 15% tax on the earnings of superannuation balances above $3 million. However, 46% are unsupportive, and of those that support in part, 48% do not support taxing unrealised gains in superannuation.

Download the report

Mid-Market 2024 Pre-Budget Survey

Research methodology

KPMG Customer Intelligence  conducted a pulse check on behalf of KPMG Enterprise. This pulse check was targeted at clients who run mid-market firms, aiming to gauge sentiment around key issues before the 2024/25 Federal Budget is announced.

Australia’s mid market is often referred to as the engine room of the nation’s economy, employing nearly a quarter of all Australians, and responsible for almost 40 percent of Australia’s business revenue.

The survey was completed by more than 100 mid-market business directors and decision-makers. Most respondents were from private companies, however there were also some from publicly listed companies, family businesses and NFPs. Respondents also came from a range of industry sectors.

The survey results are a valuable indicator of Enterprise clients’ opinions concerning the current business and budget environment.

Supporting mid-market business

KPMG has the breadth of services and depth of knowledge to provide your business with the support it needs to navigate these evolving times, and understand how this year’s Federal Budget may affect your organisation.

Please get in touch if you have any questions. 

Federal Budget Analysis Subscription

Sign up to be the first to receive KPMG's 2024 Federal Budget analysis – direct to your inbox.

Related insights

A specialist division of KPMG Australia dedicated to advising emerging, private, family and mid-market businesses.

KPMG Enterprise is dedicated to advising emerging, private and mid-market businesses.

The 2024 Federal Budget will be delivered on Tuesday 14 May 2024. Subscribe to KPMG for the latest insights into impacts on Australian businesses.

Insights into the economic impact of the 2024 Australian Federal Budget.