movie reviews csv

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imdb_reviews

• Description :

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

Additional Documentation : Explore on Papers With Code north_east

Homepage : http://ai.stanford.edu/~amaas/data/sentiment/

Source code : tfds.datasets.imdb_reviews.Builder

• 1.0.0 (default): New split API ( https://tensorflow.org/datasets/splits )

Download size : 80.23 MiB

Auto-cached ( documentation ): Yes

Supervised keys (See as_supervised doc ): ('text', 'label')

Figure ( tfds.show_examples ): Not supported.

imdb_reviews/plain_text (default config)

Config description : Plain text

Dataset size : 129.83 MiB

Feature structure :

• Feature documentation :
• Examples ( tfds.as_dataframe ):

imdb_reviews/bytes

Config description : Uses byte-level text encoding with tfds.deprecated.text.ByteTextEncoder

Dataset size : 129.88 MiB

imdb_reviews/subwords8k

Config description : Uses tfds.deprecated.text.SubwordTextEncoder with 8k vocab size

Dataset size : 54.72 MiB

imdb_reviews/subwords32k

Config description : Uses tfds.deprecated.text.SubwordTextEncoder with 32k vocab size

Dataset size : 50.33 MiB

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2022-12-10 UTC.

Datasets: rotten_tomatoes like 42

Dataset card for "rotten_tomatoes", dataset summary.

Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005.

Supported Tasks and Leaderboards

More Information Needed

Dataset Structure

Data instances.

• Size of downloaded dataset files: 0.49 MB
• Size of the generated dataset: 1.34 MB
• Total amount of disk used: 1.84 MB

An example of 'validation' looks as follows.

Data Fields

The data fields are the same among all splits.

• text : a string feature.
• label : a classification label, with possible values including neg (0), pos (1).

Data Splits

Reads Rotten Tomatoes sentences and splits into 80% train, 10% validation, and 10% test, as is the practice set out in

Jinfeng Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world Applications.''

Dataset Creation

Curation rationale, source data, initial data collection and normalization, who are the source language producers, annotations, annotation process, who are the annotators, personal and sensitive information, considerations for using the data, social impact of dataset, discussion of biases, other known limitations, additional information, dataset curators, licensing information, citation information, contributions.

Thanks to @thomwolf , @jxmorris12 for adding this dataset.

Models trained or fine-tuned on rotten_tomatoes

Sileod/deberta-v3-base-tasksource-nli, sileod/deberta-v3-small-tasksource-nli, sileod/deberta-v3-large-tasksource-nli.

Hazqeel/electra-small-finetuned-sst2-rotten_tomatoes-distilled

Pig4431/rtm_distilbert_5e, rjzauner/distilbert_rotten_tomatoes_sentiment_classifier.

Movie Review Data

Sentiment polarity datasets.

• polarity dataset v2.0 ( 3.0Mb) (includes README v2.0 ): 1000 positive and 1000 negative processed reviews. Introduced in Pang/Lee ACL 2004. Released June 2004.
• Pool of 27886 unprocessed html files (81.1Mb) from which the polarity dataset v2.0 was derived. (This file is identical to movie.zip from data release v1.0.)
• sentence polarity dataset v1.0 (includes sentence polarity dataset README v1.0 : 5331 positive and 5331 negative processed sentences / snippets. Introduced in Pang/Lee ACL 2005. Released July 2005.
• polarity dataset v1.1 (2.2Mb) (includes README.1.1 ): approximately 700 positive and 700 negative processed reviews. Released November 2002. This alternative version was created by Nathan Treloar , who removed a few non-English/incomplete reviews and changing some of the labels (judging some polarities to be different from the original author's rating). The complete list of changes made to v1.1 can be found in diff.txt .
• polarity dataset v0.9 (2.8Mb) (includes a README ):. 700 positive and 700 negative processed reviews. Introduced in Pang/Lee/Vaithyanathan EMNLP 2002. Released July 2002. Please read the "Rating Information - WARNING" section of the README.
• movie.zip (81.1Mb) : all html files we collected from the IMDb archive.

Sentiment scale datasets

• Sep 30, 2009: Yanir Seroussi points out that due to some misformatting in the raw html files, six reviews are misattributed to Dennis Schwartz (29411 should be Max Messier, 29412 should be Norm Schrager, 29418 should be Steve Rhodes, 29419 should be Blake French, 29420 should be Pete Croatto, 29422 should be Rachel Gordon) and one (23982) is blank.

Subjectivity datasets

• subjectivity dataset v1.0 (508K) (includes subjectivity README v1.0 ): 5000 subjective and 5000 objective processed sentences. Introduced in Pang/Lee ACL 2004. Released June 2004.
• Pool of unprocessed source documents (9.3Mb) from which the sentences in the subjectivity dataset v1.0 were extracted. Note : On April 2, 2012, we replaced the original gzipped tarball with one in which the subjective files are now in the correct directory (so that the subjectivity directory is no longer empty; the subjective files were mistakenly placed in the wrong directory, although distinguishable by their different naming scheme).

If you have any questions or comments regarding this site, please send email to Bo Pang or Lillian Lee .

IMDB movie review sentiment classification dataset

Load_data function.

Loads the IMDB dataset .

This is a dataset of 25,000 movies reviews from IMDB, labeled by sentiment (positive/negative). Reviews have been preprocessed, and each review is encoded as a list of word indexes (integers). For convenience, words are indexed by overall frequency in the dataset, so that for instance the integer "3" encodes the 3rd most frequent word in the data. This allows for quick filtering operations such as: "only consider the top 10,000 most common words, but eliminate the top 20 most common words".

As a convention, "0" does not stand for a specific word, but instead is used to encode the pad token.

• path : where to cache the data (relative to ~/.keras/dataset ).
• num_words : integer or None. Words are ranked by how often they occur (in the training set) and only the num_words most frequent words are kept. Any less frequent word will appear as oov_char value in the sequence data. If None, all words are kept. Defaults to None .
• skip_top : skip the top N most frequently occurring words (which may not be informative). These words will appear as oov_char value in the dataset. When 0, no words are skipped. Defaults to 0 .
• maxlen : int or None. Maximum sequence length. Any longer sequence will be truncated. None, means no truncation. Defaults to None .
• seed : int. Seed for reproducible data shuffling.
• start_char : int. The start of a sequence will be marked with this character. 0 is usually the padding character. Defaults to 1 .
• oov_char : int. The out-of-vocabulary character. Words that were cut out because of the num_words or skip_top limits will be replaced with this character.
• index_from : int. Index actual words with this index and higher.
• Tuple of Numpy arrays : (x_train, y_train), (x_test, y_test) .

x_train , x_test : lists of sequences, which are lists of indexes (integers). If the num_words argument was specific, the maximum possible index value is num_words - 1 . If the maxlen argument was specified, the largest possible sequence length is maxlen .

y_train , y_test : lists of integer labels (1 or 0).

Note : The 'out of vocabulary' character is only used for words that were present in the training set but are not included because they're not making the num_words cut here. Words that were not seen in the training set but are in the test set have simply been skipped.

get_word_index function

Retrieves a dict mapping words to their index in the IMDB dataset.

The word index dictionary. Keys are word strings, values are their index.

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The benchmarks section lists all benchmarks using a given dataset or any of its variants. We use variants to distinguish between results evaluated on slightly different versions of the same dataset. For example, ImageNet 32⨉32 and ImageNet 64⨉64 are variants of the ImageNet dataset.

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Mr (mr movie reviews).

MR Movie Reviews is a dataset for use in sentiment-analysis experiments. Available are collections of movie-review documents labeled with respect to their overall sentiment polarity (positive or negative) or subjective rating (e.g., "two and a half stars") and sentences labeled with respect to their subjectivity status (subjective or objective) or polarity.

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13 Best Movie data sets for Machine Learning Projects

July 21, 2021

After the year inside that was 2020, it’s safe to say that just about all of us are film buffs. That’s why we at iMerit have compiled this list of movie data sets for machine learning for the film buffs among us. These data sets are perfect for anyone looking to experiment and master basic machine learning concepts, and are decidedly more interesting than the typical data set one might leverage in such an endeavor.

Build your own proprietary movie dataset. Get a quote for an end-to-end data solution to your specific requirements.

The data that’s most useful for machine learning purposes contained within these data sets include cast and crew member information, script, plot, screen time, reviews, and more. Each of these can be leveraged for different machine learning purposes including natural language processing, sentiment analysis, and more. 

Here are our iMerit’s top 13 movie data sets for machine learning basics.

Movie data sets for Machine Learning

IMDB Reviews : Ideal for sentiment analysis, this movie data set contains 5,000 movie reviews. The data set has a perfect 10 review in terms of usability by the nearly 7,000 people who’ve downloaded it, making it a perfect data set to test with.

IMDB Film Reviews data set : Designed for binary sentiment classification, this movie data set contains a substantial sum of data than the previous IMDB entry on this list. The data set contains 25,000 highly polar movie reviews for training with another 25,000 for testing. It also contains some unlabeled data and raw text for those looking to cut their teeth in annotation.

MovieLens 25M data set : Collected from the MovieLens website, this movie data set contains 25 million ratings along with one million tag applications that have been applied to over 62,000 movies. 

OMDB API : This web service is a crowdsourced movie database that continuously updates with the most current movies. It contains content and images for various films including over 280,000 posters.

Film data set from UCI : Containing over 10,000 films, this movie data set was donated back in 1997 to the University of California, Irvine. It contains information around casting, roles, actors, writers, producers, cinematographers, remakes, and studios involved. 

Cornell Film Review Data : Featuring movie-review data that’s perfect for anyone looking to conduct sentiment-analysis experiments, this body of data contains over 220,000 conversations between 10,000+ pairs of movie characters. 

Full MovieLens data set on Kaggle : This movie data set contains metadata for the 45,000 films that are listed on the Full MovieLens Dataset. Information contained within pertains to films released on or before July 2017 that focuses on cast, crew, plot keywords, budget, revenue, posters, release dates, languages, production companies, countries, TMDB vote counts and vote averages. It also contains 26 million ratings from over 270,000 users for every film.

French National Cinema Center data sets : This data set focuses exclusively on french films gathered by the CNC (Centre National du Cinema) and features 33 data sets around movie attendance, television demand, cinematographic practices and establishments, blockbuster films, and more.

Linguistic Data of 32k Film Subtitles with IMBDb Meta-Data : Linguistic data from more than 32,000 films with all meta-data matched to word-count categories from subtitle files.

Movie Industry : This data repository includes 6820 movies (220 movies per year between 1986 and 2016). The following attributes have been intimately detailed from each film: budget, company, year, writer, star, cotes, score, runtime, reviews, release date, rating, name, gross, genre, director, and country. 

Indian Movie Theaters : This data set features intimate knowledge surrounding Indian theaters and their corresponding theatre capacities, screen sizes, average ticket prices, and local coordinates.

Movie Body Counts : This data set contains a tally of the number of on-screen deaths, bodies, kills, and violent action across a slew of classic hollywood sci-fi, fantasy, and action films.

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Practical Machine Learning with Python pp 331–372 Cite as

Analyzing Movie Reviews Sentiment

• Dipanjan Sarkar 4 ,
• Raghav Bali 5 &
• Tushar Sharma 5  
• First Online: 22 December 2017

16k Accesses

2 Citations

In this chapter, we continue with our focus on case-study oriented chapters, where we will focus on specific real-world problems and scenarios and how we can use Machine Learning to solve them. We will cover aspects pertaining to natural language processing (NLP), text analytics, and Machine Learning in this chapter. The problem at hand is sentiment analysis or opinion mining, where we want to analyze some textual documents and predict their sentiment or opinion based on the content of these documents. Sentiment analysis is perhaps one of the most popular applications of natural language processing and text analytics with a vast number of websites, books and tutorials on this subject. Typically sentiment analysis seems to work best on subjective text, where people express opinions, feelings, and their mood. From a real-world industry standpoint, sentiment analysis is widely used to analyze corporate surveys, feedback surveys, social media data, and reviews for movies, places, commodities, and many more. The idea is to analyze and understand the reactions of people toward a specific entity and take insightful actions based on their sentiment.

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Authors and affiliations.

Embassy Paragon, Site No. 6/2 & 6/3, Intel Technology India Pvt Ltd Embassy Paragon, Site No. 6/2 & 6/3, Bangalore, Karnataka, India

Dipanjan Sarkar

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Raghav Bali & Tushar Sharma

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© 2018 Dipanjan Sarkar, Raghav Bali and Tushar Sharma

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Sarkar, D., Bali, R., Sharma, T. (2018). Analyzing Movie Reviews Sentiment. In: Practical Machine Learning with Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3207-1_7

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DOI : https://doi.org/10.1007/978-1-4842-3207-1_7

Published : 22 December 2017

Publisher Name : Apress, Berkeley, CA

Print ISBN : 978-1-4842-3206-4

Online ISBN : 978-1-4842-3207-1

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Kaggle Competition

YashvardhanDas/Amazon-Movie-Reviews-Sentiment-Analysis

Folders and files, repository files navigation, amazon movie reviews sentiment analysis.

Kaggle Competition - Develop a sentiment analysis classifier to accurately examine the associated views of customers with respect to Amazon-hosted movie ratings and reviews .

File descriptions

1,697,533 unique reviews from Amazon Movie Reviews, with their associated star ratings and metadata. It is not necessary to use all reviews, or metadata for training. Some reviews will be missing a value in the 'Score' column. That is because, these are the scores you want to predict.

Contains a table with 300,000 unique reviews. The format of the table has two columns; i) 'Id': contains an id that corresponds to a review in train.csv for which you predict a score ii) 'Score': the values for this column are missing since it will include the score predictions. You are required to predict the star ratings of these Id using the metadata in train.csv. sample.csv - a sample submission file. The 'Id' field is populated with values from test.csv. Kaggle will only evaluate submission files in this exact same format.

Data fields

ProductId - unique identifier for the product

UserId - unique identifier for the user

HelpfulnessNumerator - number of users who found the review helpful

HelpfulnessDenominator - number of users who indicated whether they found the review helpful

Score - rating between 1 and 5

Time - timestamp for the review

Summary - brief summary of the review

Text - text of the review

Id - a unique identifier associated with a review

Note: Some of the rows of the table may have some of these values missing.

Dataset Citation

J. McAuley and J. Leskovec. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW, 2013

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