movie review dataset

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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.

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 .

Datasets: ajaykarthick / imdb-movie-reviews like 4

Imdb movie reviews.

This is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks.

The dataset is downloaded from

https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz

This data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews.

Equal distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels.

Citation Information

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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IMDB Dataset of 50K Movie Reviews

nijatmammadov/review-classification-nlp

Folders and files, repository files navigation.

About Dataset IMDB dataset having 50K movie reviews for natural language processing or Text analytics. 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. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.

• Jupyter Notebook 100.0%

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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Sentiment Classification on the Large Movie Review Dataset

Data mining project, bert sentiment classification.

• Monticone Pietro
• Moroni Claudio
• Orsenigo Davide

Problem: Sentiment Classification

A sentiment classification problem consists, roughly speaking, in detecting a piece of text and predicting if the author likes or dislikes what he/she is talking about: the input X is a piece of text and the output Y is the sentiment we want to predict, such as the rating of a movie review.

If we can train a model to map X to Y based on a labelled dataset then it can be used to predict sentiment of a reviewer after watching a movie.

Data: Large Movie Review Dataset v1.0

The dataset contains movie reviews along with their associated binary sentiment polarity labels.

• The core dataset contains 50,000 reviews split evenly into 25k train and 25k test sets.
• The overall distribution of labels is balanced (25k pos and 25k neg).
• 50,000 unlabeled documents for unsupervised learning are included, but they won’t be used.
• The train and test sets contain a disjoint set of movies, so no significant performance is obtained by memorizing movie-unique terms and their associated with observed labels.
• In the labeled train/test sets, a negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. Thus reviews with more neutral ratings are not included in the train/test sets.
• In the unsupervised set, reviews of any rating are included and there are an even number of reviews > 5 and ≤ 5.

Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis . The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).

Theoretical introduction

The encoder-decoder sequence.

Roughly speaking, an encoder-decoder sequence is an ordered collection of steps ( coders ) designed to automatically translate sentences from a language to another (e.g. the English “the pen is on the table” into the Italian “la penna è sul tavolo”), which could be useful to visualize as follows: input sentence → ( encoders ) → ( decoders ) → output/translated sentence .

For our practical purpose, encoders and decoders are effectively indistinguishable (that’s why we will call them coders ): both are composed of two layers: a LSTM or GRU neural network and an attention module (AM) . They only differ in the way in which their output is processed.

LSTM or GRU neural network

Both the input and the output of an LSTM/GRU neural network consists of two vectors:

• the hidden state : the representation of what the network has learnt about the sentence it’s reading;
• the prediction : the representation of what the network predicts (e.g. translation).

Each word in the English input sentence is translated into its word embedding vector (WEV) before being processed by the first coder (e.g. with word2vec ). The WEV of the first word of the sentence and a random hidden state are processed by the first coder of the sequence. Regarding the output: the prediction is ignored, while the hidden state and the WEV of the second word are passed as input into the second coder and so on to the last word of the sentence. Therefore in this phase the coders work as encoders .

At the end of the sequence of N encoders (N being the number of words in the input sentence), the decoding phase begins:

• the last hidden state and the WEV of the “START” token are passed to the first decoder ;
• the decoder outputs a hidden state and a prection;
• the hidden state and the prediction are passed to the second decoder;
• the second decoder outputs a new hidden state and the second word of the translated/output sentence

and so on up until the whole sentence has been translated, namely when a decoder of the sequence outputs the WEV of the “END” token. Then there is an external mechanism to convert prediction vectors into real words, so it’s very importance to notice that the only purpose of decoders is to predict the next word .

Attention module (AM)

The attention module is a further layer that is placed before the network which provides the collection of words of the sentence with a relational structure. Let’s consider the word “table” in the sentence used as an exampe above. Because of the AM, the encoder will weight the preposition “on” (processed by the previous encoder) more than the article “the” which refers to the subject “cat”.

Bidirectional Encoder Representations from Transformers (BERT)

Transformer.

The transformer is a coder endowed with the AM layer. Transformers have been observed to work much better than the basic encoder-decoder sequences.

BERT is a sequence of encoder-type transformers which was pre-trained to predict a word or sentence (i.e. used as decoder). The benefit of improved performance of Transformers comes at a cost: the loss of bidirectionality , which is the ability to predict both next word and the previous one. BERT is the solution to this problem, a Tranformer which preserves biderectionality .

The first token is not “START”. In order to use BERT as a pre-trained language model for sentence-classification, we need to input the BERT prediction of “CLS” into a linear regression because

• the model has been trained to predict the next sentence, not just the next word;
• the semantic information of the sentence is encoded in the prediction output of “CLS” as a document vector of 512 elements.

• bert_final_data
• https://www.kaggle.com/dataset/5f1193b4685a6e3aa8b72fa3fdc427d18c3568c66734d60cf8f79f2607551a38
• https://www.kaggle.com/dataset/9850d2e4b7d095e2b723457263fbef547437b159e3eb7ed6dc2e88c7869fca0b
• Bert-For-Tf2
• Google github repository
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• A Visual Guide to Using BERT for the First Time
• Machine Translation(Encoder-Decoder Model)!
• The Illustarted Tranformers
• The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
• BERT Explained: State of the art language model for NLP
• Learning Word Vectors for Sentiment Analysis .

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Movie reviews (movie review polarity dataset enriched with "annotator rationales").

This dataset is based on the movie review polarity dataset (v2.0) collected and maintained by Bo Pang and Lillian Lee. Their dataset (we'll call it PL2.0) consists of 1000 positive and 1000 negative movie reviews obtained from the Internet Movie Database (IMDb) review archive.

The main contribution of this release is the enrichment of the documents with "annotator rationales," a concept we describe in our NAACL HLT 2007 paper.

Basically, "rationales" are segments of the text that support an annotator's classification. Let's say we have a movie review that is labeled as positive (i.e. the writer has a favorable opinion of the movie). Then the rationales would be segments of the text that support the claim (by an annotator) that the review is, indeed, positive.

Here are some examples of positive rationales (the segments enclosed by double square brackets):

• [[you will enjoy the hell out of]] American Pie.
• fortunately, they [[managed to do it in an interesting and funny way]].
• he is [[one of the most exciting martial artists on the big screen]], continuing to perform his own stunts and [[dazzling audiences]] with his flashy kicks and punches.
• the romance was [[enchanting]].

And here are some examples of negative rationales:

• A woman in peril. A confrontation. An explosion. The end. [[Yawn. Yawn. Yawn.]]
• when a film makes watching Eddie Murphy [[a tedious experience, you know something is terribly wrong]].
• the movie is [[so badly put together]] that even the most casual viewer may notice the [[miserable pacing and stray plot threads]].
• [[don't go see]] this movie

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Computer Science > Artificial Intelligence

Title: detecting spoilers in movie reviews with external movie knowledge and user networks.

Abstract: Online movie review platforms are providing crowdsourced feedback for the film industry and the general public, while spoiler reviews greatly compromise user experience. Although preliminary research efforts were made to automatically identify spoilers, they merely focus on the review content itself, while robust spoiler detection requires putting the review into the context of facts and knowledge regarding movies, user behavior on film review platforms, and more. In light of these challenges, we first curate a large-scale network-based spoiler detection dataset LCS and a comprehensive and up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View Spoiler Detection framework that takes into account the external knowledge about movies and user activities on movie review platforms. Specifically, MVSD constructs three interconnecting heterogeneous information networks to model diverse data sources and their multi-view attributes, while we design and employ a novel heterogeneous graph neural network architecture for spoiler detection as node-level classification. Extensive experiments demonstrate that MVSD advances the state-of-the-art on two spoiler detection datasets, while the introduction of external knowledge and user interactions help ground robust spoiler detection. Our data and code are available at this https URL

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IMDb Charts

Imdb top 250 movies.

1. The Shawshank Redemption

2. The Godfather

3. The Dark Knight

4. The Godfather Part II

5. 12 Angry Men

6. Schindler's List

7. The Lord of the Rings: The Return of the King

8. Pulp Fiction

9. The Lord of the Rings: The Fellowship of the Ring

10. The Good, the Bad and the Ugly

11. Forrest Gump

12. The Lord of the Rings: The Two Towers

13. Fight Club

14. Inception

15. Star Wars: Episode V - The Empire Strikes Back

16. The Matrix

17. Goodfellas

18. One Flew Over the Cuckoo's Nest

20. Interstellar

21. It's a Wonderful Life

22. Seven Samurai

23. The Silence of the Lambs

24. Saving Private Ryan

25. Dune: Part Two

26. City of God

27. Life Is Beautiful

28. The Green Mile

29. Terminator 2: Judgment Day

30. Star Wars: Episode IV - A New Hope

31. Back to the Future

32. Spirited Away

33. The Pianist

34. Parasite

36. Spider-Man: Across the Spider-Verse

37. Gladiator

38. The Lion King

39. The Departed

40. Léon: The Professional

41. American History X

42. Whiplash

43. The Prestige

44. Grave of the Fireflies

45. Harakiri

46. The Usual Suspects

47. Casablanca

48. The Intouchables

49. Cinema Paradiso

50. Modern Times

51. Rear Window

52. Once Upon a Time in the West

54. City Lights

55. Django Unchained

56. Apocalypse Now

57. Memento

58. 12th Fail

60. Raiders of the Lost Ark

61. The Lives of Others

62. Sunset Boulevard

63. Avengers: Infinity War

64. Paths of Glory

65. Spider-Man: Into the Spider-Verse

66. Witness for the Prosecution

67. The Shining

68. The Great Dictator

70. Inglourious Basterds

71. The Dark Knight Rises

72. Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb

73. American Beauty

76. Amadeus

77. Toy Story

78. Das Boot

79. Avengers: Endgame

80. Braveheart

81. Good Will Hunting

82. Princess Mononoke

84. Your Name.

85. High and Low

86. Once Upon a Time in America

87. 3 Idiots

88. Singin' in the Rain

89. Capernaum

90. Come and See

91. Requiem for a Dream

92. Toy Story 3

93. Star Wars: Episode VI - Return of the Jedi

94. Eternal Sunshine of the Spotless Mind

95. The Hunt

96. 2001: A Space Odyssey

97. Oppenheimer

98. Reservoir Dogs

100. Lawrence of Arabia

101. The Apartment

102. North by Northwest

103. Incendies

104. Citizen Kane

106. Vertigo

107. Double Indemnity

108. Scarface

109. Full Metal Jacket

110. Amélie

112. A Clockwork Orange

114. To Kill a Mockingbird

115. A Separation

116. The Sting

117. Indiana Jones and the Last Crusade

118. Die Hard

119. Metropolis

120. Like Stars on Earth

121. Snatch

122. Hamilton

123. L.A. Confidential

125. Bicycle Thieves

126. Taxi Driver

127. Downfall

128. Dangal

129. For a Few Dollars More

130. Batman Begins

131. The Wolf of Wall Street

132. Some Like It Hot

133. Green Book

134. The Kid

135. The Father

136. Judgment at Nuremberg

137. The Truman Show

138. All About Eve

139. Top Gun: Maverick

140. Shutter Island

141. There Will Be Blood

142. Casino

143. Jurassic Park

145. The Sixth Sense

146. Pan's Labyrinth

147. Unforgiven

148. No Country for Old Men

149. A Beautiful Mind

150. The Thing

151. Kill Bill: Vol. 1

152. The Treasure of the Sierra Madre

153. Yojimbo

154. Monty Python and the Holy Grail

155. The Great Escape

156. Finding Nemo

157. Prisoners

158. Rashomon

159. Howl's Moving Castle

160. The Elephant Man

161. Chinatown

162. Dial M for Murder

163. Gone with the Wind

164. V for Vendetta

165. Lock, Stock and Two Smoking Barrels

166. The Secret in Their Eyes

167. Inside Out

168. Raging Bull

169. Three Billboards Outside Ebbing, Missouri

170. Trainspotting

171. The Bridge on the River Kwai

174. Spider-Man: No Way Home

175. Catch Me If You Can

176. Warrior

177. Gran Torino

178. My Neighbor Totoro

179. Million Dollar Baby

180. Harry Potter and the Deathly Hallows: Part 2

181. Children of Heaven

182. 12 Years a Slave

183. Blade Runner

184. Before Sunrise

185. Ben-Hur

186. Barry Lyndon

187. The Grand Budapest Hotel

188. Gone Girl

189. Hacksaw Ridge

190. The Gold Rush

191. Memories of Murder

192. In the Name of the Father

193. Dead Poets Society

194. Mad Max: Fury Road

195. Wild Tales

196. The Deer Hunter

197. The General

198. On the Waterfront

199. Monsters, Inc.

200. Sherlock Jr.

202. How to Train Your Dragon

203. The Third Man

204. The Wages of Fear

205. Wild Strawberries

206. Mary and Max

207. Mr. Smith Goes to Washington

208. Ratatouille

209. Ford v Ferrari

210. Tokyo Story

211. The Big Lebowski

213. The Seventh Seal

216. Spotlight

217. Hotel Rwanda

218. The Terminator

219. Platoon

220. The Passion of Joan of Arc

221. La haine

222. Before Sunset

223. The Best Years of Our Lives

224. Pirates of the Caribbean: The Curse of the Black Pearl

225. The Exorcist

227. Network

228. Jai Bhim

229. Stand by Me

230. The Wizard of Oz

231. The Incredibles

232. Hachi: A Dog's Tale

233. The Handmaiden

234. Into the Wild

235. My Father and My Son

236. The Sound of Music

237. The Battle of Algiers

238. To Be or Not to Be

239. The Grapes of Wrath

240. Groundhog Day

241. Amores Perros

242. Rebecca

243. The Iron Giant

244. Cool Hand Luke

245. The Help

246. It Happened One Night

247. Aladdin

248. Drishyam

249. Dances with Wolves

250. Gangs of Wasseypur

The Top Rated Movie list only includes feature films.

• Shorts, TV movies, and documentaries are not included
• The list is ranked by a formula which includes the number of ratings each movie received from users, and value of ratings received from regular users
• To be included on the list, a movie must receive ratings from at least 25000 users

More to explore

Top box office (us), most popular movies, top rated english movies, most popular tv shows, top 250 tv shows, lowest rated movies, most popular celebs.

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