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

  Languages… See the full description on the dataset page: https://huggingface.co/datasets/cornell-movie-review-data/rotten_tomatoes.

IMDB movie review sentiment classification dataset (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)). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/

The IMDB dataset was modified as follows to prepare it for use in a Galaxy Training Tutorial (https://training.galaxyproject.org/):

The top 50 words are excluded (mostly stop words). Included the next 10,000 top words. Reviews are limited to 500 words max (Longer reviews trimmed and shorter reviews are padded). 25,000 reviews are used for training and testing each. Files are in tsv (tab separated value) format to be consumed by Galaxy (www.usegalaxy.org).

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.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imdb_reviews', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

R

The objective of sentiment analysis for movie reviews is to automatically analyze and categorize the sentiments expressed in reviews, providing insights into audience opinions, emotions, and reactions towards films.

The dataset is a carefully selected set of Tamil film reviews with the goal of advancing NLP research in the areas of text classification, sentiment analysis, and aspect-based sentiment analysis. We have invited users to review twenty-five films using a Google form. Additional reviews were taken from websites such as IMDb and YouTube. From the list of selected aspects, we also made sure that the review collection was based on the presence of at least one target aspect, including cinematography, acting, screenplay, story, director, songs, background music, and editing. About 1,390 reviews total, tagged for positive as well as negative views across eight different categories, make up the dataset.

The global movie rating sites market is a dynamic and rapidly evolving sector, driven by the increasing consumption of online streaming services and the growing reliance on user reviews and professional critiques to inform viewing choices. The market, estimated at $2 billion in 2025, is projected to experience robust growth, fueled by factors such as the expanding reach of internet access, particularly in emerging markets, and the continued rise of mobile-first content consumption. Key market drivers include the escalating demand for credible and unbiased movie reviews to combat information overload and the need for personalized recommendations within the overwhelming variety of available content. The integration of advanced analytics and machine learning algorithms by major players further enhances the market's potential, offering more accurate and personalized recommendations to users. Segmentation within the market reveals a strong emphasis on user-generated content, reflecting the influence of peer reviews in shaping consumer decisions. However, the market also faces potential restraints such as the challenge of maintaining accuracy and impartiality in user ratings, as well as the increasing competition from social media platforms that offer informal yet influential movie discussions. The proliferation of niche movie rating platforms targeting specific genres or demographics also presents a challenge to the dominance of established players. The market's geographical distribution shows significant concentration in North America and Europe, reflecting the higher internet penetration and established movie-going culture in these regions. However, rapid growth is anticipated in Asia-Pacific regions, particularly in India and China, driven by the booming film industries and increasing smartphone usage. The competitive landscape is characterized by both established players like Rotten Tomatoes and IMDb, with significant brand recognition and extensive user bases, and emerging niche platforms targeting specific audience segments. The competitive dynamics will likely see increased investment in technology, data analytics, and marketing to attract and retain users in a crowded market. Future growth will depend heavily on the ability of platforms to adapt to evolving consumer preferences, leverage data effectively, and integrate seamlessly with other entertainment platforms. The focus on improving user experience and delivering personalized recommendations will be crucial for success.

Data Set

The labelled data set consists of 25,000 IMDB movie reviews, specially selected for sentiment analysis. The sentiment of reviews is binary, meaning the IMDB rating < 5 results in a sentiment score of "Negative", and rating >=7 have a sentiment score of "Positive." No individual movie has more than 30 reviews.

File description

MovieReviewTrainingDatabase.csv - The labelled training set. The file is comma-delimited and has a header row followed by 25,000 rows containing the sentiment and the text for each review.

Data fields

sentiment - Sentiment of the review; "Positive" for positive reviews and "Negative" for negative reviews review - Text of the review

The global movie rating sites market is experiencing robust growth, driven by the increasing consumption of online streaming services and the rising demand for credible film reviews before purchasing tickets or subscribing. The market's expansion is fueled by several factors, including the proliferation of smartphones and internet access, making it easier for users to access rating platforms. Furthermore, the integration of social media features on many platforms fosters engagement and user-generated content, creating a dynamic and interactive ecosystem. The market is segmented by application (movie promotion, movie research, audience choice, and others) and by rating type (user-based, professional-based, and others). While precise market sizing data is unavailable, given the significant presence of established players like Rotten Tomatoes and IMDb, and considering the considerable global viewership of movies, we can estimate the 2025 market size to be approximately $2 billion. This estimation accounts for advertising revenue, premium subscriptions (where applicable), and potential data licensing to film studios and distributors. The projected CAGR suggests continued substantial growth throughout the forecast period (2025-2033), likely driven by technological advancements and the ever-growing global movie-watching audience. However, potential restraints include the risk of biased reviews and the increasing competition from new platforms and emerging technologies like AI-powered recommendation systems. The North American market currently holds a significant share due to the established presence of major players and a large movie-going audience. However, rapid growth is anticipated in the Asia-Pacific region, particularly in countries like India and China, fueled by the expansion of streaming platforms and increasing internet penetration. Europe, with its diverse film culture and established digital infrastructure, also represents a substantial market segment. Competitive pressures are intensifying, with existing players continually innovating to enhance user experiences, introduce new features, and attract and retain users in a crowded market. The market's future trajectory will be shaped by the strategic moves of key players, technological disruptions, and evolving consumer preferences regarding how they discover and choose movies to watch. Strategic partnerships and acquisitions could also play a significant role in shaping the market landscape in the coming years.

This repository was created for my Master's thesis in Computational Intelligence and Internet of Things at the University of Córdoba, Spain. The purpose of this repository is to store the datasets found that were used in some of the studies that served as research material for this Master's thesis. Also, the datasets used in the experimental part of this work are included.

Below are the datasets specified, along with the details of their references, authors, and download sources.

----------- STS-Gold Dataset ----------------

The dataset consists of 2026 tweets. The file consists of 3 columns: id, polarity, and tweet. The three columns denote the unique id, polarity index of the text and the tweet text respectively.

Reference: Saif, H., Fernandez, M., He, Y., & Alani, H. (2013). Evaluation datasets for Twitter sentiment analysis: a survey and a new dataset, the STS-Gold.

File name: sts_gold_tweet.csv

----------- Amazon Sales Dataset ----------------

This dataset is having the data of 1K+ Amazon Product's Ratings and Reviews as per their details listed on the official website of Amazon. The data was scraped in the month of January 2023 from the Official Website of Amazon.

Owner: Karkavelraja J., Postgraduate student at Puducherry Technological University (Puducherry, Puducherry, India)

Features:

• product_id - Product ID
• product_name - Name of the Product
• category - Category of the Product
• discounted_price - Discounted Price of the Product
• actual_price - Actual Price of the Product
• discount_percentage - Percentage of Discount for the Product
• rating - Rating of the Product
• rating_count - Number of people who voted for the Amazon rating
• about_product - Description about the Product
• user_id - ID of the user who wrote review for the Product
• user_name - Name of the user who wrote review for the Product
• review_id - ID of the user review
• review_title - Short review
• review_content - Long review
• img_link - Image Link of the Product
• product_link - Official Website Link of the Product

License: CC BY-NC-SA 4.0

File name: amazon.csv

----------- Rotten Tomatoes Reviews Dataset ----------------

This rating inference dataset is a sentiment classification dataset, containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. On average, these reviews consist of 21 words. The first 5331 rows contains only negative samples and the last 5331 rows contain only positive samples, thus the data should be shuffled before usage.

This data is collected from https://www.cs.cornell.edu/people/pabo/movie-review-data/ as a txt file and converted into a csv file. The file consists of 2 columns: reviews and labels (1 for fresh (good) and 0 for rotten (bad)).

Reference: Bo Pang and Lillian Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 115–124, Ann Arbor, Michigan, June 2005. Association for Computational Linguistics

File name: data_rt.csv

----------- Preprocessed Dataset Sentiment Analysis ----------------

Preprocessed amazon product review data of Gen3EcoDot (Alexa) scrapped entirely from amazon.in
Stemmed and lemmatized using nltk.
Sentiment labels are generated using TextBlob polarity scores.

The file consists of 4 columns: index, review (stemmed and lemmatized review using nltk), polarity (score) and division (categorical label generated using polarity score).

DOI: 10.34740/kaggle/dsv/3877817

Citation: @misc{pradeesh arumadi_2022, title={Preprocessed Dataset Sentiment Analysis}, url={https://www.kaggle.com/dsv/3877817}, DOI={10.34740/KAGGLE/DSV/3877817}, publisher={Kaggle}, author={Pradeesh Arumadi}, year={2022} }

This dataset was used in the experimental phase of my research.

File name: EcoPreprocessed.csv

----------- Amazon Earphones Reviews ----------------

This dataset consists of a 9930 Amazon reviews, star ratings, for 10 latest (as of mid-2019) bluetooth earphone devices for learning how to train Machine for sentiment analysis.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 5 columns: ReviewTitle, ReviewBody, ReviewStar, Product and division (manually added - categorical label generated using ReviewStar score)

License: U.S. Government Works

Source: www.amazon.in

File name (original): AllProductReviews.csv (contains 14337 reviews)

File name (edited - used for my research) : AllProductReviews2.csv (contains 9930 reviews)

----------- Amazon Musical Instruments Reviews ----------------

This dataset contains 7137 comments/reviews of different musical instruments coming from Amazon.

This dataset was employed in the experimental phase of my research. To align it with the objectives of my study, certain reviews were excluded from the original dataset, and an additional column was incorporated into this dataset.

The file consists of 10 columns: reviewerID, asin (ID of the product), reviewerName, helpful (helpfulness rating of the review), reviewText, overall (rating of the product), summary (summary of the review), unixReviewTime (time of the review - unix time), reviewTime (time of the review (raw) and division (manually added - categorical label generated using overall score).

Source: http://jmcauley.ucsd.edu/data/amazon/

File name (original): Musical_instruments_reviews.csv (contains 10261 reviews)

File name (edited - used for my research) : Musical_instruments_reviews2.csv (contains 7137 reviews)

Context

The competition is over 2 yrs ago. I just wanna play around the dataset.

Content

The labeled data set consists of 50,000 IMDB movie reviews, specially selected for sentiment analysis. The sentiment of reviews is binary, meaning the IMDB rating < 5 results in a sentiment score of 0, and rating >=7 have a sentiment score of 1. No individual movie has more than 30 reviews. The 25,000 review labeled training set does not include any of the same movies as the 25,000 review test set. In addition, there are another 50,000 IMDB reviews provided without any rating labels.

• id - Unique ID of each review
• sentiment - Sentiment of the review; 1 for positive reviews and 0 for negative reviews
• review - Text of the review

Acknowledgements

The origin place is here. Awesome tutorial is here, we can play with it.

Inspiration

Just for study and learning

AlbMoRe is a sentiment analysis corpus of movie reviews in Albanian, consisting of 800 records in CSV format. Each record includes a text review retrieved from IMDb and translated in Albanian by the author. It also contains a 0 negative) or 1 (positive) label added by the author. The corpus is fully balanced, consisting of 400 positive and 400 negative reviews about 67 movies of different genres. AlbMoRe corpus is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using the data, please cite the following paper: Çano Erion. AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in Albanian. CoRR, abs/2306.08526, 2023. URL https://arxiv.org/abs/2306.08526.

AlbMoRe is a sentiment analysis corpus of movie reviews in Albanian, consisting of 800 records in CSV format. Each record includes a text review retrieved from IMDb and translated in Albanian by the author. It also contains a 0 negative) or 1 (positive) label added by the author. The corpus is fully balanced, consisting of 400 positive and 400 negative reviews about 67 movies of different genres. AlbMoRe corpus is released under CC-BY license (https://creativecommons.org/licenses/by/4.0/). If using the data, please cite the following paper: Çano Erion. AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in Albanian. CoRR, abs/2306.08526, 2023. URL https://arxiv.org/abs/2306.08526.

IMDB Movie Reviews Sentiment Dataset

This dataset contains CSV versions of the Large Movie Review dataset by Maas, et al. (2011) from its original Stanford AI Repository. It contains 50k highly polar movie reviews, evenly split to 25k positives and 25k negatives. Each sample is labeled with a 0 (positive) or 1 (negative). The additional ~11k unlabeled review data has also been included in CSV format for your convenience.

Citations

Works using this dataset must use the appropriate citations via this bibtex entry:

@InProceedings{maas-EtAl:2011:ACL-HLT2011,
 author  = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
 title   = {Learning Word Vectors for Sentiment Analysis},
 booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
 month   = {June},
 year   = {2011},
 address  = {Portland, Oregon, USA},
 publisher = {Association for Computational Linguistics},
 pages   = {142--150},
 url    = {http://www.aclweb.org/anthology/P11-1015}
}

The global movie rating sites market is experiencing robust growth, driven by the increasing popularity of streaming services, a surge in online movie consumption, and the growing reliance on user reviews and professional ratings to inform viewing decisions. The market, estimated at $2 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, the continuous evolution of user interfaces and functionalities on these platforms enhances user experience, fostering engagement and loyalty. Secondly, strategic partnerships between rating sites and streaming platforms provide cross-promotional opportunities, expanding reach and user base. Thirdly, the rising demand for data-driven insights in the film industry is driving the adoption of professional rating services within the movie research and production segments. Competition among established players like Rotten Tomatoes and IMDb, alongside the emergence of niche platforms catering to specific film genres or demographics, is shaping the market landscape. However, the market faces certain restraints. Data security and privacy concerns regarding user information are a major challenge. Maintaining the accuracy and integrity of ratings to avoid manipulation or biased reviews is also crucial for sustaining user trust. Furthermore, the market's growth is susceptible to fluctuations in the film industry itself, including production delays, changes in consumer preferences, and the impact of external economic factors. The market is segmented by application (movie promotion, movie research, audience choice, others) and type (user ratings, professional ratings, others), providing opportunities for specialized platforms to emerge and cater to specific niche needs. Geographic expansion, especially in rapidly developing markets in Asia Pacific, presents significant potential for future growth. The North American market currently holds a substantial share due to the established presence of key players and high online movie consumption.

53,400 movie reviews by the average length of 33 words were selected.

This dataset is designed for Movie Sentiment Analysis, offering a rich collection of textual movie reviews labeled with their corresponding sentiment (positive, negative, or neutral). The primary goal of this dataset is to provide a valuable resource for researchers, data scientists, and machine learning enthusiasts interested in natural language processing (NLP), sentiment analysis, and the broader field of computational social science.

The reviews included in this dataset have been meticulously collected from various online movie review platforms and public forums, ensuring a diverse range of opinions and writing styles. We've taken care to anonymize personal information, focusing solely on the textual content relevant to sentiment. The sentiment labels have been either manually annotated by expert reviewers or derived through a robust, supervised machine learning pipeline, with a focus on accuracy and inter-annotator agreement where applicable.

The inspiration behind creating this dataset stems from the growing importance of understanding public opinion, particularly in the entertainment industry. Movie studios, distributors, and filmmakers can leverage sentiment analysis to gauge audience reception, identify areas for improvement, and inform future creative decisions. Furthermore, this dataset aims to contribute to the broader NLP community by providing a ready-to-use resource for developing and benchmarking sentiment analysis models, exploring linguistic nuances in review texts, and training machine learning algorithms to classify emotional tones in written communication accurately.

Performances of sentiment analysis models on PTT movie reviews.

This is the sentiment analysis dataset based on IMDB reviews initially released by Stanford University. 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. Raw text and already processed bag of words formats are provided. See the README file contained in the release for more… See the full description on the dataset page: https://huggingface.co/datasets/scikit-learn/imdb.