imdb movie reviews dataset csv

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Imdb movie reviews.

The IMDb Movie Reviews dataset is a binary sentiment analysis dataset consisting of 50,000 reviews from the Internet Movie Database (IMDb) labeled as positive or negative. The dataset contains an even number of positive and negative reviews. Only highly polarizing reviews are considered. A negative review has a score ≤ 4 out of 10, and a positive review has a score ≥ 7 out of 10. No more than 30 reviews are included per movie. The dataset contains additional unlabeled data.

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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.

IMDb Non-Commercial Datasets

Subsets of IMDb data are available for access to customers for personal and non-commercial use. You can hold local copies of this data, and it is subject to our terms and conditions. Please refer to the Non-Commercial Licensing and copyright/license and verify compliance.

As of March 18, 2024 the datasets on this page are backed by a new data source. There has been no change in location or schema, but if you encounter issues with the datasets following the March 18th update, please contact [email protected].

Data Location

The dataset files can be accessed and downloaded from https://datasets.imdbws.com/ . The data is refreshed daily.

IMDb Dataset Details

Each dataset is contained in a gzipped, tab-separated-values (TSV) formatted file in the UTF-8 character set. The first line in each file contains headers that describe what is in each column. A ‘\N’ is used to denote that a particular field is missing or null for that title/name. The available datasets are as follows:

title.akas.tsv.gz

• titleId (string) - a tconst, an alphanumeric unique identifier of the title
• ordering (integer) – a number to uniquely identify rows for a given titleId
• title (string) – the localized title
• region (string) - the region for this version of the title
• language (string) - the language of the title
• types (array) - Enumerated set of attributes for this alternative title. One or more of the following: "alternative", "dvd", "festival", "tv", "video", "working", "original", "imdbDisplay". New values may be added in the future without warning
• attributes (array) - Additional terms to describe this alternative title, not enumerated
• isOriginalTitle (boolean) – 0: not original title; 1: original title

title.basics.tsv.gz

• tconst (string) - alphanumeric unique identifier of the title
• titleType (string) – the type/format of the title (e.g. movie, short, tvseries, tvepisode, video, etc)
• primaryTitle (string) – the more popular title / the title used by the filmmakers on promotional materials at the point of release
• originalTitle (string) - original title, in the original language
• isAdult (boolean) - 0: non-adult title; 1: adult title
• startYear (YYYY) – represents the release year of a title. In the case of TV Series, it is the series start year
• endYear (YYYY) – TV Series end year. ‘\N’ for all other title types
• runtimeMinutes – primary runtime of the title, in minutes
• genres (string array) – includes up to three genres associated with the title

title.crew.tsv.gz

• directors (array of nconsts) - director(s) of the given title
• writers (array of nconsts) – writer(s) of the given title

title.episode.tsv.gz

• tconst (string) - alphanumeric identifier of episode
• parentTconst (string) - alphanumeric identifier of the parent TV Series
• seasonNumber (integer) – season number the episode belongs to
• episodeNumber (integer) – episode number of the tconst in the TV series

title.principals.tsv.gz

• nconst (string) - alphanumeric unique identifier of the name/person
• category (string) - the category of job that person was in
• job (string) - the specific job title if applicable, else '\N'
• characters (string) - the name of the character played if applicable, else '\N'

title.ratings.tsv.gz

• averageRating – weighted average of all the individual user ratings
• numVotes - number of votes the title has received

name.basics.tsv.gz

• primaryName (string)– name by which the person is most often credited
• birthYear – in YYYY format
• deathYear – in YYYY format if applicable, else '\N'
• primaryProfession (array of strings)– the top-3 professions of the person
• knownForTitles (array of tconsts) – titles the person is known for

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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.

Exploring the IMDB Dataset with TensorFlow: A Python Guide

💡 Problem Formulation: When working with machine learning and natural language processing, having access to a rich dataset is crucial. The IMDB dataset, which contains movie reviews for sentiment analysis, is a common starting point. The goal is to download the IMDB dataset conveniently, then process and explore it in Python using TensorFlow, transforming the raw data into a usable format for ML models. We need methods that are efficient, straightforward, and suitable for downstream tasks like sentiment analysis.

Method 1: TensorFlow Datasets API

The TensorFlow Datasets API is a collection of datasets ready to use with TensorFlow. It encapsulates fetching, parsing, and preparing the data into a format that’s easy to use with TensorFlow models. For the IMDB dataset, the API provides utilities to download and preprocess the data, including tokenizing and encoding the reviews.

Here’s an example:

This code snippet utilizes the tfds.load function to download the IMDB dataset and prepares it for training and testing. By specifying the split argument, you can control which subset of the data to load. The example iterates over the first item returning a dictionary with text and label pairs.

Method 2: Keras IMDB Dataset Utility

Keras, which is now part of TensorFlow’s core API, has a module for loading the IMDB dataset that is more tailored to neural network training. It allows you to specify the number of words to use, and it automatically tokenizes and encodes the text data.

This code snippet calls the imdb.load_data() method to fetch the IMDB dataset. By setting the num_words parameter, the data will be limited to the top 10,000 most frequent words. The output is a sequence of word indices representing the words of the first movie review.

Method 3: Manual Download and Parsing

If you want maximum control over the dataset downloading and preprocessing steps, you can manually download the IMDB dataset and write custom parsing code. This is more complex but allows for fine-grained control over the data processing logic.

In this example, we use the requests library to download the dataset as a compressed file and then extract it using tarfile . The files are read directly from the disk, offering an opportunity to implement custom preprocessing procedures.

Method 4: TensorFlow’s TextLineDataset

For those looking to work directly with the raw text data line by line, TensorFlow’s TextLineDataset can be used to stream text from a file and is particularly useful for large text files that do not fit into memory.

This snippet demonstrates how to use TensorFlow’s TextLineDataset to read lines of text from a file. This line-by-line approach is memory-efficient and handy for large datasets, ensuring that the whole dataset does not need to be loaded into memory.

Bonus One-Liner Method 5: pandas and TensorFlow

For quick exploration and prototyping, you can combine the strengths of pandas and TensorFlow. This method takes advantage of pandas for initial dataset loading and manipulation, and TensorFlow for later processing and model training.

In this example, we create a pandas DataFrame from a CSV version of the IMDb dataset and then convert it into a TensorFlow Dataset, which can be used for model training and evaluation.

Summary/Discussion

• Method 1: TensorFlow Datasets API. Strengths: Simplifies the process, handling most of the heavy lifting. Weaknesses: Less flexibility in data preprocessing.
• Method 2: Keras IMDB Dataset Utility. Strengths: Integrated with Keras, making it straightforward for neural networks training. Weaknesses: The fixed preprocessing may not be suitable for all projects.
• Method 3: Manual Download and Parsing. Strengths: Full control over the preprocessing steps. Weaknesses: More complex and time-consuming.
• Method 4: TensorFlow’s TextLineDataset. Strengths: Efficient memory use, reads files line by line. Weaknesses: Less straightforward for advanced preprocessing techniques.
• Bonus One-Liner Method 5: pandas and TensorFlow. Strengths: Combines the ease of use of pandas with the TensorFlow modeling capabilities. Weaknesses: May not scale well for very large datasets.

Emily Rosemary Collins is a tech enthusiast with a strong background in computer science, always staying up-to-date with the latest trends and innovations. Apart from her love for technology, Emily enjoys exploring the great outdoors, participating in local community events, and dedicating her free time to painting and photography. Her interests and passion for personal growth make her an engaging conversationalist and a reliable source of knowledge in the ever-evolving world of technology.

Datasets: scikit-learn / imdb like 0

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IMDB Large Movie Review Dataset

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).

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

Character, path to directory where data will be stored. If NULL , user_cache_dir will be used to determine path.

Character. Return training ("train") data or testing ("test") data. Defaults to "train".

Logical, set TRUE to delete dataset.

Logical, set TRUE to return the path of the dataset.

Logical, set TRUE to remove intermediate files. This can greatly reduce the size. Defaults to FALSE.

Logical, set TRUE if you have manually downloaded the file and placed it in the folder designated by running this function with return_path = TRUE .

A tibble with 25,000 rows and 2 variables:

Character, denoting the sentiment

Character, text of the review

In the entire collection, no more than 30 reviews are allowed for any given movie because reviews for the same movie tend to have correlated ratings. Further, 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.

When using this dataset, please cite the ACL 2011 paper

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} }

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Here are 347 public repositories matching this topic..., cmasch / cnn-text-classification.

Text classification with Convolution Neural Networks on Yelp, IMDB & sentence polarity dataset v1.0

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Fetch movie data from IMDB and output in JSON format.

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Builds a Microsoft SQL Server 2016+ relational database from IMDb official data files, to support personal querying.

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mohdahmad242 / Transfer-Learning-Model-hosted-on-Heroku-using-React-Flask

Transfer Learning model using RoBERTa on IMDb dataset deployed on React and Flask ( Regional Winner in Facebook Developer Community Challenge 2020 )

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advaitsave / Text-preprocessing-and-classification

IMDB Movie Reviews - Text preprocessing and classification. Includes BOW model, TF_IDF, VADER entiment analysis, Topic Modelling using Latent Dirichlet Allocation and Word Embeddings. (Python)

• Updated Mar 26, 2019

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Scrape Data From IMDB Movie DataBase

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Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer

• Original Article
• Published: 16 April 2024
• Volume 14 , article number  87 , ( 2024 )

Cite this article

• Mian Muhammad Danyal 1 , 2   na1 ,
• Sarwar Shah Khan 3 , 4 ,
• Muzammil Khan 3   na1 ,
• Subhan Ullah 2   na1 ,
• Muhammad Bilal Ghaffar 2   na1 &
• Wahab Khan 2   na1  

71 Accesses

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Movies have been important in our lives for many years. Movies provide entertainment, inspire, educate, and offer an escape from reality. Movie reviews help us choose better movies, but reading them all can be time-consuming and overwhelming. To make it easier, sentiment analysis can classify movie reviews into positive and negative categories. Opinion mining (OP), called sentiment analysis (SA), uses natural language processing to identify and extract opinions expressed through text. Naive Bayes, a supervised learning algorithm, offers simplicity, efficiency, and strong performance in classification tasks due to its feature independence assumption. This study evaluates the performance of four Naïve Bayes variations using two vectorization techniques, Count Vectorizer and Term Frequency–Inverse Document Frequency (TF–IDF), on two movie review datasets: IMDb Movie Reviews Dataset and Rotten Tomatoes Movie Reviews. Bernoulli Naive Bayes achieved the highest accuracy using Count Vectorizer on the IMDB and Rotten Tomatoes datasets. Multinomial Naive Bayes, on the other hand, achieved better accuracy on the IMDB dataset with TF–IDF. During preprocessing, we implemented different techniques to enhance the quality of our datasets. These included data cleaning, spelling correction, fixing chat words, lemmatization, and removing stop words. Additionally, we fine-tuned our models through hyperparameter tuning to achieve optimal results. Using TF–IDF, we observed a slight performance improvement compared to using the count vectorizer. The experiment highlights the significant role of sentiment analysis in understanding the attitudes and emotions expressed in movie reviews. By predicting the sentiments of each review and calculating the average sentiment of all reviews, it becomes possible to make an accurate prediction about a movie’s overall performance.

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Sentiment Analysis of IMDb Movie Reviews: A Comparative Analysis of Feature Selection and Feature Extraction Techniques

Sentiment Analysis through Word Vectors: A Study on Movie Reviews from IMDb

Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database

Data availibility statement.

The data that support the findings of this study are openly available through the Open Science Framework at https://github.com/Ankit152/IMDB-sentiment-analysis.git and https://www.kaggle.com/datasets/talha002/rottentomatoes-400k-review

Abbreviations

Aspect-based sentiment analysis

Artificial intelligence

Bag-of-words

Bernoulli Naive Bayes

Complement Naive Bayes

Cross-validation

Deep learning

Gaussian Naive Bayes

Grid search

Internet movie database

K-Nearest Neighbours

Support vector machines

Machine learning

Multinomial Naive Bayes

• Naive Bayes

Natural language processing

Natural language tool kit

Opinion mining

Rotten Tomatoes

True Positive

True Negative

False Positive

False Negative

• Sentiment analysis

Term Frequency–Inverse Document Frequency

Word to vector

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Acknowledgements

We sincerely thank everyone who helped us finish this research paper. We are grateful to the participants for their helpful feedback and ideas, which improved our research methods and the quality of our results. We appreciate everyone who gave their time to join our study, as this research wouldn’t have been possible without them. Thank you to everyone who took the time to contribute to this research paper.

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Mian Muhammad Danyal, Muzammil Khan, Subhan Ullah, Muhammad Bilal Ghaffar, Wahab Khan have contributed equally to this work.

Authors and Affiliations

Center for Excellence in Information Technology, Institute of Management Sciences, Peshawar, 24720, Pakistan

Mian Muhammad Danyal

Department of Computer Science, City University of Science and Information Technology, Peshawar, 25000, Pakistan

Mian Muhammad Danyal, Subhan Ullah, Muhammad Bilal Ghaffar & Wahab Khan

Department of Computer and Software Technology, University of Swat, Swat, 19130, Pakistan

Sarwar Shah Khan & Muzammil Khan

Department of Computer Science, Iqra University Swat Campus, Swat, 19130, Pakistan

Sarwar Shah Khan

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Contributions

The author contributions are as follow: “Conceptualization, MMD and SSK; methodology, MBG and MK; software, MMD, SU; validation, SSK and WK; formal analysis, MK, WK, and MBG; investigation, SU; data curation, SU and SSK; writing-original draft preparation, MMD, and MBG; writing-review and editing, SSK; visualization, MBG, and MK.

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Correspondence to Muzammil Khan .

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Danyal, M.M., Khan, S.S., Khan, M. et al. Sentiment analysis of movie reviews based on NB approaches using TF–IDF and count vectorizer. Soc. Netw. Anal. Min. 14 , 87 (2024). https://doi.org/10.1007/s13278-024-01250-9

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Received : 02 April 2023

Revised : 16 March 2024

Accepted : 20 March 2024

Published : 16 April 2024

DOI : https://doi.org/10.1007/s13278-024-01250-9

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