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. For more dataset information, please go through the following link, http://ai.stanford.edu/~amaas/data/sentiment/

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.

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.

This dataset contains nearly 1 Million unique movie reviews from 1150 different IMDb movies spread across 17 IMDb genres - Action, Adventure, Animation, Biography, Comedy, Crime, Drama, Fantasy, History, Horror, Music, Mystery, Romance, Sci-Fi, Sport, Thriller and War. The dataset also contains movie metadata such as date of release of the movie, run length, IMDb rating, movie rating (PG-13, R, etc), number of IMDb raters, and number of reviews per movie.

Q-b1t/IMDB-Dataset-of-50K-Movie-Reviews-Backup dataset hosted on Hugging Face and contributed by the HF Datasets community

Context

A movie review dataset. NLP tasks Sentiment Analysis.

Note : all the movie review are long sentence(most of them are longer than 200 words.)

Content

two columns used (text : the review of the movie and label : the sentiment label of the movie review)

Dataset containing information about movies which appears on IMDB website. Data was obtained by means of a web scraping in Python and combined with repository shared by IMDB. Data was preprocessed to include only movies which were released after 1970 and currently have over 50 000 ratings. Additionally there were selected only these movies whose budgets and gross' are denominated in USD to avoid discrepancies. Dataset contains 3348 observations described by 12 attributes.

Attributes 1. id - movie's ID used by IMDB repository 2. primaryTitle - title in English 3. originalTitle - original title in native language 4. isAdult - parental guidance 5. runtimeMinutes - total runtime in minutes 6. genres - genres 7. averageRating - final rating, based on all the ratings 8. numVotes - total number of votes (ratings) 9. budget - total budget in USD 10. gross - total gross worldwide in USD 11. release_date - release date, first occurrence 12. directors - directors

LAST UPDATE: 12th November 2023

IMDB-MULTI is a relational dataset that consists of a network of 1000 actors or actresses who played roles in movies in IMDB. A node represents an actor or actress, and an edge connects two nodes when they appear in the same movie. In IMDB-MULTI, the edges are collected from three different genres: Comedy, Romance and Sci-Fi.

Large Movie Review Dataset translated to Javanese. 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. We translated the original IMDB Dataset to Javanese using the multi-lingual MarianMT Transformer model from Helsinki-NLP/opus-mt-en-mul.

Context:

The movie industry is a vast and ever-growing landscape, with countless movies being produced each year. Keeping track of all these movies and their characteristics can be a daunting task for researchers, film enthusiasts, and data scientists alike. That's where a comprehensive dataset that lists all movies and their genre can come in handy.

Column Description:

• movie_id - IMDB Movie ID
• movie_name - Name of the movie
• year - Release year
• certificate - Certificate of the movie
• run_time - Total movie run time
• genre - Genre of the movie
• rating - Rating of the movie
• description - Description of the movie
• director - Director of the movie
• director_id - IMDB id of the director
• star - Star of the movie
• star_id - IMDB id of the star
• votes - Number of votes in IMDB website
• gross(in $) - Gross Box Office of the movie

Source:

The primary source for an IMDb all movies dataset based on genre would be IMDb, the world's most popular and authoritative source for movie, TV, and celebrity content. IMDb has an extensive database of movies that is constantly updated with new titles and information.

Inspiration:

Creating an IMDb all movies dataset based on genre can provide a wealth of insights and opportunities for analysis. For example, researchers could use the dataset to study trends in movie genres over time or compare the characteristics of different genres. Film enthusiasts could use the dataset to discover new movies in their favorite genres or explore movies outside of their usual comfort zone. Data scientists could use the dataset to build predictive models or recommend movies to users based on their genre preferences. Overall, an IMDb all movies dataset based on genre has the potential to unlock a wealth of knowledge and insights about the movie industry.

This is a dump generated by pg_dump -Fc of the IMDb data used in the "How Good are Query Optimizers, Really?" paper. PostgreSQL compatible SQL queries and scripts to automatically create a VM with this dataset can be found here: https://git.io/imdb

IMDb is an online database of information related to films, television programs, home videos, video games, and streaming content online. Crawl feeds team crawled more than 300K+ records for research and analysis purposes.

Contact crawl feeds team to customize dataset as per your needs like format changes, data frequency, and adding or removing fields.

This dataset is a work in progress. It includes data that was screen scraped using jsonlite and XML libraries in R and an open API through OMDB from the IMDB website. Movie IDs to help gather much of this data come from one or two Kaggle projects. There is a workflow from original cobbled together spreadsheets to the final product with 27 variables and over 5000 observations.

More detail on this data will be provided later in the project this data was gathered for. Stay tuned ...

IMDB-BINARY is a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres.

A dataset for binary sentiment classification containing 25,000 highly polarized movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

dvilasuero/mini-imdb dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains 4669820 ratings from 1499238 users to 351109 movies on the imdb.com website. This data is collected from reviews (https://www.imdb.com/review/rw0000001/). Each row in this dataset is as follows:userID, movieID, rating, review dateFor example : ur18238764, tt2177461, 9, 22 January 2019

IMDb-Face is large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. All images are obtained from the IMDb website.

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).