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.

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From IMDB's database, I downloaded two datasets of actors and movies. I then cleaned and merged the datasets for a combined dataset containing known actors and relevant information, including a movie they appeared in.

Dataset Card for IMDb Movie Dataset: All Movies by Genre

  Dataset Summary

This dataset is an adapted version of "IMDb Movie Dataset: All Movies by Genre" found at: https://www.kaggle.com/datasets/rajugc/imdb-movies-dataset-based-on-genre?select=history.csv. Within the dataset, the movie title and year columns were combined, the genre was extracted from the seperate csv files, the pre-existing genre column was renamed to expanded-genres, any movies missing a description… See the full description on the dataset page: https://huggingface.co/datasets/jquigl/imdb-genres.

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.

This dataset contains a list of over 6,000 top-rated titles on IMDb, including both movies and TV series, with a minimum average user rating of 7 and over 10,000 votes.

A dataset is updated daily at 10:00 AM CET. If you find this dataset helpful, feel free to give it an upvote! 😊

You can find the IMDb (Unofficial) API at this link: IMDb API on RapidAPI. This API offers access to the entire IMDb database, including detailed ratings, episode information, cast details, and much more.

All Datasets

• Full IMDb Dataset (1M+)
• IMDb Top 1000 Movies
• IMDb Top 1000 TV Series
• IMDb Top 1000 Worst Rated Titles
• "https://www.kaggle.com/datasets/octopusteam/imdb-top-rated-titles-movies-and-tv-series">IMDb Top Rated Titles (Movies & TV Series)

Dataset Card for IMDB-BINARY (IMDb-B)

  Dataset Summary

The IMDb-B dataset 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".

  Supported Tasks and Leaderboards

IMDb-B should be used for graph classification… See the full description on the dataset page: https://huggingface.co/datasets/graphs-datasets/IMDB-BINARY.

test3534/imdb dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Dataset

This dataset was created by Nodirbek Kamalov

Contents

Dataset

This dataset was created by Farshad Tofighi

Released under CC0: Public Domain

Contents

We have cleaned the noisy IMDB-WIKI dataset using a constrained clustering method, resulting this new benchmark for in-the-wild age estimation. The annotations also allow this dataset to use for some other tasks, like gender classification and face recognition/verification. For more details, please refer to our FPAge paper.

Explore the IMDB Movie Dataset to uncover trends, audience preferences, and success factors like ratings, revenue, and genres. Perfect for analysis!

Dataset

This dataset was created by ShivamYadav11321

Released under CC0: Public Domain

Contents

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).

zapsdcn/imdb dataset hosted on Hugging Face and contributed by the HF Datasets community

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 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… See the full description on the dataset page: https://huggingface.co/datasets/ajaykarthick/imdb-movie-reviews.

We proudly present you the IMDB show ontology. This is an ontology based on IMDB data and geocoded locations data for many scenes for shows which previously was not available in a single dataset. The present ontology is extensively documented in our GitHub repository: https://github.com/AlexHoorn/group51-kdd Relations are aligned with foaf and schema ontologies and every show is explicitly aligned with wikidata via a Owl:sameAs predicate.

For the contents and structure of this ontology we would kindly refer you here: https://github.com/AlexHoorn/MovieLocationsOntology

For the creation and data in this ontology we would kindly refer you here: https://github.com/AlexHoorn/MovieLocationsOntology/tree/main/data

We highly recommended you to visit our movie location app to explore this data.