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 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.

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.

"Movie Recommendation on the IMDB Dataset: A Journey into Machine Learning" is an exciting project focused on leveraging the IMDB Dataset for developing an advanced movie recommendation system. This project aims to explore the vast potential of machine learning techniques in providing personalized movie recommendations to users.

The IMDB Dataset, comprising a wealth of movie information including genres, ratings, and user reviews, serves as the foundation for this project. By harnessing the power of machine learning algorithms and data analysis, the project seeks to build a recommendation system that can accurately suggest movies tailored to each individual's preferences.

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.

Unlock one of the most comprehensive movie datasets available—4.5 million structured IMDb movie records, extracted and enriched for data science, machine learning, and entertainment research.

This dataset includes a vast collection of global movie metadata, including details on title, release year, genre, country, language, runtime, cast, directors, IMDb ratings, reviews, and synopsis. Whether you're building a recommendation engine, benchmarking trends, or training AI models, this dataset is designed to give you deep and wide access to cinematic data across decades and continents.

Perfect for use in film analytics, OTT platforms, review sentiment analysis, knowledge graphs, and LLM fine-tuning, the dataset is cleaned, normalized, and exportable in multiple formats.

What’s Included:

• Genres: Drama, Comedy, Horror, Action, Sci-Fi, Documentary, and more

• Delivery: Direct download

Use Cases:

• Train LLMs or chatbots on cinematic language and metadata

• Build or enrich movie recommendation engines

• Run cross-lingual or multi-region film analytics

• Benchmark genre popularity across time periods

• Power academic studies or entertainment dashboards

• Feed into knowledge graphs, search engines, or NLP pipelines

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

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.

Analysis of ‘IMDB Movies Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

IMDB Dataset of top 1000 movies and tv shows. You can find the EDA Process on - https://www.kaggle.com/harshitshankhdhar/eda-on-imdb-movies-dataset

Please consider UPVOTE if you found it useful.

Content

Data:- - Poster_Link - Link of the poster that imdb using - Series_Title = Name of the movie - Released_Year - Year at which that movie released - Certificate - Certificate earned by that movie - Runtime - Total runtime of the movie - Genre - Genre of the movie - IMDB_Rating - Rating of the movie at IMDB site - Overview - mini story/ summary - Meta_score - Score earned by the movie - Director - Name of the Director - Star1,Star2,Star3,Star4 - Name of the Stars - No_of_votes - Total number of votes - Gross - Money earned by that movie

Inspiration

• Analysis of the gross of a movie vs directors.
• Analysis of the gross of a movie vs different - different stars.
• Analysis of the No_of_votes of a movie vs directors.
• Analysis of the No_of_votes of a movie vs different - different stars.
• Which actor prefer which Genre more?
• Which combination of actors are getting good IMDB_Rating maximum time?
• Which combination of actors are getting good gross?

--- Original source retains full ownership of the source dataset ---

9

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.

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.

Analysis of ‘IMDB Movie Dataset Latest’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ayushjain001/imdb-movie-dataset-latest on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset is being extracted from the website imdb.com using we scrapping in python( Beautiful Soup Library).It contains 1000 rows and 10 columns.

Content

This dataset contains rating of movie based on viewers review and arranged in descending order of rating using web scrap .

Inspiration

Viewer seeing this data will have an opportunity to perform various analytics technique on data and analyze the data.

--- Original source retains full ownership of the source dataset ---

Dataset

This dataset was created by Ruturaj Marathe

Released under MIT

Contents

Dataset: IMDb Movie Reviews Description: Contains 50,000 movie reviews labeled as positive or negative. Use Case: Fine-tuning GPT for sentiment analysis or opinion mining

This dataset was scraped from the IMDB.com website. The dataset had total 12 columns.

• Movie title
• Total Run Time
• Movie Rating
• User Rating
• Genres
• Overview
• Movie's Plot Keywords
• Director Name
• Top 5 Cast's Name
• Writer Name
• Releasing year
• IMDb movie url path

User can use this dataset for their research purpose.

Context

IMDB dataset having 50K movie reviews for natural language processing or Text analytics.

Content

This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. It provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing.

Acknowledgements

http://mng.bz/0tIo

Dataset

This dataset was created by Iqra007

Released under Apache 2.0

Contents