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

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.

R

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.

Context

This is the IMDB dataset exactly same as ImDb Movie Reviews Dataset, contains the movie reviews.

Content

The real dataset contains text files for training and testing purpose, but I created two csv files from those text files to ease the task ✌️ . Now you only need to download and apply your model. Each file contains 25000 reviews with label 0 for negative and 1 for positive. Each file has two columns 0 and 1, 0 represents reviews and 1 represents labels.

9

title.akas.csv

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"… See the full description on the dataset page: https://huggingface.co/datasets/labofsahil/IMDb-Dataset.

Context

IMDB Dataset of top 10000 movies. It contains the basic details that are updated in IMDB

Content

• movie-title - Title of the movie
• year - year in which the movie released
• genre - The style or category of the movie
• synopsis - The basic plot of the movie available in IMDB
• cast - List of people involved in the movie

Description:

• The dataset comprises anonymized data on movies available on IMDb, capturing various aspects such as genre, rating, and revenue.
• This dataset can be utilized for analyzing movie trends, audience preferences, and the impact of different attributes like genre and director on movie success.
• It can aid in understanding the factors contributing to high ratings and box office revenue, as well as providing insights into the popularity of genres over time.
• This dataset can be utilized for analyzing movie success factors, audience preferences, and genre trends.
• It can help in identifying the relationship between movie features (e.g., genre, director) and ratings or revenue, examining the popularity of actors and directors, and understanding critical reception through Metascore.

Features:

This dataset provides valuable insights derived from an analysis of IMDB movie data, specifically tailored to inform strategic decision-making for film production companies. It offers a comprehensive overview of trends in movie genres, release timing, ratings, top-performing directors and actors, and potential production partners.

The analysis includes:

Monthly Production Trends: Identifies peak production months and average annual output.

Genre Popularity: Analyzes genre popularity based on quantity and average duration.

Rating Distribution: Reveals common rating ranges and target ratings for success.

High-Rated Production Houses: Highlights production houses associated with top-rated films.

Top Directors: Lists directors with a track record of successful films.

Popular Actors: Identifies popular actors with high average ratings and vote counts.

Potential Global Partners: Suggests potential global partners based on audience reach.

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 13 November 2021.

--- 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 ---

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 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).

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

"IMDb Top 250 Movies Dataset: Ultimate Movie Ranking Collection"

Description: The "IMDb Top 250 Movies Dataset: Ultimate Movie Ranking Collection" is an extensive and meticulously curated dataset, available on Kaggle, that offers a comprehensive compilation of the top 250 movies as ranked by IMDb. This dataset serves as an invaluable resource for movie enthusiasts, researchers, and data scientists seeking to explore and analyze the world of cinema.

With a wide array of attributes, the dataset provides detailed information for each movie entry. The columns include:

1. Sl_No: A unique serial number assigned to each movie in the dataset.
2. Name: The title of the movie.
3. Release_Year: The year when the movie was released.
4. Duration: The duration of the movie in minutes.
5. Certificate: The certification or rating assigned to the movie.
6. Rating: The IMDb rating of the movie, reflecting its overall user-generated score.
7. Votes: The number of votes received by the movie on IMDb.
8. Director: The name of the director(s) of the movie.
9. Stars: The main actors or actresses who starred in the movie.
10. Description: A brief summary or description of the movie.

This dataset empowers users to conduct in-depth analyses, extract meaningful insights, and unravel patterns within the IMDb Top 250 movies. It enables researchers to explore correlations between attributes such as ratings, votes, and duration. Furthermore, it facilitates the examination of the impact of directors, the popularity of stars, and the influence of certification on movie rankings.

Whether you're interested in studying the evolution of film ratings over time, identifying common themes or genres among top-rated movies, or analyzing the characteristics of acclaimed directors and actors, the "IMDb Top 250 Movies Dataset: Ultimate Movie Ranking Collection" provides a robust foundation for comprehensive research, predictive modeling, and captivating visualizations.

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.

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

The MovieLens-IMDB dataset is a collection of user ratings for movies, with each rating indicating the user's preference for the movie.