Dataset

This dataset was created by Yueming

Released under Database: Open Database, Contents: Database Contents

Contents

Movie and TV coding of firearm use and rates per year of firearm homicide for ages 15-24

MansaT/Movie-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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

Discover a vast collection of movie titles and their corresponding emojis in this comprehensive dataset, spanning across popular film industries including Hollywood, Anime, Bollywood, and Kannada cinema. With a rich variety of genres and cultures represented, this dataset offers valuable insights into the intersection of movies and emojis, providing a unique perspective on the global cinematic landscape.

Explore the enchanting world of Hollywood blockbusters, delve into the captivating realm of Anime masterpieces, experience the magic of Bollywood's musical extravaganzas, and immerse yourself in the soulful narratives of Kannada cinema. This dataset covers it all, allowing you to analyze the diverse emotions and themes associated with each movie through expressive emojis.

Uncover fascinating patterns and trends as you examine the relationship between movie titles and emojis. Gain insights into the visual representation of emotions, genres, and cultural references embedded within the film industry. Whether you are an AI researcher, data scientist, movie enthusiast, or emoji aficionado, this dataset serves as a valuable resource for sentiment analysis, cultural studies, and entertainment research.

Columns

• Movies: List of Movie names- Hollywood, Anime, Bollywood and Sandalwood
• Emojis: These emojis represent the movie

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.

Movies Dataset from AllMovie is a comprehensive collection featuring over 430,000 records, encompassing a wide range of films across various genres and languages. This extensive dataset includes essential data points such as movie titles, genres, release dates, posters, languages, directors, durations, synopses, trailers, average ratings, cast information, and URLs. Such detailed metadata is invaluable for developers, researchers, and enthusiasts aiming to analyze trends, build recommendation systems, or conduct in-depth studies of the film industry.

For those interested in alternative datasets, the IMDb Non-Commercial Datasets provide subsets of IMDb data accessible for personal and non-commercial use. These datasets allow users to hold local copies of movie information, facilitating various analytical projects.

Additionally, the MovieLens datasets offer a range of movie rating data suitable for research purposes. For instance, the MovieLens 20M dataset comprises 20 million ratings and 465,000 tag applications applied to 27,000 movies by 138,000 users, making it a valuable resource for studies in user preferences and recommendation algorithms.

Incorporating these datasets into your projects can significantly enhance the quality and depth of your analyses, providing a solid foundation for exploring various aspects of the cinematic world.

Why Choose Crawl Feeds for Your Data Needs?

Crawl Feeds is your trusted partner in acquiring high-quality, curated datasets tailored to your specific requirements. With a vast repository that includes the Movies Dataset, we empower developers and businesses to drive innovation. Explore our easy-to-use platform and transform your ideas into actionable insights.

Get Started with Crawl Feeds Today

The MovieQA dataset is a dataset for movie question answering. to evaluate automatic story comprehension from both video and text. The data set consists of almost 15,000 multiple choice question answers obtained from over 400 movies and features high semantic diversity. Each question comes with a set of five highly plausible answers; only one of which is correct. The questions can be answered using multiple sources of information: movie clips, plots, subtitles, and for a subset scripts and DVS.

Data exposed: Linked Data about Movies Size of data set: 6,148,121 triples.

Openness: Open

Mixture of material from Wikipedia, Freebase and Geonames and states on http://wiki.linkedmdb.org/Main/Licensing:

Content created by (or contributed to) LinkedMDB (interlinking data, in particular) is licensed under the Creative Commons Attributions License(CC-BY). You are free to use CC-BY content as long as you provide proper attribution back to the source (LinkedMDB). Attribution should be given with a link or a reference to LinkedMDB.

The Movie dataset is a graph dataset constructed from the Movielens-2k dataset. The Movielens-2k dataset contains movies information such as actors, genres, and tags information. The feature of each movie is the tags information. The task is to predict the genres of the movies.

Dataset

This dataset was created by Samar Hendawi

Contents

Stable benchmark dataset. 1 million ratings from 6000 users on 4000 movies. Released 2/2003.

Whereas the action recognition community has focused mostly on detecting simple actions like clapping, walking or jogging, the detection of fights or in general aggressive behaviors has been comparatively less studied. Such capability may be extremely useful in some video surveillance scenarios like in prisons, psychiatric or elderly centers or even in camera phones. After an analysis of previous approaches we test the well-known Bag-of-Words framework used for action recognition in the specific problem of fight detection, along with two of the best action descriptors currently available: STIP and MoSIFT. For the purpose of evaluation and to foster research on violence detection in video we introduce a new video database containing 1000 sequences divided in two groups: fights and non-fights. Experiments on this database and another one with fights from action movies show that fights can be detected with near 90% accuracy.

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

We collected movie dataset from Internet Movie Database (IMDB) website for our experiments using an IMDbPy script to extract all the movie metadata. We obtained the box office revenues from The Movies Dataset, Box-office Mojo and The Movie Database (TMDB).These databases predominantly consisted of movies from 2006 to 2020 in various countries, and we also collected movie posters. We also used the Open Images dataset V6 for object detection of movie posters.

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 ---

This repository contains network graphs and network metadata from Moviegalaxies, a website providing network graph data from about 773 films (1915–2012). The data includes individual network graph data in Graph Exchange XML Format and descriptive statistics on measures such as clustering coefficient, degree, density, diameter, modularity, average path length, the total number of edges, and the total number of nodes.