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Rotten Tomatoes movies and critic reviews dataset

Rotten Tomatoes movies and critic reviews dataset

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2020
Creators: (Leone, Stefano)

The Rotten Tomatoes Movies and Critic Reviews dataset is a collection of information scraped from the Rotten Tomatoes website as of October 31, 2020. It encompasses data on over 17,000 movies, including details such as movie titles, descriptions, genres, durations, directors, actors, as well as user and critic ratings. A distinctive feature of this dataset is its ability to facilitate comparisons between audience scores (ratings from regular users) and tomatometer scores (ratings from certified critics), offering valuable insights into differing perspectives on films. In the movies dataset each record represents a movie available on Rotten Tomatoes, with the URL used for the scraping, movie tile, description, genres, duration, director, actors, users’ ratings, and critics’ ratings.
In the critics dataset each record represents a critic review published on Rotten Tomatoes, with the URL used for the scraping, critic name, review publication, date, score, and content.

Rotten Tomatoes allows to compare the ratings given by regular users (audience score) and the ratings given/reviews provided by critics (tomatometer) who are certified members of various writing guilds or film critic-associations.The dataset is 0.23 GB large.

The dataset is structured into two main components:

  1. Movies Dataset: Each record represents a movie available on Rotten Tomatoes, containing fields such as:

    • rotten_tomatoes_link: The specific URL from which the movie data was scraped.
    • movie_title: The title of the movie as displayed on the Rotten Tomatoes website.
    • movie_info: A brief description of the movie.
    • genres: The genres associated with the movie, separated by commas if multiple.
    • original_release_date: The date on which the movie was originally released.
    • content_rating: The category indicating the movie’s suitability for different audiences.
    • critics_consensus: Comments from Rotten Tomatoes summarizing critics’ opinions.

  2. Critics Dataset: Each record represents a critic’s review published on Rotten Tomatoes, including details such as:

    • critic_name: The name of the critic who reviewed the movie.
    • top_critic: A boolean value indicating whether the critic is classified as a top critic.
    • publisher_name: The name of the publication for which the critic works.
    • review_type: Specifies whether the review was labeled as ‘fresh’ or ‘rotten’.
    • review_score: The score provided by the critic for the movie.
    • review_date: The date when the review was published.
    • review_content: The content of the review.
Food.com Recipes and Interactions

Food.com Recipes and Interactions

Stefan
Open Dataset
Creators: Shuyang, Li
Publication Date: 2019
Creators: Shuyang, Li

The Food.com Recipes and Interactions dataset is a large-scale collection of culinary data, comprising over 180,000 recipes and 700,000 user reviews spanning an 18-year period. Compiled by Shuyang Li and published in 2019, this dataset provides a rich source of information for studying user interactions, culinary trends, and recipe recommendation systems. It originates from Food.com (formerly GeniusKitchen), one of the largest online recipe-sharing platforms, making it a valuable resource for researchers and practitioners in food science, natural language processing, and user behavior analysis. The data set is 0.89 GB large and consists of two primary components: recipe data and user interaction data. The recipe data contains structured information about each recipe, including the recipe ID, name tokens (a tokenized version of the recipe title), ingredient tokens, steps tokens (instructions in tokenized form), cooking techniques, caloric level, and ingredient IDs corresponding to specific ingredients. These features allow for deep analysis of how recipes are structured, categorized, and consumed over time. The user interaction data captures engagement metrics, tracking how users interact with recipes. It includes the user ID, a list of recipes reviewed, the number of items reviewed, the ratings assigned, and the total number of ratings provided by each user. This structure enables research into user preference modeling, recipe popularity trends, and the development of personalized recommendation systems for recipe suggestions.

FilmTV movies dataset

FilmTV movies dataset

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2018
Creators: (Leone, Stefano)

The FilmTV movies dataset serves as a valuable resource for researchers, data analysts, and movie enthusiasts interested in exploring various aspects of cinema. With data spanning over a century, the dataset provides a broad temporal view of film trends, genre popularity, and audience reception. Movies data are available on websites such as IMDb with average votes, vote numbers, reviews and descriptions. While IMDb is the most trustworthy source for data, other websites as FilmTV can provide the information on how users from different countries rate the movies compared to each other. The dataset is 0.11 GB large.

Each row represents a movie available on FilmTV.it, with the original title, year, genre, duration, country, director, actors, average vote and votes.
The file in the English version contains 37,711 movies and 19 attributes, while the Italian version contains one extra-attribute for the local title used when the movie was published in Italy.

The data set includes movies from: 1897 – 2023. Data has been scraped from the publicly available website https://www.filmtv.it as of 2023-10-21.

Stock return prediction with tweets

Stock return prediction with tweets

Stefan
Open Dataset
Creators: Madhyastha, Pranava; Sowinska, Karolina
Publication Date: 2020
Creators: Madhyastha, Pranava; Sowinska, Karolina

This dataset is designed to analyze the impact of Twitter-based textual information on stock returns. Compiled by researchers Karolina Sowinska and Pranava Madhyastha, this dataset was published in 2020 and is made available under the GNU General Public License v3.0 or later. It provides valuable data for financial analytics and natural language processing, particularly in studying the relationship between social media sentiment and stock market performance. By linking tweets to stock return data, the dataset enables the development of predictive models for stock movement based on public sentiment. The dataset comprises 862,231 labeled tweets, all in English, each associated with specific companies. These tweets serve as samples for analyzing public opinion and sentiment regarding different stocks and financial events. A cleaned subset of 85,176 labeled instances is also included, making the dataset suitable for both large-scale machine learning models and more focused analyses. Each tweet is linked to corresponding stock return data, allowing for a company-level examination of how Twitter sentiment impacts one-day, two-day, three-day, and seven-day stock returns. This structured linkage between tweets and financial performance provides a unique opportunity to study the effects of social media on stock price fluctuations. The dataset is approximately 225 MB in size on GitHub, making it manageable for various analytical tasks, including sentiment analysis, text-based predictive modeling, and financial forecasting. It is structured into two primary components:

  • Tweet Data: This includes the textual content of tweets, user metadata, timestamps, and the companies referenced in each tweet. These features allow researchers to perform sentiment analysis, track user engagement, and examine the frequency of stock-related discussions on social media.

  • Stock Return Data: This includes numerical stock return values corresponding to the companies mentioned in the tweets. The returns are recorded over multiple time intervals, enabling the study of both short-term and long-term price movements in response to social media discussions.

MovieTweetings

MovieTweetings

Stefan
Open Dataset
Creators: Dooms, Simon; De Pessemier, Toon; Martens, Luc
Publication Date: 2013
Creators: Dooms, Simon; De Pessemier, Toon; Martens, Luc
MovieTweetings is a dataset consisting of ratings on movies that were contained in well-structured tweets on Twitter. The goal of this dataset is to provide the RecSys community with a live, natural and always up-to-date movie ratings dataset. The dataset has been actively collecting ratings since February 28, 2013, and will be updated as much as possible to incorporate rating data from the newest tweets available. The dataset includes 921,398 ratings from 71,707 unique users. The ratings contained in the tweets are scaled from 0 to 10, as is the norm on the IMDb platform. In total, the dataset has a size of 26,2 MB and consists of two main files:

  • ratings.dat contains extracted ratings, structured as:
    user_id::movie_id::rating::rating_timestamp

    • user_id: Unique identifier for the user.
    • movie_id: IMDb identifier for the movie.
    • rating: User’s score on a 10-star scale.
    • rating_timestamp: Unix timestamp when the rating was extracted.

  • items.dat includes metadata about the rated movies, structured as:
    movie_id::movie_title (movie_year)::genre|genre|genre

    • movie_id: IMDb identifier for the movie.
    • movie_title: Name of the movie along with the release year.
    • genre: Pipe-separated list of genres.
Twitter US Airline Sentiment

Twitter US Airline Sentiment

Stefan
Open Dataset
Creators: (Makone, Ashutosh)
Publication Date: 2016
Creators: (Makone, Ashutosh)

The Twitter US Airline Sentiment dataset is a collection of tweets aimed at analyzing public sentiment toward major U.S. airlines. Compiled in February 2015, the dataset consists of 14,640 tweets directed at several U.S. airlines. It serves as a valuable resource for sentiment analysis and natural language processing research, particularly in understanding customer satisfaction, airline service quality, and issues reported by travelers. Each tweet in the dataset is labeled with one of three sentiment categories: positive, neutral, or negative. Tweets labeled as negative are further categorized into specific negative sentiment reasons, such as late flight, customer service issue, canceled flight, and lost luggage, providing deeper insights into common complaints. The dataset also identifies the airline mentioned in each tweet, covering six major U.S. carriers: United Airlines, US Airways, American Airlines, Southwest Airlines, Delta Air Lines, and Virgin America. Additional metadata is provided for each tweet, including tweet ID, tweet text, tweet coordinates (if available), user information, and location data, allowing for further contextual analysis. The dataset is relatively small, with a total size of 8,46 MB, making it easily manageable for sentiment analysis tasks and machine learning applications. It includes 14,640 tweets from 7,700 unique users, providing a broad yet concise representation of customer interactions with airlines on Twitter. The tweets were collected over a one-month period in February 2015, offering a snapshot of public sentiment during that specific timeframe.

The Music Streaming Sessions Dataset

The Music Streaming Sessions Dataset

Stefan
Open Dataset
Creators: Brost, Brian; Mehrotra, Rishabh; Jehan, Tristan
Publication Date: 2018
Creators: Brost, Brian; Mehrotra, Rishabh; Jehan, Tristan

The MSSD is a large-scale collection of user interaction data from a music streaming service, designed to support research in user behavior modeling, music information retrieval, and session-based recommendation systems. Released in 2019, this dataset contains approximately 160 million listening sessions, making it one of the most extensive datasets available for analyzing how users engage with music streaming platforms. It provides valuable insights into listening habits, session structures, and sequential user interactions, enabling researchers to study music recommendation, user retention, and engagement patterns. The dataset has a size of 70 GB and captures approximately 3.7 million unique tracks, covering a diverse range of musical content. Each session includes detailed user interactions, such as play, pause, skip, and seek actions, offering a granular view of how listeners interact with music over time. Additionally, it contains rich metadata and audio features for each track, including details such as track ID, artist name, album name, and genre, along with audio attributes like tempo, key, and loudness. These elements make the dataset highly valuable for both behavioral studies and technical research in music information retrieval.

Spotify Million Playlist Dataset

Spotify Million Playlist Dataset

Stefan
Open Dataset
Creators: Chen, Ching-Wei; Lamere, Paul ; Schedl, Markus ; Zamani, Hamed
Publication Date: 2018
Creators: Chen, Ching-Wei; Lamere, Paul ; Schedl, Markus ; Zamani, Hamed
We released a dataset of one million user-created playlists from the Spotify platform, dubbed the Million Playlist Dataset (MPD). The dataset includes, for each playlist, its title as well as the list of tracks (including album and artist names), and some additional metadata such as Spotify URIs and the playlist’s number of followers. The dataset has a size of 5,39 GB and contains 1,000,000 playlists, including playlist titles and track titles, created by users on the Spotify platform between January 2010 and October 2017. It is ideal for building and evaluating recommendation algorithms, studying user behavior in music consumption, and understanding how playlists evolve over time. The dataset is widely used by researchers and developers to improve machine learning models for music streaming applications, ensuring a more personalized and engaging experience for users.

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