Showing 225-232 of 272 results
TripAdvisor European restaurants

TripAdvisor European restaurants

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2021
Creators: (Leone, Stefano)

TripAdvisor is the most popular travel website and it stores data for almost all restaurants, showing locations (even latitude and longitude coordinates), restaurant descriptions, user ratings and reviews, and many more aspects. The dataset is 0.68 GB large.

The TripAdvisor dataset includes 1,083,397 restaurants with attributes such as location data, average rating, number of reviews, open hours, cuisine types, awards, etc.

The dataset combines the restaurants from the main European countries, the data has been scraped in early May 2021.

The dataset is structured with various variables for each restaurant, such as:​

  • restaurant_link: Unique TripAdvisor restaurant link.​
  • restaurant_name: Name of the restaurant on TripAdvisor.​
  • original_location: Original location displayed on TripAdvisor.​
  • country: Country name retrieved from original_location.​
  • region: Region name retrieved from original_location.​
  • province: Province name retrieved from original_location.​
  • city: City name retrieved from original_location.​
  • address: Address displayed on TripAdvisor.​
  • latitude: Latitude coordinate.​
  • longitude: Longitude coordinate.​
  • claimed: Indicates if the restaurant business is claimed on TripAdvisor.​
  • awards: Award names.​
  • popularity_detailed: Detailed popularity ranking.​
  • popularity_generic: Generic popularity ranking (among all places to eat in the area).
European Funds dataset from Morningstar

European Funds dataset from Morningstar

Stefan
Open Dataset
Creators: (Leone, Stefano)
Publication Date: 2019
Creators: (Leone, Stefano)

The file contains 57,603 Mutual Funds and 9,495 ETFs with general aspects (as Total Net Assets, management company and size), portfolio indicators (as cash, stocks, bonds, and sectors), returns (as yeartodate, 2020-11) and financial ratios (as price/earning, Treynor and Sharpe ratios, alpha, and beta).
Additional data in terms of sustainability is also available. A key feature of this dataset is the inclusion of detailed Morningstar ratings, which are widely used in the financial industry to assess fund quality based on past performance, risk-adjusted returns, and analyst evaluations. Additionally, it offers categorization of funds, allowing for segmentation by investment type, sector, region, and fund style (e.g., growth vs. value investing). The dataset has a total size of approximately 103.88 MB.

Overall, the dataset is structured into the following variables:​

  • ticker: Fund ticker code.​
  • isin: Fund ISIN code.​
  • fund_name: Extended name of the fund.​
  • inception_date: Date of the fund’s inception.​
  • category: Fund category.​
  • rating: Morningstar rating.​
  • analyst_rating: Morningstar analyst rating.​
  • risk_rating: Morningstar risk rating.​
  • performance_rating: Morningstar performance rating.​
US Funds dataset from Yahoo Finance

US Funds dataset from Yahoo Finance

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

The US Funds dataset from Yahoo Finance collects data on 24,821 mutual funds and 1,680 exchange-traded funds (ETFs). This contains detailed information on various aspects of each fund, including general characteristics, portfolio indicators, returns, and financial ratios. A notable feature of this dataset is its extensive coverage, offering insights into both mutual funds and ETFs, which can be instrumental for comparative analyses and investment research. The dataset was published in 2018 and contains data up to November 2020, providing a temporal coverage that spans several years leading up to that point. In total, it covers 1.7 GB.

The dataset includes various variables for each fund, such as:​

  • fund_symbol: Symbol of the ETF.​
  • price_date: Date of the price (in YYYY-MM-DD format).​
  • open: Open daily price.​
  • high: Highest daily price.​
  • low: Lowest daily price.​
  • close: Close daily price.​
  • adj_close: Adjusted close daily price, which considers elements that have impacted the price such as share splits, dividends, etc.​
  • volume: Daily traded volume.​
  • nav_per_share: Daily Net Asset Value (NAV) per share.​
  • region: Name of the region in which the fund has the domicile.​
  • initial_investment: Minimum amount for initial investment.​
  • subsequent_investment: Minimum amount for subsequent investments.​
  • exchange_code: Code of the exchange where the fund is traded.​
  • exchange_name: Name of the exchange where the fund is traded

 

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.

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