linguistics

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Goodreads-books

Goodreads-books

Stefan
Open Dataset
Creators: Zając, Zygmunt
Publication Date: 2019
Creators: Zając, Zygmunt

The primary reason for creating this dataset is the requirement of a good clean dataset of books. It contains important features such as book titles, authors, average ratings, ISBN identifiers, language codes, number of pages, ratings count, text reviews count, publication dates, and publishers. A distinctive aspect of this dataset is its ability to support a wide range of book-related analyses, such as trends in book popularity, author influence, and reader preferences. The data set is 1.56 MB large and was scraped via the Goodreads API. It encompasses over 10,000 observations, each representing a unique book entry with multiple attributes. The structure of the dataset is straightforward, consisting of a single CSV file with the following key columns:

  • bookID: A unique identification number for each book.
  • title: The official title of the book.
  • authors: Names of the authors, with multiple authors separated by a delimiter.
  • average_rating: The average user rating for the book.
  • isbn & isbn13: The 10-digit and 13-digit International Standard Book Numbers, respectively.
  • language_code: The primary language in which the book is published (e.g., ‘eng’ for English).
  • num_pages: The total number of pages in the book.
  • ratings_count: The total number of ratings the book has received from users.
  • text_reviews_count: The total number of text reviews written by users.
  • publication_date: The original publication date of the book.
  • publisher: The name of the publishing house.
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.

Customer Support on Twitter

Customer Support on Twitter

Stefan
Open Dataset
Creators: Axelbrooke, Stuart
Publication Date: 2017
Creators: Axelbrooke, Stuart

The Customer Support on Twitter dataset is a large, modern corpus of tweets and replies to aid innovation in natural language understanding and conversational models, and for study of modern customer support practices and impact. It is intended to facilitate advancements in natural language understanding and the development of conversational models. Compiled by Stuart Axelbrooke in 2017, this dataset encompasses tweets and replies from prominent companies such as Apple, Amazon, Uber, Delta, and Spotify. It provides valuable insights into contemporary customer support practices and their impact, making it an essential resource for researchers interested in automated response generation, sentiment analysis, and conversational flow modeling. The dataset is approximately 516.53 MB in size. It is designed for the analysis of conversation dynamics and contains several key attributes. Each tweet entry has a unique, anonymized tweet ID (tweet_id), an anonymized user ID (author_id), a timestamp (created_at), and the tweet text (text), where sensitive information such as phone numbers and email addresses has been masked to ensure privacy. It differentiates between inbound tweets (inbound), which are directed at companies by customers, and outbound tweets, which are responses from the companies. Additionally, in_response_to_tweet_id and response_tweet_id fields allow for the reconstruction of entire conversation threads by linking tweets to their respective responses.

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