A TripAdvisor Dataset for Dyadic Context Analysis

Resumen

There are many contexts where dyadic data are present. In social networks, users are linked to a variety of items, defining interactions. In the social platform of TripAdvisor, users are linked to restaurants by means of reviews posted by them. Using the information of these interactions, we can get valuable insights for forecasting, proposing tasks related to recommender systems, sentiment analysis, text-based personalisation or text summarisation, among others. Furthermore, in the context of TripAdvisor there is a scarcity of public datasets and lack of well-known benchmarks for model assessment. We present six new TripAdvisor datasets from the restaurants of six different cities: London, New York, New Delhi, Paris, Barcelona and Madrid. <strong>If you use this data, please cite the following paper under submission process</strong> (preprint - arXiv) We exclusively collected the reviews written in English from the restaurants of each city. The tabular data is comprised of a set of six different CSV files, containing numerical, categorical and text features: <strong>parse_count</strong>: numerical (integer), corresponding number of extracted review by the web scraper (auto-incremental) <strong>author_id: </strong>categorical (string), univocal, incremental and anonymous identifier of the user (UID_XXXXXXXXXX) <strong>restaurant_name: </strong>categorical (string), name of the restaurant matching the review <strong>rating_review: </strong>numerical (integer), review score in the range 1-5 <strong>sample: </strong>categorical (string), indicating “positive” sample for scores 4-5 and “negative” for scores 1-3 <strong>review_id: </strong>categorical (string), univocal and internal identifier of the review (review_XXXXXXXXX) <strong>title_review: </strong>text, review title <strong>review_preview: </strong>text, preview of the review, truncated in the website when the text is very long <strong>review_full: </strong>text, complete review <strong>date: </strong>timestamp, publication date of the review in the format (day, month, year) <strong>city</strong>: categorical (string), city of the restaurant which the review was written for <strong>url_restaurant: </strong>text, restaurant url