Extracting and Ranking Travel Tips from User-Generated Reviews
Year: 2017
Type: conference-paper
Abstract: User-generated reviews are a key driving force behind some of the leading websites, such as Amazon, TripAdvisor, and Yelp. Yet, the proliferation of user reviews in such sites also poses an information overload challenge: many items, especially popular ones, have a large number of reviews, which cannot all be read by the user. In this work, we propose to extract short practical tips from user reviews. We focus on tips for travel attractions extracted from user reviews on TripAdvisor. Our method infers a list of templates from a small gold set of tips and applies them to user reviews to extract tip candidates. For each attraction, the associated candidates are then ranked according to their predicted usefulness. Evaluation based on labeling by professional annotators shows that our method produces high-quality tips, with good coverage of cities and attractions.
Authors: Ido Guy, Avihai Mejer, Alexander Nus, Fiana Raiber
Language: English
Topic: Web Data Mining and Analysis
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2604359435