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Final submission of iConference 2014 Track "Social Media Expo"PDF


Title:Improving Restaurants by Extracting Subtopics from Yelp Reviews
Author(s):Huang, James; Rogers, Stephanie; Joo, Eunkwang
Subject(s):restaurant review, Yelp
Abstract:In this paper, we describe latent subtopics discovered from Yelp restaurant reviews by running an online Latent Dirichlet Allocation (LDA) algorithm. The goal is to point out demand of customers from a large amount of reviews, with high dimensionality. These topics can provide meaningful insights to restaurants about what customers care about in order to increase their Yelp ratings, which directly affects their revenue. We used the open dataset from the Yelp Dataset Challenge with over 158,000 restaurant reviews. To find latent subtopics from reviews, we adopted Online LDA, a generative probabilistic model for collections of discrete data such as text corpora. We present the breakdown of hidden topics over all reviews, predict stars per hidden topics discovered, and extend our findings to that of temporal information regarding restaurants peak hours. Overall, we have found several interesting insights and a method which could definitely prove useful to restaurant owners.
Issue Date:2014-04-04
Citation Info:Huang, J.; Rogers, S.; Joo, E. (2014). Improving Restaurants by Extracting Subtopics from Yelp Reviews. In iConference 2014 (Social Media Expo)
Series/Report:iConference 2014 (Social Media Expo)
Genre:Conference Paper / Presentation
Publication Status:published or submitted for publication
Date Available in IDEALS:2014-04-07

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