Modeling Online Reviews with Multi-grain Topic Models
In this paper we present a novel framework for extracting the ratable aspects of objects from online user reviews. Extracting such aspects is an important challenge in automatically mining product opinions from the web and in generating opinion-based summaries of user reviews 18, 19, 7, 12, 27, 36, 21. Our models are based on extensions to standard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain models are more appropriate for our task since standard models tend to produce topics that correspond to global properties of objects (e.g., the brand of a product type) rather than the aspects of an object that tend to be rated by a user. The models we present not only extract ratable aspects, but also cluster them into coherent topics, e.g., waitress and bartender are part of the same topic sta. For restaurants. This di.erentiates it from much of the previous work which extracts aspects through term frequency analysis with minimal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve signi.cantly upon standard topic models.
Ivan Titov Ryan McDonald
Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801 Google Inc.76 Ninth Avenue New York, NY 10011
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）