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. Ex- tracting such aspects is an important challenge in automati- cally mining product opinions from the web and in generat- ing opinion-based summaries of user reviews (18, 19, 7, 12, 27, 36, 21). Our models are based on extensions to stan- dard topic modeling methods such as LDA and PLSA to induce multi-grain topics. We argue that multi-grain mod- els 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 staff for restaurants. This differentiates it from much of the previous work which extracts aspects through term frequency analysis with min- imal clustering. We evaluate the multi-grain models both qualitatively and quantitatively to show that they improve significantly upon standard topic models.