document: 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 ` staff ' for restaurants . This differentiates 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 significantly upon standard topic models .
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