Modeling Online Reviews with Multi-grain Topic Models

TL;DR

This paper introduces multi-grain topic models that improve the extraction of ratable aspects from online reviews by capturing more specific, coherent topics compared to standard models, aiding opinion mining and summarization.

Contribution

The paper presents a novel multi-grain topic modeling framework that better captures specific object aspects and clusters related topics, advancing opinion mining from reviews.

Findings

Multi-grain models outperform standard topic models in extracting aspects.

Models produce more coherent and specific aspect clusters.

Significant improvements shown in qualitative and quantitative evaluations.

Abstract

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. 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.