Modeling online reviews with multi-grain topic models

TLDR

Extracting ratable aspects from online reviews is a key challenge in opinion mining, and multi‑grain models are preferable because standard topic models capture global product properties rather than user‑rated aspects, distinguishing this work from prior term‑frequency based approaches. The paper proposes a novel framework to extract ratable aspects from online user reviews. The framework extends LDA/PLSA with multi‑grain topics to extract and cluster ratable aspects, and is evaluated qualitatively and quantitatively, showing significant improvement over standard topic models. The models extract and cluster ratable aspects into coherent topics and outperform standard topic models in both 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 [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.