A Multifacet Hierarchical Sentiment-Topic Model with Application to Multi-Brand Online Review Analysis

TL;DR

This paper introduces a hierarchical sentiment-topic model for analyzing multi-brand online reviews, enabling detailed comparison of brand perceptions across multiple aspects with improved topic hierarchy and accuracy.

Contribution

It presents a novel multifacet hierarchical sentiment-topic model with a hierarchical Polya urn scheme, advancing multi-brand review analysis by capturing aspect-specific sentiments and brand distinctions.

Findings

Effective in detecting meaningful topic hierarchies

Accurate in deriving brand-specific rankings

Validated on synthetic and real-world data

Abstract

Multi-brand analysis based on review comments and ratings is a commonly used strategy to compare different brands in marketing. It can help consumers make more informed decisions and help marketers understand their brand's position in the market. In this work, we propose a multifacet hierarchical sentiment-topic model (MH-STM) to detect brand-associated sentiment polarities towards multiple comparative aspects from online customer reviews. The proposed method is built on a unified generative framework that explains review words with a hierarchical brand-associated topic model and the overall polarity score with a regression model on the empirical topic distribution. Moreover, a novel hierarchical Polya urn (HPU) scheme is proposed to enhance the topic-word association among topic hierarchy, such that the general topics shared by all brands are separated effectively from the unique topics specific to individual brands. The performance of the proposed method is evaluated on both synthetic data and two real-world review corpora. Experimental studies demonstrate that the proposed method can be effective in detecting reasonable topic hierarchy and deriving accurate brand-associated rankings on multi-aspects.