通过分层主题模型分析多品牌评论，精准识别各品牌在不同维度的口碑差异。
A Multifacet Hierarchical Sentiment-Topic Model with Application to Multi-Brand Online Review Analysis
- 构建分层情感-主题框架，统一建模评论词汇与情感极性。
- 在真实数据集上实现多维度品牌口碑排名，准确率显著提升。
- 适合市场分析、消费者行为研究者，用于对比多个品牌表现。
基于评论内容和评分的多品牌分析是营销中常用的策略，有助于消费者决策和品牌定位理解。本文提出一种多维度分层情感-主题模型（MH-STM），从在线客户评论中检测针对多个比较维度的品牌情感极性。该方法基于统一生成框架，利用分层品牌相关主题模型解释评论词汇，并通过回归模型基于经验主题分布预测整体情感得分。此外，提出新型分层庞亚乌尔（HPU）机制，增强主题层次中的词-主题关联，有效分离所有品牌共有的通用主题与各品牌特有主题。在合成数据和两个真实评论语料库上的实验表明，该方法能有效构建合理主题层次，并在多维度上获得准确的品牌排名。
原文摘要 · Abstract (English)
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.
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