Hierarchical features-based targeted aspect extraction from online reviews

Jin He, Lei Li*, Yan Wang, Xindong Wu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

With the prevalence of online review websites, large-scale data promote the necessity of focused analysis. This task aims to capture the information that is highly relevant to a specific aspect. However, the broad scope of the aspects of the various products makes this task overarching but challenging. A commonly used solution is to modify the topic models with additional information to capture the features for a specific aspect (referred to as a targeted aspect). However, the existing topic models, either perform the full analysis to capture features as many as possible or estimate the similarity to capture features as coherent as possible, overlook the fine-grained semantic relations between the features, resulting in the captured features coarse and confusing. In this paper, we propose a novel Hierarchical Features-based Topic Model (HFTM) to extract targeted aspects from online reviews, then to capture the aspect-specific features. Specifically, our model can not only capture the direct features posing target-to-feature semantics but also capture the latent features posing feature-to-feature semantics. The experiments conducted on real-world datasets demonstrate that HFTMl outperforms the state-of-the-art baselines in terms of both aspect extraction and document classification.

Original languageEnglish
Pages (from-to)205-223
Number of pages19
JournalIntelligent Data Analysis
Volume25
Issue number1
DOIs
Publication statusPublished - 2021

Keywords

  • Aspect extraction
  • Focused analysis
  • Online reviews
  • Text mining
  • Topic modeling

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