Joint multi-grain topic sentiment: Modeling semantic aspects for online reviews

Md Hijbul Alam, Woo Jong Ryu, Sang-Geun Lee

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

The availability of electronic word-of-mouth, online consumer reviews, is increasing rapidly. Users frequently look for important aspects of a product or service in the reviews. They are typically interested in sentiment-oriented ratable aspects (i.e., semantic aspects). However, extracting semantic aspects across domains is challenging. We propose a domain-independent topic sentiment model called Joint Multi-grain Topic Sentiment (JMTS) to extract semantic aspects. JMTS effectively extracts quality semantic aspects automatically, thereby eliminating the requirement for manual probing. We conduct both qualitative and quantitative comparisons to evaluate JMTS. The experimental results confirm that JMTS generates semantic aspects with correlated top words and outperforms state-of-the-art models in several performance metrics.

Original languageEnglish
Pages (from-to)206-223
Number of pages18
JournalInformation Sciences
Volume339
DOIs
Publication statusPublished - 2016 Apr 20

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Keywords

  • Aspect discovery
  • Opinion mining
  • Sentiment analysis
  • Topic model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management

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