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.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Kore a (NRF) funded by the Ministry of Science, ICT and Future Planning (numbers 2015R1A2A1A10052665 and 2015R1A2A1A15052701 ).
© 2016 Elsevier Inc. All rights reserved.
- Aspect discovery
- Opinion mining
- Sentiment analysis
- Topic model
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence