Fine-grained opinion mining by integrating multiple review sources

Qingliang Miao, Qiudan Li, Dajun Zeng

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine-grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature-opinion tuples are generated. Experimental results on real-world datasets show that the proposed approach is effective.

Original languageEnglish (US)
Pages (from-to)2288-2299
Number of pages12
JournalJournal of the American Society for Information Science and Technology
Volume61
Issue number11
DOIs
StatePublished - Nov 2010

Fingerprint

integration strategy
knowledge
Feature extraction
customer
Opinion mining
Domain knowledge
Online reviews
Web 2.0
Sentiment

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Information Systems
  • Human-Computer Interaction
  • Computer Networks and Communications

Cite this

Fine-grained opinion mining by integrating multiple review sources. / Miao, Qingliang; Li, Qiudan; Zeng, Dajun.

In: Journal of the American Society for Information Science and Technology, Vol. 61, No. 11, 11.2010, p. 2288-2299.

Research output: Contribution to journalArticle

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