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Fine‐grained opinion mining by integrating multiple review sources

Author

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  • Qingliang Miao
  • Qiudan Li
  • Daniel Zeng

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.

Suggested Citation

  • Qingliang Miao & Qiudan Li & Daniel Zeng, 2010. "Fine‐grained opinion mining by integrating multiple review sources," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(11), pages 2288-2299, November.
    • Handle: RePEc:bla:jamist:v:61:y:2010:i:11:p:2288-2299
    DOI: 10.1002/asi.21400
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    Cited by:

    1. Pashchenko, Yana & Rahman, Mst Farjana & Hossain, Md Shamim & Uddin, Md Kutub & Islam, Tarannum, 2022. "Emotional and the normative aspects of customers’ reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    2. Zunqiang Zhang & Guoqing Chen & Jin Zhang & Xunhua Guo & Qiang Wei, 2016. "Providing Consistent Opinions from Online Reviews: A Heuristic Stepwise Optimization Approach," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 236-250, May.
    3. Perez-Cepeda, Maximiliano & Arias-Bolzmann, Leopoldo G., 2022. "Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter," Journal of Business Research, Elsevier, vol. 140(C), pages 384-393.
    4. Abhijit Bera & Mrinal Kanti Ghose & Dibyendu Kumar Pal, 2021. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(4), pages 1-12, October.
    5. Hossain, Md Shamim & Rahman, Mst Farjana, 2022. "Detection of potential customers’ empathy behavior towards customers' reviews," Journal of Retailing and Consumer Services, Elsevier, vol. 65(C).
    6. Saba Resnik & Mateja Kos Koklič, 2018. "User-Generated Tweets about Global Green Brands: A Sentiment Analysis Approach," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 30(2), pages 125-145.

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