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Fine-grained opinion mining by integrating multiple review sources

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  • Qingliang Miao
  • Qiudan Li
  • Daniel Zeng

Abstract

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

    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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).
    6. 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.

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