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Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning

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  • Zhu, John Jianjun
  • Chang, Yung-Chun
  • Ku, Chih-Hao
  • Li, Stella Yiyan
  • Chen, Chi-Jen

Abstract

This research proposes and tests mechanisms for defining and identifying the critical online consumer reviews that firms could prioritize to optimize their online response strategies, while incorporating the latest artificial intelligence (AI) technology to deal with the overwhelming volume of information. Three sets of analytical tools are introduced: Heuristic Processing, Linguistic Feature Analysis, and Deep Learning-based Natural Language Processing (NLP), to extract review information. Twelve algorithms to classify critical reviews were developed accordingly and empirically tested for their effectiveness. Our econometric analysis of 110,146 online reviews from a chain operation in hospitality industry over seven years identifies six outstanding algorithms. Firm value rating, comment length, valence, and certain consumer emotions, in addition to past comment-response behavior, are found to be superior in predicting incoming review criticality. However, the service attributes such as urgency to reply and the feasibility of actions to take are not as informative.

Suggested Citation

  • Zhu, John Jianjun & Chang, Yung-Chun & Ku, Chih-Hao & Li, Stella Yiyan & Chen, Chi-Jen, 2021. "Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning," Journal of Business Research, Elsevier, vol. 129(C), pages 860-877.
    • Handle: RePEc:eee:jbrese:v:129:y:2021:i:c:p:860-877
    DOI: 10.1016/j.jbusres.2020.11.007
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    3. Feng, Yi & Yin, Yunqiang & Wang, Dujuan & Dhamotharan, Lalitha, 2022. "A dynamic ensemble selection method for bank telemarketing sales prediction," Journal of Business Research, Elsevier, vol. 139(C), pages 368-382.

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