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Mining and classifying customer reviews: a survey

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Abstract

With the increasing number of customer reviews on the Web, there is a growing need for effective methods to retrieve valuable information hidden in these reviews, as sellers need to gain a deep understanding of customers’ preferences in a timely manner. With the continuous enhancement of opinion mining or sentiment analysis research, researchers have proposed many automatic mining and classification methods. However, how to choose a trusted method is a difficult problem for companies, because customer reviews (or opinions) contain a lot of uncertain information and noise. This article reports on a detailed survey of recent opinion mining literature. It also reviews how to extract text features in opinions that may contain noise or uncertainties, how to express knowledge in opinions, and how to classify them. Through this extensive study, this paper discusses open questions and recommends future research directions for building the next generation of opinion mining systems.

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Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    L. D. C. S. Subhashini, Yuefeng Li, Jinglan Zhang & Yutong Wu

  2. University of Colombo School of Computing, Colombo, Sri Lanka

    L. D. C. S. Subhashini & Ajantha S. Atukorale

  3. University of Sri Jayewardenepura, Nugegoda, Sri Lanka

    L. D. C. S. Subhashini

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Subhashini, L.D.C.S., Li, Y., Zhang, J. et al. Mining and classifying customer reviews: a survey. Artif Intell Rev 54, 6343–6389 (2021). https://doi.org/10.1007/s10462-021-09955-5

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