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Extracting and ranking product features in consumer reviews based on evidence theory

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Abstract

Online consumer product reviews have been the primary source for manufacturers and consumers to obtain consumer knowledge and product information. However, the explosion of product reviews has dramatically increased the difficulty of information extraction and selection. This paper proposes a novel feature ranking algorithm based on the Dempster-Shafer evidence theory to identify and extract the important features from online product reviews. Specifically, the study first extracts product feature and sentiment pairs, and build the bipartite weighted network based on those pairs. Then, we rank those important features by considering the impact of their feature frequency distribution and network location information. Finally, the empirical evaluation using two realistic consumer reviews datasets, the performance of our algorithm has an increment of 10–40% than TF-IDF, TextRank, and HITS benchmark in the feature identification and extraction.

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Notes

  1. https://pypi.org/project/jieba/.

  2. http://www.ltp-cloud.com/.

  3. http://www.jd.com.

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Acknowledgements

The authors are very grateful for the insightful comments and suggestions of the anonymous reviewers and the editor, which have helped to significantly improve this article. Furthermore, this work was supported by China Postdoctoral Science Found (No. 2021M692135) and Shanghai Philosophy and Social Science Planning Project (No. 2021BTQ003).

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Authors and Affiliations

  1. Business School, University of Shanghai for Science and Technology, Shanghai, China

    Lixin Zhou

  2. School of Economics and Management, Tongji University, Shanghai, China

    Li Tang

  3. School of Automation, Nanjing University of Science and Technology, Nanjing, China

    Zhenyu Zhang

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  1. Lixin Zhou
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  2. Li Tang
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Correspondence to Li Tang or Zhenyu Zhang.

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Zhou, L., Tang, L. & Zhang, Z. Extracting and ranking product features in consumer reviews based on evidence theory. J Ambient Intell Human Comput 14, 9973–9983 (2023). https://doi.org/10.1007/s12652-021-03664-1

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