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Products Ranking Through Aspect-Based Sentiment Analysis of Online Heterogeneous Reviews

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Abstract

With the rapid growth of online shopping platforms, more and more customers intend to share their shopping experience and product reviews on the Internet. Both large quantity and various forms of online reviews bring difficulties for potential consumers to summary all the heterogenous reviews for reference. This paper proposes a new ranking method through online reviews based on different aspects of the alternative products, which combines both objective and subjective sentiment values. Firstly, weights of these aspects are determined with LDA topic model to calculate the objective sentiment value of the product. During this process, the realistic meaning of each aspect is also summarized. Then, consumers’ personalized preferences are taken into consideration while calculating total scores of alternative products. Meanwhile, comparative superiority between every two products also contributes to their final scores. Therefore, a directed graph model is constructed and the final score of each product is computed by improved PageRank algorithm. Finally, a case study is given to illustrate the feasibility and effectiveness of the proposed method. The result demonstrates that while considering only objective sentiment values of the product, the ranking result obtained by our proposed method has a strong correlation with the actual sales orders. On the other hand, if consumers express subjective preferences towards a certain aspect, the final ranking is also consistent with the actual performance of alternative products. It provides a new research idea for online customer review mining and personalized recommendation.

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Acknowledgements

The authors would like to thank the anonymous referees for their insightful and constructive comments and suggestions that have led to an improved version of this paper. This research was supported in part by the National Natural Science Foundation of China [Grant Numbers 71771034, 71421001] and the Scientific and Technological Innovation Foundation of Dalian (2018J11CY009). This paper is a significantly extended and revised version of the conference paper presented at KSS-2017 (Guo, Du & Kou, 2017).

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Authors and Affiliations

  1. Institute of Systems Engineering, Dalian University of Technology, Dalian, 6024, China

    Chonghui Guo, Zhonglian Du & Xinyue Kou

Authors
  1. Chonghui Guo
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  2. Zhonglian Du
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  3. Xinyue Kou
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Corresponding author

Correspondence to Chonghui Guo.

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Chonghui Guo is a professor of the Institute of Systems Engineering, Dalian University of Technology, Dalian, China. He received the B.S. degree in Mathematics from Liaoning University in 1995, M.S. degree in Operational Research and Control Theory in 1999 and Ph.D. degree in Management Science and Engineering from Dalian University of Technology in 2002. He was a postdoctoral research fellow in the Department of Computer Science in Tsinghua University, Beijing, China. His studies concentrate on data mining and knowledge discovery. He has published over 100 peer-reviewed papers in academic journals and conferences, besides 5 text-books and 2 monographs. He has been the Principal Investigator on over 10 research projects from the Government and the Industry.

Zhonglian Du received her B.S. degree in 2015 from School of Business, Jiangnan University. She is currently a master student in the Institute of Systems Engineering, Dalian University of Technology. Her general research interests include text mining and business intelligence. Her papers have been published and presented on journals and conferences such as the System Engineering and the 18th International Symposium on Knowledge and Systems Sciences.

Xinyue Kou received her B.S. degree in 2016 from School of Economics and Management, China University of Petroleum. She is currently a master student in the Institute of Systems Engineering, Dalian University of Technology. Her general research interests include two-sided matching and group decision. Her papers have been published and presented in journals and conferences such as the Expert Systems with Applications and Knowledge-Based Systems.

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Guo, C., Du, Z. & Kou, X. Products Ranking Through Aspect-Based Sentiment Analysis of Online Heterogeneous Reviews. J. Syst. Sci. Syst. Eng. 27, 542–558 (2018). https://doi.org/10.1007/s11518-018-5388-2

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