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Topic sentiment mining for sales performance prediction in e-commerce

  • Big Data Analytics in Operations & Supply Chain Management
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Abstract

In the era of big data, huge number of product reviews has been posted to online social media. Accordingly, mining consumers’ sentiments about products can generate valuable business intelligence for enhancing management’s decision-making. The main contribution of our research is the design of a novel methodology that extracts consumers’ sentiments over topics of product reviews (i.e., product aspects) to enhance sales predicting performance. In particular, consumers’ daily sentiments embedded in the online reviews over latent topics are extracted through the joint sentiment topic model. Finally, the sentiment distributions together with other quantitative features are applied to predict sales volume of the following period. Based on a case study conducted in one the largest e-commerce companies in China, our empirical tests show that sentiments over topics together with other quantitative features can more accurately predict sales volume when compared with using quantitative features alone.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant No. 71301163), Humanities and Social Sciences Foundation of the Ministry of Education (No. 14YJA630075, 15YJA630068), Hebei Social Science Fund (HB13GL021), and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No. 15XNLQ08). Lau’s work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project: CityU 11502115), and the Shenzhen Municipal Science and Technology R&D Funding - Basic Research Program (Project No. JCYJ20160229165300897).

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Authors and Affiliations

  1. Department of Information Systems, City University of Hong Kong, Hong Kong, People’s Republic of China

    Hui Yuan & Raymond Lau

  2. School of Information, Renmin University of China, Beijing, 100872, People’s Republic of China

    Wei Xu & Qian Li

  3. Smart City Research Center, Renmin University of China, Beijing, 100872, People’s Republic of China

    Wei Xu

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  1. Hui Yuan
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Correspondence to Wei Xu.

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Yuan, H., Xu, W., Li, Q. et al. Topic sentiment mining for sales performance prediction in e-commerce. Ann Oper Res 270, 553–576 (2018). https://doi.org/10.1007/s10479-017-2421-7

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