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Topic sentiment mining for sales performance prediction in e-commerce

Author

Listed:
  • Hui Yuan

    (City University of Hong Kong)

  • Wei Xu

    (Renmin University of China
    Renmin University of China)

  • Qian Li

    (Renmin University of China)

  • Raymond Lau

    (City University of Hong Kong)

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.

Suggested Citation

  • Hui Yuan & Wei Xu & Qian Li & Raymond Lau, 2018. "Topic sentiment mining for sales performance prediction in e-commerce," Annals of Operations Research, Springer, vol. 270(1), pages 553-576, November.
    • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-017-2421-7
    DOI: 10.1007/s10479-017-2421-7
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    1. Eric J. Johnson & Wendy W. Moe & Peter S. Fader & Steven Bellman & Gerald L. Lohse, 2004. "On the Depth and Dynamics of Online Search Behavior," Management Science, INFORMS, vol. 50(3), pages 299-308, March.
    2. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    3. Ying Liu & Hong Li & Geng Peng & Benfu Lv & Chong Zhang, 2015. "Online purchaser segmentation and promotion strategy selection: evidence from Chinese E-commerce market," Annals of Operations Research, Springer, vol. 233(1), pages 263-279, October.
    4. C. Raju & Y. Narahari & K. Ravikumar, 2006. "Learning dynamic prices in electronic retail markets with customer segmentation," Annals of Operations Research, Springer, vol. 143(1), pages 59-75, March.
    5. Mark Daskin & Collette Coullard & Zuo-Jun Shen, 2002. "An Inventory-Location Model: Formulation, Solution Algorithm and Computational Results," Annals of Operations Research, Springer, vol. 110(1), pages 83-106, February.
    6. Tingliang Huang & Jan A. Van Mieghem, 2014. "Clickstream Data and Inventory Management: Model and Empirical Analysis," Production and Operations Management, Production and Operations Management Society, vol. 23(3), pages 333-347, March.
    7. Jonah Berger & Alan T. Sorensen & Scott J. Rasmussen, 2010. "Positive Effects of Negative Publicity: When Negative Reviews Increase Sales," Marketing Science, INFORMS, vol. 29(5), pages 815-827, 09-10.
    8. Baichun Xiao & Youyi Feng & Edward Roche, 2005. "Uncertain Switching Costs and Purchase Decisions in Electronic Markets," Annals of Operations Research, Springer, vol. 135(1), pages 179-196, March.
    9. César Alfaro & Javier Cano-Montero & Javier Gómez & Javier M. Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
    10. Chris Forman & Anindya Ghose & Batia Wiesenfeld, 2008. "Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets," Information Systems Research, INFORMS, vol. 19(3), pages 291-313, September.
    11. Nawel Amrouche & Ruiliang Yan, 2016. "A manufacturer distribution issue: how to manage an online and a traditional retailer," Annals of Operations Research, Springer, vol. 244(2), pages 257-294, September.
    12. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    13. Luxhoj, James T. & Riis, Jens O. & Stensballe, Brian, 1996. "A hybrid econometric--neural network modeling approach for sales forecasting," International Journal of Production Economics, Elsevier, vol. 43(2-3), pages 175-192, June.
    14. César Alfaro & Javier Cano-Montero & Javier Gómez & Javier Moguerza & Felipe Ortega, 2016. "A multi-stage method for content classification and opinion mining on weblog comments," Annals of Operations Research, Springer, vol. 236(1), pages 197-213, January.
    15. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
    Full references (including those not matched with items on IDEAS)

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