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Forecasting product sales using text mining: a case study in new energy vehicle

Author

Listed:
  • Yi Ding

    (Anhui University)

  • Peng Wu

    (Anhui University
    Anhui University)

  • Jie Zhao

    (Anhui University)

  • Ligang Zhou

    (Anhui University
    Anhui University)

Abstract

This study aims to improve the prediction accuracy of product sales by developing an online review-driven combination forecasting model. The proposed model includes two parts: online reviews and the forecasting model. For online reviews, the sentiment value concerning different product attributes is defined from the sentiment score and the sentiment tendency based on prospect theory. Furthermore, the Mallat pyramid algorithm is used to mitigate the impact of reviews with nonstandard expressions, malicious reviews and spam on the sentiment value of online reviews. A combination forecasting model composed of a backpropagation neural network (BPNN), recurrent neural network (RNN) and long short-term memory (LSTM) neural network is constructed. Taking the sales of BYD-Tang as a case study, some statistical evaluation indicators and the Diebold-Mariano (DM) test indicate the superior performance of our proposed online review-driven combination forecasting model in prediction accuracy.

Suggested Citation

  • Yi Ding & Peng Wu & Jie Zhao & Ligang Zhou, 2025. "Forecasting product sales using text mining: a case study in new energy vehicle," Electronic Commerce Research, Springer, vol. 25(1), pages 495-527, February.
    • Handle: RePEc:spr:elcore:v:25:y:2025:i:1:d:10.1007_s10660-023-09701-9
    DOI: 10.1007/s10660-023-09701-9
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