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Price prediction of e-commerce products through Internet sentiment analysis

Author

Listed:
  • Kuo-Kun Tseng

    (Harbin Institute of Technology, Shenzhen Graduate School)

  • Regina Fang-Ying Lin

    (Harbin Institute of Technology, Shenzhen Graduate School)

  • Hongfu Zhou

    (Harbin Institute of Technology, Shenzhen Graduate School)

  • Kevin Jati Kurniajaya

    (Harbin Institute of Technology, Shenzhen Graduate School)

  • Qianyu Li

    (Harbin Institute of Technology, Shenzhen Graduate School)

Abstract

With the rapid development of the Internet and data-processing technologies, Internet sentiment analysis can be used to explore many possibilities, from Internet news about products or the influence of product price to the influence of sale behaviour and important brand strategies. In this paper, we analyse news affecting the price of products, and establish a new model for price prediction. The results show that significant news events have an impact on the sale prices of electronic products, and can improve the accuracy of price forecasts. Thus, the contribution of this paper is to propose a new forecasting model for the price of e-commerce products.

Suggested Citation

  • Kuo-Kun Tseng & Regina Fang-Ying Lin & Hongfu Zhou & Kevin Jati Kurniajaya & Qianyu Li, 2018. "Price prediction of e-commerce products through Internet sentiment analysis," Electronic Commerce Research, Springer, vol. 18(1), pages 65-88, March.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:1:d:10.1007_s10660-017-9272-9
    DOI: 10.1007/s10660-017-9272-9
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    References listed on IDEAS

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    1. Zhishuo Liu & Yongcong Wang & Shuang Zhu & Baopeng Zhang & Lingyun Wei, 2015. "Steel Prices Index Prediction in China Based on BP Neural Network," Springer Books, in: Zhenji Zhang & Zuojun Max Shen & Juliang Zhang & Runtong Zhang (ed.), Liss 2014, edition 127, pages 603-608, Springer.
    2. Yousefi, Shahriar & Weinreich, Ilona & Reinarz, Dominik, 2005. "Wavelet-based prediction of oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 265-275.
    3. Pai, Ping-Feng & Lin, Chih-Sheng, 2005. "A hybrid ARIMA and support vector machines model in stock price forecasting," Omega, Elsevier, vol. 33(6), pages 497-505, December.
    4. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
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    Cited by:

    1. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    2. Ahmed Fathalla & Ahmad Salah & Ahmed Ali, 2023. "A Novel Price Prediction Service for E-Commerce Categorical Data," Mathematics, MDPI, vol. 11(8), pages 1-20, April.
    3. Aneeta Elsa Simon & Manu K.S., 2023. "Does Sentiments Impact the Returns of Commodity Derivatives? An Evidence from Multi-commodity Exchange India," Vision, , vol. 27(1), pages 79-92, February.
    4. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    5. Jianping Li & Yinhong Yao & Yuanjie Xu & Jingyu Li & Lu Wei & Xiaoqian Zhu, 2019. "Consumer’s risk perception on the Belt and Road countries: evidence from the cross-border e-commerce," Electronic Commerce Research, Springer, vol. 19(4), pages 823-840, December.
    6. Salvatore Carta & Andrea Medda & Alessio Pili & Diego Reforgiato Recupero & Roberto Saia, 2018. "Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data," Future Internet, MDPI, vol. 11(1), pages 1-19, December.

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