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Detecting Product Adoption Intentions via Multiview Deep Learning

Author

Listed:
  • Zhu Zhang

    (State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Shenzhen Artificial Intelligence and Data Science Institute (Longhua), Shenzhen 518129, China)

  • Xuan Wei

    (Department of Information, Technology, and Innovation, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Xiaolong Zheng

    (State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Shenzhen Artificial Intelligence and Data Science Institute (Longhua), Shenzhen 518129, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China)

  • Qiudan Li

    (State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Shenzhen Artificial Intelligence and Data Science Institute (Longhua), Shenzhen 518129, China)

  • Daniel Dajun Zeng

    (State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Shenzhen Artificial Intelligence and Data Science Institute (Longhua), Shenzhen 518129, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China)

Abstract

Detecting product adoption intentions on social media could yield significant value in a wide range of applications, such as personalized recommendations and targeted marketing. In the literature, no study has explored the detection of product adoption intentions on social media, and only a few relevant studies have focused on purchase intention detection for products in one or several categories. Focusing on a product category rather than a specific product is too coarse-grained for precise advertising. Additionally, existing studies primarily focus on using one type of text representation in target social media posts, ignoring the major yet unexplored potential of fusing different text representations. In this paper, we first formulate the problem of product adoption intention mining and demonstrate the necessity of studying this problem and its practical value. To detect a product adoption intention for an individual product, we propose a novel and general multiview deep learning model that simultaneously taps into the capability of multiview learning in leveraging different representations and deep learning in learning latent data representations using a flexible nonlinear transformation. Specifically, the proposed model leverages three different text representations from a multiview perspective and takes advantage of local and long-term word relations by integrating convolutional neural network (CNN) and long short-term memory (LSTM) modules. Extensive experiments on three Twitter datasets demonstrate the effectiveness of the proposed multiview deep learning model compared with the existing benchmark methods. This study also significantly contributes research insights to the literature about intention mining and provides business value to relevant stakeholders such as product providers.

Suggested Citation

  • Zhu Zhang & Xuan Wei & Xiaolong Zheng & Qiudan Li & Daniel Dajun Zeng, 2022. "Detecting Product Adoption Intentions via Multiview Deep Learning," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 541-556, January.
  • Handle: RePEc:inm:orijoc:v:34:y:2022:i:1:p:541-556
    DOI: 10.1287/ijoc.2021.1083
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    References listed on IDEAS

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    1. Yanwu Yang & Daniel Zeng & Yinghui Yang & Jie Zhang, 2015. "Optimal Budget Allocation Across Search Advertising Markets," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 285-300, May.
    2. Xiao Fang & Paul Jen-Hwa Hu & Zhepeng (Lionel) Li & Weiyu Tsai, 2013. "Predicting Adoption Probabilities in Social Networks," Information Systems Research, INFORMS, vol. 24(1), pages 128-145, March.
    3. Xin Li & Kun Chen & Sherry X. Sun & Terrance Fung & Huaiqing Wang & Daniel D. Zeng, 2016. "A Commonsense Knowledge-Enabled Textual Analysis Approach for Financial Market Surveillance," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 278-294, May.
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    Cited by:

    1. Xu, Qianwen Ariel & Jayne, Chrisina & Chang, Victor, 2024. "An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

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