Detecting Product Adoption Intentions via Multiview Deep Learning

Zhu Zhang (), Xuan Wei (), Xiaolong Zheng (), Qiudan Li () and Daniel Dajun Zeng ()
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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

INFORMS Journal on Computing, 2022, vol. 34, issue 1, 541-556

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.

Keywords: web mining; business intelligence; intention detection; deep learning; social media analytics (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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