Detecting Product Adoption Intentions via Multiview Deep Learning
Zhang, Zhu1,2; Wei, Xuan3; Zheng, Xiaolong1,2,4; Li, Qiudan1,2; Zeng, Daniel Dajun1,2,4
Source PublicationINFORMS JOURNAL ON COMPUTING
ISSN1091-9856
2021-09-14
Pages17
Corresponding AuthorZheng, Xiaolong(xiaolong.zheng@ia.ac.cn)
AbstractDetecting 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.
Keywordweb mining business intelligence intention detection deep learning social media analytics
DOI10.1287/ijoc.2021.1083
WOS KeywordSEARCH
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2020AAA0108401] ; Ministry of Science and Technology of China[2019QY (Y) 0101] ; Ministry of Science and Technology of China[2020AAA0103405] ; Science Fund for Creative Research Groups of the National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[71472175] ; National Natural Science Foundation of China[71974187] ; National Natural Science Foundation of China[61671450] ; National Natural Science Foundation of China[71902179] ; National Natural Science Foundation of China[72074209] ; National Natural Science Foundation of China[71825007] ; Strategic Priority Research Pro-gram of Chinese Academy of Sciences[XDA27030100] ; Research Foundation of SKL-MCCS for Young Scientists[20190212] ; Longhua District Science and Technology Innovation Fund[10162a20200617b70da63] ; National Science Foundation[1228509]
Funding OrganizationMinistry of Science and Technology of China ; Science Fund for Creative Research Groups of the National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Research Pro-gram of Chinese Academy of Sciences ; Research Foundation of SKL-MCCS for Young Scientists ; Longhua District Science and Technology Innovation Fund ; National Science Foundation
WOS Research AreaComputer Science ; Operations Research & Management Science
WOS SubjectComputer Science, Interdisciplinary Applications ; Operations Research & Management Science
WOS IDWOS:000708981400001
PublisherINFORMS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46231
Collection复杂系统管理与控制国家重点实验室_互联网大数据与信息安全
Corresponding AuthorZheng, Xiaolong
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab ofManagement & Control Complex Syst, Beijing 100190, Peoples R China
2.Shenzhen Artificial Intelligence & Data Sci Inst, Shenzhen 518129, Peoples R China
3.Shanghai Jiao Tong Univ, Antai Coll Econ & Management, Dept Informat Technol & Innovat, Shanghai 200030, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Zhu,Wei, Xuan,Zheng, Xiaolong,et al. Detecting Product Adoption Intentions via Multiview Deep Learning[J]. INFORMS JOURNAL ON COMPUTING,2021:17.
APA Zhang, Zhu,Wei, Xuan,Zheng, Xiaolong,Li, Qiudan,&Zeng, Daniel Dajun.(2021).Detecting Product Adoption Intentions via Multiview Deep Learning.INFORMS JOURNAL ON COMPUTING,17.
MLA Zhang, Zhu,et al."Detecting Product Adoption Intentions via Multiview Deep Learning".INFORMS JOURNAL ON COMPUTING (2021):17.
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