Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70703

Moderating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach

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Title:Moderating Effects of Time-Related Factors in Predicting the Helpfulness of Online Reviews: a Deep Learning Approach
Authors:Namvar, Morteza
Boyce, James
Sarna , Jatin
Zheng , Yuanyuan
Chua Yeow Kuan , Alton
show 1 moreAmeli, Sina
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Keywords:Social Media and e-Business Transformation
deep learning
helpfulness
online reviews
order
show 1 moretime interval
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Date Issued:05 Jan 2021
Abstract:Given the importance of online reviews, as shown by extensive research, we address the problem of predicting the helpfulness of online product reviews by developing a comprehensive research model guided by the theoretical foundations of signaling and social influence theories. We use review order and time interval to incorporate the moderating effects of the time-related variable on the reviewer’s valuation of products and the related details they provide. Applying deep learning techniques in text processing and model building on a dataset of 239297 reviews, the empirical findings represent strong support of the proposed approach and show its superior performance in predicting review helpfulness compared to current approaches. This research contributes to theory by analyzing online reviews from the points of two well-known information processing theories and contributes to practice by developing a model to sort the newly posted reviews.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/70703
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.092
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Social Media and e-Business Transformation


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