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

被引：0

作者：

Namvar, Morteza ^{[1
]}

Boyce, James ^{[1
]}

Zheng, Yuanyuan ^{[2
]}

Sarna, Jatin ^{[1
]}

Kuan, Alton Chua Yeow ^{[3
]}

Ameli, Sina ^{[4
]}

机构：

[1] Univ Queensland, Brisbane, Qld, Australia

[2] TAO Solut, Toronto, ON, Canada

[3] Nanyang Technol Univ, Singapore, Singapore

[4] Youi Insurance, Sippy Downs, Qld, Australia

来源：

PROCEEDINGS OF THE 54TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES | 2021年

关键词：

NEGATIVITY BIAS; SENTIMENT; ENGAGEMENT;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 a 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.

引用

页码：754 / 762

页数：9

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