Online product reviews helpfulness prediction based on topic analysis

被引：0

作者：

Zhang W. ^{[1
]}

Wang Q. ^{[1
]}

Du Y. ^{[2
]}

Nie K. ^{[3
]}

Li J. ^{[1
]}

机构：

[1] College of Economics and Management, Beijing University of Technology, Beijing

[2] TravelSky Technology Limited, Beijing

[3] College of Business Management, Zhejiang Gongshang University, Hangzhou

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2022年 / 42卷 / 10期

基金：

中国国家自然科学基金;

关键词：

help-LDA; online reviews; review helpfulness; review helpfulness prediction; topic modeling;

D O I：

10.12011/SETP2021-1206

中图分类号：

学科分类号：

摘要：

With the prosperity of e-commerce, online reviews have become an important information source for both the online consumers and vendors in their decision making. However, with the marvelous reviews on the e-commerce platform, it is hard for consumers and vendors to acquire valuable information to support their decision making. This paper proposes a novel topic model called help-LDA by extending the classic LDA model with considering the helpfulness of online reviews. On the one hand, the proposed help-LDA model can extract helpful topics from online reviews. On the other hand, the proposed help-LDA model can be used for online review representation with goal of predicting the helpfulness of online reviews. With the real data collected from Dianping.com, we conduct extensive experiments to compare the proposed help-LDA model and the baseline models in topic modeling and helpfulness prediction of online reviews. The experimental results demonstrate the superiority of the proposed help-LDA model over the baseline models. © 2022 Systems Engineering Society of China. All rights reserved.

引用

页码：2757 / 2768

页数：11

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