在线商品评论有用性主题分析及预测研究
Online product reviews helpfulness prediction based on topic analysis
查看参考文献34篇
|
文摘
|
随着电子商务的飞速发展,电商平台上的在线商品评论成为消费者在线购物时做出购买决策的重要参考,同时也是平台商家获取在线消费者真实关切的重要信息来源.然而,海量的良莠不齐的在线商品评论使得消费者和商家很难从中获取有价值的高质量信息.一方面,本文在经典的主题分析LDA模型的基础之上提出了一种基于评论有用性的主题分析模型,即Help-LDA模型.相比与假定每条评论具有同等重要程度的LDA模型,Help-LDA模型根据评论有用性对不用评论赋予不同的权重,进而从有用性较高的评论中抽取出对于消费者更有用的决策信息.另一方面,本文基于Help-LDA模型提出了新的评论文本表示方法,并结合SVM方法进行评论有用性预测.通过收集大众点评网站在线评论进行的实验表明,Help-LDA模型能够从电商评论中高质量抽取在线消费者对于商家商品和服务的真实关切.并且基于Help-LDA模型的评论文本表示结合SVM方法能够显著提升在线评论有用性预测性能. |
|
其他语种文摘
|
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. |
|
来源
|
系统工程理论与实践
,2022,42(10):2757-2768 【核心库】
|
|
DOI
|
10.12011/SETP2021-1206
|
|
关键词
|
在线商品评论
;
主题分析
;
评论有用性
;
评论主题提取
;
有用性预测
|
|
地址
|
1.
北京工业大学经济与管理学院, 北京, 100124
2.
中国民航信息网络股份有限公司大数据技术部, 北京, 101318
3.
浙江工商大学工商管理学院, 杭州, 310018
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-6788 |
|
学科
|
自动化技术、计算机技术 |
|
基金
|
国家自然科学基金
;
北京市自然科学基金
;
国家社科基金一般项目
|
|
文献收藏号
|
CSCD:7328037
|
参考文献 共
34
共2页
|
|
1.
中国互联网络信息中心.
第47次中国互联网络发展状况统计报告,2021
|
CSCD被引
26
次
|
|
|
|
|
2.
张文. 基于Co-training协同训练的在线虚假评论识别研究.
系统工程理论与实践,2020,40(10):2669-2683
|
CSCD被引
3
次
|
|
|
|
|
3.
Cheung C M K. The impact of electronic word-of-mouth communication: A literature analysis and integrative model.
Decision Support Systems,2012,54(1):461-470
|
CSCD被引
16
次
|
|
|
|
|
4.
Saumya S. Ranking online consumer reviews.
Electronic Commerce Research and Applications,2018,29:78-89
|
CSCD被引
2
次
|
|
|
|
|
5.
Qi J. Mining customer requirements from online reviews: A product improvement perspective.
Information & Management,2016,53:951-963
|
CSCD被引
27
次
|
|
|
|
|
6.
Lorenzo-Romero C. Co-creation: Customer integration in social media based product and service development.
Procedia-Social and Behavioral Sciences,2014,148:383-396
|
CSCD被引
2
次
|
|
|
|
|
7.
Netzer O. Mine your own business: Market-structure surveillance through text mining.
Market Science,2012,31(3):521-543
|
CSCD被引
9
次
|
|
|
|
|
8.
Lee S. Predicting the helpfulness of online reviews using multilayer perceptron neural networks.
Expert Systems with Applications,2014,41(6):3041-3046
|
CSCD被引
10
次
|
|
|
|
|
9.
钱宇. 海量用户评论在APP更新设计中的参与作用挖掘.
系统工程理论与实践,2021,41(3):554-564
|
CSCD被引
5
次
|
|
|
|
|
10.
国显达. 基于CNN-BiLSTM的消费者网络评论情感分析.
系统工程理论与实践,2020,40(3):653-663
|
CSCD被引
17
次
|
|
|
|
|
11.
Ngo-Ye T L. Predicting the helpfulness of online reviews using a scripts-enriched text regression model.
Expert Systems with Applications,2017,71:98-110
|
CSCD被引
3
次
|
|
|
|
|
12.
Singh J P. Predicting the "helpfulness" of online consumer reviews.
Journal of Business Research,2017,70:346-355
|
CSCD被引
6
次
|
|
|
|
|
13.
Du J. Neighbor-aware review helpfulness prediction.
Decision Support Systems,2021,148:113581
|
CSCD被引
3
次
|
|
|
|
|
14.
Qazi A. A concept-level approach to the analysis of online review helpfulness.
Computers in Human Behavior,2016,58:75-81
|
CSCD被引
7
次
|
|
|
|
|
15.
Zhang W. A comparative study of TF*IDF, LSI and multi-words for text classification.
Expert Systems with Applications,2011,38(3):2758-2765
|
CSCD被引
31
次
|
|
|
|
|
16.
Hofmann T. Probabilistic latent semantic indexing.
Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,1999:50-57
|
CSCD被引
70
次
|
|
|
|
|
17.
张涛. 基于网络浏览行为的小众领域用户画像建模.
系统工程理论与实践,2020,40(3):641-652
|
CSCD被引
1
次
|
|
|
|
|
18.
Mudambi S M. What makes a helpful online review? A study of customer reviews on Amazon.com.
MIS Quarterly,2010,34(1):185-200
|
CSCD被引
42
次
|
|
|
|
|
19.
Ren G. Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews.
Information Processing & Management,2019,56(4):1425-1438
|
CSCD被引
7
次
|
|
|
|
|
20.
Karimi S. Online review helpfulness: Impact of reviewer profile image.
Decision Support Systems,2017,96:39-48
|
CSCD被引
4
次
|
|
|
|
|
了解气象卫星更多信息，请访问