Attribute-Sentiment Pair Correlation Model Based on Online User Reviews

With the popularization of Internet applications and the rapid development of e-commerce, online shopping has become a widespread and important pattern of consumption. Online user comments are an important data asset on e-commerce sites and have a great potential value for online users and merchants...

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Bibliographic Details
Published in:Journal of sensors 2019-01, Vol.2019 (2019), p.1-11
Main Authors: Liu, Shaohui, Chen, Jinpeng, Wu, Ji, Fu, Xiang Ling
Format: Article
Language:English
Subjects:
Algorithms
Cellular telephones
Cluster analysis
Clustering
Collaboration
Consumers
Data analysis
Dictionaries
Electronic commerce
International conferences
Market entry
Product development
Product reviews
Semantics
Sentiment analysis
World Wide Web
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Summary:With the popularization of Internet applications and the rapid development of e-commerce, online shopping has become a widespread and important pattern of consumption. Online user comments are an important data asset on e-commerce sites and have a great potential value for online users and merchants. However, accurate and effective extraction of the characteristics of products and users’ sentiment evaluation from a tremendous amount of comments is a significant challenge. Based on the concept of the LinLog energy model, this paper proposes an online review attribute-sentiment pair correlation model that evaluates user comments. After preprocessing the comment data of mobile phones and constructing an attribute dictionary, the proposed model conducts a clustering analysis of attributes and sentiment pairs to gain accurate assessment of attributes in order to explore potential information from user comments. Experiments conducted on one real-world dataset with comprehensive measurements verify the efficacy of the proposed model.
ISSN:1687-725X
1687-7268
DOI:10.1155/2019/2456752