Paper Info
Title
Turning Online Product Reviews To Customer Knowledge: A Semantic-Based Sentiment Classification Approach
Abstract
Many product review websites have been established (e.g., epinion.com, Rateitall.com) for collecting user reviews for a variety of products. In addition, it has also become a common practice for merchants or product manufacturers to setup online forums that allow their customers to provide reviews or express opinions on products they are interested or have purchased To facilitate merchants, product manufacturers, and customers in exploiting online product reviews for their marketing, product design, or purchasing decision making, classification of the products reviews into positive and negative categories is essential. In this study, we propose a Semantic-based Sentiment Classification (SSC) technique that constructs from a training set of precategorized product reviews a sentiment classification model on the basis of a collection of positive and negative cite features. Furthermore, the proposed SSC technique includes a semantic expansion mechanism that uses WordNet for expanding the given set of positive and negative cite features. On the basis of three product review corpora, our empirical evaluation results suggest that the proposed SSC technique achieves higher classification effectiveness than the traditional syntactic-level sentiment classification technique does. Moreover, the SSC technique with the use of few seed features (e.g., 10 or 20) can result in comparable classification effectiveness to that attained by the use of a comprehensive list of positive and negative cite features (a total of 4206 words) defined in the General Inquirer.
Year
Venue
Keywords
2006
PACIFIC ASIA CONFERENCE ON INFORMATION SYSTEMS 2006, SECTIONS 1-8
Sentiment Classification, Semantic Cue Feature, Product Review, Opinion Mining
DocType
Citations 
PageRank 
Conference
5
0.43
References 
Authors
10
3
Name
Order
Citations
PageRank
Chih-ping Wei174374.20
Chin-Sheng Yang2948.35
Chun-Neng Huang31325.55