Authors:
Christopher C. Yang
1
and
Y. C. Wong
2
Affiliations:
1
College of Information Science and Technology, Drexel University; Chinese University of Hong Kong, Hong Kong
;
2
Chinese University of Hong Kong, Hong Kong
Keyword(s):
Class association rules mining, market intelligence, Web content mining, knowledge management.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
The Web has provided an excellent platform for business to consumer (B2C) electronic commerce. B2C electronic commerce offers convenience, choice, lower cost and customization to consumers.The electronic shopping platform allows consumers to make intelligent comparison and purchasing decision on consumer products. In addition to comparing product specifications as described on electronic catalogue for better purchasing decision, consumers also hunger for consumer reviews to identify the best products that fit their preferences. For example, a professional photographer would like to identify a camera with lens of high quality and zooming power but a general user may like to find a camera that is cheap, light, and with a large LCD screen. When consumers take consumer reviews as reference, they are interested in both opinion orientation and product features that they are describing. Most of the prior works on consumer opinions mining focus on identifying opinion orientation. Some recent
works have started to classify product features but heavily rely on linguistic and natural language processing techniques. However, the writing in consumer reviews is usually less formal and many of them do not conform to the grammatical rules. Therefore, the linguistic and language processing approach is not satisfactory. In this work, we propose a sentiment analysis system to classify product features of consumer reviews by mining class association rules. The experimental result shows that the performance is promising. The content mining approach outperforms the natural language processing approach.
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