Automatic domain ontology extraction for context-sensitive opinion mining
Data(s) |
2009
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Resumo |
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline. |
Formato |
application/pdf |
Identificador | |
Publicador |
AIS Electronic Library |
Relação |
http://eprints.qut.edu.au/42065/1/42065.pdf http://www.icis09.org/ Lau, Raymond Y.K., Lai, Chapmann C.L., Ma, Jian, & Li, Yuefeng (2009) Automatic domain ontology extraction for context-sensitive opinion mining. In ICIS 2009 Proceedings, AIS Electronic Library, Phoenix, Arizona, pp. 35-53. |
Direitos |
Copyright 2009 [please consult the authors] |
Fonte |
Faculty of Science and Technology |
Palavras-Chave | #080600 INFORMATION SYSTEMS #consumer feedbacks #business strategy #opinion mining #fuzzy domain ontology |
Tipo |
Conference Paper |