Automatic domain ontology extraction for context-sensitive opinion mining
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.
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.
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|Item Type:||Conference Paper|
|Keywords:||consumer feedbacks, business strategy, opinion mining, fuzzy domain ontology|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2009 [please consult the authors]|
|Deposited On:||22 Jun 2011 03:06|
|Last Modified:||11 Aug 2011 05:22|
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