Learning domain-specific sentiment lexicons for predicting product sales

Lau, Raymond, Zhang, Wenping, Bruza, Peter D., & Wong, K.F. (2011) Learning domain-specific sentiment lexicons for predicting product sales. In Proceeding of the 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE), IEEE, Beijing, China, pp. 131-138.

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Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web

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3 citations in Scopus
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ID Code: 51672
Item Type: Conference Paper
Refereed: Yes
Keywords: Business intelligence, Kullback-Leibler Divergence, Sentiment Analysis, Sentiment Lexicon , Statistical Learning
DOI: 10.1109/ICEBE.2011.55
ISBN: 9780769545189
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 17 Jul 2012 22:25
Last Modified: 16 Jul 2017 02:02

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