Automatic construction of domain-specific sentiment lexicon for context sensitive opinion retrieval

Lau, Raymond Y. K. and Zhang, Wenping and Tao, Xiaohui (2012) Automatic construction of domain-specific sentiment lexicon for context sensitive opinion retrieval. In: 4th IEEE International Conference on Computer Science and Information Technology (ICCSIT 2011), 10-12 June 2011, Chengdu, China.

Official URL: http://www.iccsit.org/

Abstract

Existing opinion retrieval systems often employ manually
constructed generic sentiment lexicons to identify sentiment
words embedded in opinionated texts such as product
reviews or blog posts. 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. The main contribution of this
paper is the development of a statistical learning method
for the automatic construction of domain-specific sentiment
lexicons to enhance cross-domain opinion retrieval. 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. Our research
opens the door to the development of more effective opinion
retrieval system to extract business intelligence from huge
number opinionated expressions posted to the Web.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version, in accordance with the copyright policy of the publisher. Series: IPCSIT (International Proceedings of Computer Science and Information Technology) vol. 51, 2012 [not in index LB].
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 26 May 2016 05:35
Last Modified: 04 Jul 2017 23:21
Uncontrolled Keywords: sentiment lexicon, opinion retrieval, statistical learning, business intelligence
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
08 Information and Computing Sciences > 0807 Library and Information Studies > 080704 Information Retrieval and Web Search
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.7763/IPCSIT
URI: http://eprints.usq.edu.au/id/eprint/20184

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