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Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon SCIE SCOPUS

Title
Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon
Authors
Na S.-HLee YNam S.-HLee J.-H.
Date Issued
2009-04
Publisher
SPRINGER
Abstract
Lexicon-based approaches have been widely used for opinion retrieval due to their simplicity. However, no previous work has focused on the domain-dependency problem in opinion lexicon construction. This paper proposes simple feedback-style learning for query-specific opinion lexicon using the set of top-retrieved documents in response to a query. The proposed learning starts from the initial domain-independent general lexicon and creates a query-specific lexicon by re-updating the opinion probability of the initial lexicon based on top-retrieved documents. Experimental results on recent TREC test sets show that the query-specific lexicon provides a significant improvement over previous approaches, especially in BLOG-06 topics.
URI
https://oasis.postech.ac.kr/handle/2014.oak/35949
DOI
10.1007/978-3-642-00958-7_76
ISSN
0302-9743
Article Type
Article
Citation
LECTURE NOTES IN COMPUTER SCIENCE, vol. 5478, page. 734 - 738, 2009-04
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이종혁LEE, JONG HYEOK
Grad. School of AI
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