Please use this identifier to cite or link to this item:
|Title:||Improving opinion retrieval based on query-specific sentiment lexicon||Authors:||Na, S.-H.
|Issue Date:||2009||Citation:||Na, S.-H.,Lee, Y.,Nam, S.-H.,Lee, J.-H. (2009). Improving opinion retrieval based on query-specific sentiment lexicon. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5478 LNCS : 734-738. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-00958-7_76||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 topics1. © Springer-Verlag Berlin Heidelberg 2009.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/41079||ISBN:||3642009573||ISSN:||03029743||DOI:||10.1007/978-3-642-00958-7_76|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 10, 2019
checked on Jul 5, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.