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|Title:||Improving opinion retrieval based on query-specific sentiment lexicon|
|Authors:||Na, S.-H. |
|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)|
|Appears in Collections:||Staff Publications|
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