Improving Opinion Retrieval Based on Query-Specific Sentiment Lexicon

Lecture notes in computer sciencePublished 1 January 2009Open access

Seung‐Hoon Na, Yeha Lee, Sang-Hyob Nam, Jong-Hyeok Lee

DOI10.1007/978-3-642-00958-7_76

Source URL

Citations47

SJR quartileQ4

SJR score0.11

SNIP0.06

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TL;DR

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.

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.

Keywords

Computer Science

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