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Vol.46 No.1
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블로고스피어에서 주제에 관한 의견을 찾는 융합적 의견탐지방법

Fusion Approach to Targeted Opinion Detection in Blogosphere

한국도서관·정보학회지 / Journal of Korean Library and Information Science Society, (P)2466-2542;
2015, v.46 no.1, pp.321-344
https://doi.org/10.16981/kliss.46.1.201503.321
Yang, Kiduk (경북대학교)
Yang,, K. (2015). 블로고스피어에서 주제에 관한 의견을 찾는 융합적 의견탐지방법. 한국도서관·정보학회지, 46(1), 321-344, https://doi.org/10.16981/kliss.46.1.201503.321
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Abstract

This paper presents a fusion approach to sentiment detection that combines multiple sources of evidence to retrieve blogs that contain opinions on a specific topic. Our approach to finding opinionated blogs on topic consists of first applying traditional information retrieval methods to retrieve blogs on a given topic and then boosting the ranks of opinionated blogs based on the opinion scores computed by multiple sentiment detection methods. Our sentiment detection strategy, whose central idea is to rely on a variety of complementary evidences rather than trying to optimize the utilization of a single source of evidence, includes High Frequency module, which identifies opinions based on the frequency of opinion terms (i.e., terms that occur frequently in opinionated documents), Low Frequency module, which makes use of uncommon/rare terms (e.g., “sooo good”) that express strong sentiments, IU Module, which leverages n-grams with IU (I and you) anchor terms (e.g., I believe, You will love), Wilson’s lexicon module, which uses a collection-independent opinion lexicon constructed from Wilson’s subjectivity terms, and Opinion Acronym module, which utilizes a small set of opinion acronyms (e.g., imho). The results of our study show that combining multiple sources of opinion evidence is an effective method for improving opinion detection performance.

keywords
융합, 정보 검색, 의견 감지, Fusion, Information retrieval, Opinion detection

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