- 권한신청
- P-ISSN1013-0799
- E-ISSN2586-2073
- KCI
지도적 잠재의미색인(LSI)기법을 이용한 의견 문서 자동 분류에 관한 실험적 연구
An Experimental Study on Opinion Classification Using Supervised Latent Semantic Indexing(LSI)
정영미 (연세대학교)
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초록
본 연구에서는 의견이나 감정을 담고 있는 의견 문서들의 자동 분류 성능을 향상시키기 위하여 개념색인의 하나인 잠재의미색인 기법을 사용한 분류 실험을 수행하였다. 실험을 위해 수집한 1,000개의 의견 문서는 500개씩의 긍정 문서와 부정 문서를 포함한다. 의견 문서 텍스트의 형태소 분석을 통해 명사 형태의 내용어 집합과 용언, 부사, 어기로 구성되는 의견어 집합을 생성하였다. 각기 다른 자질 집합들을 대상으로 의견 문서를 분류한 결과 용어색인에서는 의견어 집합, 잠재의미색인에서는 내용어와 의견어를 통합한 집합, 지도적 잠재의미색인에서는 내용어 집합이 가장 좋은 성능을 보였다. 전체적으로 의견 문서의 자동 분류에서 용어색인 보다는 잠재의미색인 기법의 분류 성능이 더 좋았으며, 특히 지도적 잠재의미색인 기법을 사용할 경우 최고의 분류 성능을 보였다.
- keywords
- 의견 마이닝, 의견 자동분류, 개념색인, 용어색인, 잠재의미색인, 지도적 잠재의미색인, opinion mining, sentiment classification, LSI, supervised latent semantic indexing, latent semantic indexing, term indexing, opinion mining, sentiment classification, LSI, supervised latent semantic indexing, latent semantic indexing, term indexing
Abstract
The aim of this study is to apply latent semantic indexing(LSI) techniques for efficient automatic classification of opinionated documents. For the experiments, we collected 1,000 opinionated documents such as reviews and news, with 500 among them labelled as positive documents and the remaining 500 as negative. In this study, sets of content words and sentiment words were extracted using a POS tagger in order to identify the optimal feature set in opinion classification. Findings addressed that it was more effective to employ LSI techniques than using a term indexing method in sentiment classification. The best performance was achieved by a supervised LSI technique.
- keywords
- 의견 마이닝, 의견 자동분류, 개념색인, 용어색인, 잠재의미색인, 지도적 잠재의미색인, opinion mining, sentiment classification, LSI, supervised latent semantic indexing, latent semantic indexing, term indexing, opinion mining, sentiment classification, LSI, supervised latent semantic indexing, latent semantic indexing, term indexing
참고문헌
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