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A Korean Sentence and Document Sentiment Classification System Using Sentiment Features  

Hwang, Jaw-Won (동아대학교 컴퓨터공학과)
Ko, Young-Joong (동아대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.14, no.3, 2008 , pp. 336-340 More about this Journal
Abstract
Sentiment classification is a recent subdiscipline of text classification, which is concerned not with the topic but with opinion. In this paper, we present a Korean sentence and document classification system using effective sentiment features. Korean sentiment classification starts from constructing effective sentiment feature sets for positive and negative. The synonym information of a English word thesaurus is used to extract effective sentiment features and then the extracted English sentiment features are translated in Korean features by English-Korean dictionary. A sentence or a document is represented by using the extracted sentiment features and is classified and evaluated by SVM(Support Vector Machine).
Keywords
Sentiment Classification; Sentiment Feature Extraction; SVM;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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