A Korean Sentence and Document Sentiment Classification System Using Sentiment Features
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TL;DR
This paper presents a Korean sentence and document classification system using effective sentiment features, a recent subdiscipline of text classification, concerned not with the topic but with opinion.
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).