Effective Use of Linguistic Features for Sentiment Analysis of Korean

In this paper, we propose a new linguistic approach for sentiment analysis of Korean. In order to overcome shortcomings of previous works confined to statistical methods, we make effective use of various linguistic features reflecting the nature of Korean such as contextual intensifiers, contextual shifters, modal affixes, and the morphological dependency chunk structures. Moreover, unlike complex statistical formulae which are hard to understand, we use simple mathematical formulae in the process of term weighting. Through experiments of news corpus, we verify an improvement on the results of sentiment analysis of Korean in comparison to the experimental results using TFIDF as popular statistical method employing word frequency. This approach, especially the chunking method, will be beneficial to sentiment analysis of other morphologically rich languages like Japanese and Turkish.