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http://dx.doi.org/10.9717/kmms.2014.17.2.232

An Empirical Comparison of Machine Learning Models for Classifying Emotions in Korean Twitter  

Lim, Joa-Sang (상명대학교 미디어소프트웨어학과)
Kim, Jin-Man (상명대학교 일반대학원 컴퓨터과학과)
Publication Information
Journal of Korea Multimedia Society / v.17, no.2, 2014 , pp. 232-239 More about this Journal
Abstract
As online texts have been rapidly growing, their automatic classification gains more interest with machine learning methods. Nevertheless, comparatively few research could be found, aiming for Korean texts. Evaluating them with statistical methods are also rare. This study took a sample of tweets and used machine learning methods to classify emotions with features of morphemes and n-grams. As a result, about 76% of emotions contained in tweets was correctly classified. Of the two methods compared in this study, Support Vector Machines were found more accurate than Na$\ddot{i}$ve Bayes. The linear model of SVM was not inferior to the non-linear one. Morphological features did not contribute to accuracy more than did the n-grams.
Keywords
Machine Learning; Support Vector Machine; Na$\ddot{i}$ve Bayes; Twitter emotion classification;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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