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한국어 트위터의 감정 분류를 위한 기계학습의 실증적 비교

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

  • 임좌상 (상명대학교 미디어소프트웨어학과) ;
  • 김진만 (상명대학교 일반대학원 컴퓨터과학과)
  • 투고 : 2014.01.15
  • 심사 : 2014.02.19
  • 발행 : 2014.02.28

초록

온라인에서의 글쓰기가 늘어나면서, 기계학습을 통해 이를 분류하는 연구가 늘고 있다. 그럼에도 불구하고 한국어로 작성된 마이크로블로그를 대상으로 한 연구는 많지 않다. 또한 통계적으로 기계학습을 평가한 연구를 찾아보기 힘들다. 본 논문에서는 트위터를 대상으로, 표본을 추출하고, 형태소와 음절을 자질로 사용하여 기계학습에 따라 감정을 분류하였다. 그 결과 약 76%정도 트위터에 포함된 감정이 분류되었다. Support Vector Machine이 Na$\ddot{i}$ve Bayes보다 정확했고, 선형모델도 비구조적인 텍스트 처리에 비선형모델에 상응하는 정확성을 보였다. 또한 형태소가 음절 자질에 비해 높은 정확성을 보이지 않았다.

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.

키워드

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