A corpus-based approach to classifying emotions using Korean linguistic features

Recently, social network services have become the popular communication tools among internet and mobile users. And it has been shared various opinions, which could be included various emotions. Emotion analysis aims to extract various emotion information, such as joy, happy, funny, fear, sad, and lonely, and so on, from texts expressed in natural language. Previous studies about emotion analysis on texts written in Korean have focused generally on the basic sentiments such as positive/neutral/negative preferences or 4–10 emotion classes. In this paper, we propose an emotion analysis method based on supervised learning to classify various emotions from messages written in Korean. We had found the feature set optimized to each emotion class through evaluating the combinations of various linguistic features and built a model to classify the emotion using the optimized feature set. To do this, it was constructed the corpus that is manually annotated with 25 emotion classes. We performed a 10-fold cross variation experiment for evaluating the performance of the proposed method. Our method obtained F-value ranged from 73.1 to 98.0% for each of 25 emotion classes. The optimized feature sets for most of emotion classes include commonly word 2-gram, POS 1-gram, and character 1-gram feature.