Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese

Textual emotion recognition has been a promising research topic in recent years. Many researchers were trying to build a perfect automated system capable of detecting correct human emotion from text data. In this paper, we conducted several experiments to indicate how the data pre-processing affects a machine learning method on textual emotion recognition. These experiments were performed on the benchmark dataset Vietnamese Social Media Emotion Corpus (UIT-VSMEC). We explored Vietnamese social media characteristics to proposed different pre-processing techniques, and key-clause extraction with emotional context to improve the machine performance on UIT-VSMEC. Our experimental evaluation shows that with appropriate pre-processing techniques, Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40\%, a significant improvement of 4.66\% over the CNN model built by the authors of UIT-VSMEC (59.74\%).