Textual emotion recognition has been a promising research topic in recent
years. Many researchers aim to build more accurate and robust emotion detection
systems. In this paper, we conduct several experiments to indicate how data
pre-processing affects a machine learning method on textual emotion
recognition. These experiments are performed on the Vietnamese Social Media
Emotion Corpus (UIT-VSMEC) as the benchmark dataset. We explore Vietnamese
social media characteristics to propose 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 based on Vietnamese social media
characteristics, 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%).

研究表明，适当的基于越南社交媒体特征的预处理技术结合情感上下文的关键子句提取，可显著提高 MLR 的性能，达到最佳 F1 得分 64.40%, 比 UIT-VSMEC 作者构建的 CNN 模型 (59.74%) 提高了 4.66%。