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 %DIFDELCMD < \\%%% %DIF < , a significant improvement of 4.66\\% over the CNN model built by the authors of UIT-VSMEC (59.74\\%).abstract 
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 %DIFDELCMD < \\%%% %DIF < , a significant improvement of 4.66\\% over the CNN model built by the authors of UIT-VSMEC (59.74\\%).abstract \%, a significant improvement of 4.66\% over the CNN model built by the authors of UIT-VSMEC (59.74\%).
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were trying to build a perfect automated system capable of detecting correct human emotion from text data
aim to build more accurate and robust emotion detection systems
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conducted
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benchmark dataset
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as the benchmark dataset. We explore
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propose
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based on Vietnamese social media characteristics
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\%, a significant improvement of 4.66\% over the CNN model built by the authors of UIT-VSMEC (59.74\%).