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The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is ex- pressed in a tweet. Compared to classifica- tion tasks that identify 1 among n emo- tions for a tweet, the present task can pro- vide more fine-grained (real-valued) sen- timent analysis. This paper presents a sys- tem that uses a bi-directional LSTM-CNN model to complete the competition task. Combining bi-directional LSTM and CNN, the prediction process considers both global information in a tweet and lo- cal important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Co- efficient.

YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model