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

• Yuanye He ^[1] ; Liang-Chih Yu ^[1] ; K. Robert Lai ^[1] ; Weiyi Liu ^[2]
  1. [1] Yuan Ze University

     Yuan Ze University

     Taoyuan District, Taiwán

     
  2. [2] Yunnan University

     Yunnan University

     China

     
• Localización: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2017: Proceedings of the Workshop / Alexandra Balahur Dobrescu (ed. lit.), Saif M. Mohammad (ed. lit.), Erik van der Goot (ed. lit.), 2017, ISBN 978-1-945626-95-1, págs. 238-242
• Idioma: inglés
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• Resumen

  • 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.