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

Yuanye He, Liang-Chih Yu, K. Robert Lai, Weiyi Liu


Abstract
The EmoInt-2017 task aims to determine a continuous numerical value representing the intensity to which an emotion is expressed in a tweet. Compared to classification tasks that identify 1 among n emotions for a tweet, the present task can provide more fine-grained (real-valued) sentiment analysis. This paper presents a system 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 local important information. The proposed method ranked sixth among twenty-one teams in terms of Pearson Correlation Coefficient.
Anthology ID:
W17-5233
Volume:
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Editors:
Alexandra Balahur, Saif M. Mohammad, Erik van der Goot
Venue:
WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
238–242
Language:
URL:
https://aclanthology.org/W17-5233
DOI:
10.18653/v1/W17-5233
Bibkey:
Cite (ACL):
Yuanye He, Liang-Chih Yu, K. Robert Lai, and Weiyi Liu. 2017. YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model. In Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 238–242, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
YZU-NLP at EmoInt-2017: Determining Emotion Intensity Using a Bi-directional LSTM-CNN Model (He et al., WASSA 2017)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/W17-5233.pdf