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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/W17-5233.pdf