Abstract
This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emo-tions need to be quantified into a real valued score in [0, 1]. We propose an en-semble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTM-CNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 17th among 48 systems in EI-reg and 19th among 38 systems in V-reg.- Anthology ID:
- S18-1046
- Volume:
- Proceedings of the 12th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marianna Apidianaki, Saif M. Mohammad, Jonathan May, Ekaterina Shutova, Steven Bethard, Marine Carpuat
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 313–318
- Language:
- URL:
- https://preview.aclanthology.org/remove-affiliations/S18-1046/
- DOI:
- 10.18653/v1/S18-1046
- Cite (ACL):
- Zewen Chi, Heyan Huang, Jiangui Chen, Hao Wu, and Ran Wei. 2018. Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets. In Proceedings of the 12th International Workshop on Semantic Evaluation, pages 313–318, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets (Chi et al., SemEval 2018)
- PDF:
- https://preview.aclanthology.org/remove-affiliations/S18-1046.pdf