Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, Pascale Fung
Abstract
In this paper, we propose Emo2Vec which encodes emotional semantics into vectors. We train Emo2Vec by multi-task learning six different emotion-related tasks, including emotion/sentiment analysis, sarcasm classification, stress detection, abusive language classification, insult detection, and personality recognition. Our evaluation of Emo2Vec shows that it outperforms existing affect-related representations, such as Sentiment-Specific Word Embedding and DeepMoji embeddings with much smaller training corpora. When concatenated with GloVe, Emo2Vec achieves competitive performances to state-of-the-art results on several tasks using a simple logistic regression classifier.- Anthology ID:
- W18-6243
- Volume:
- Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Alexandra Balahur, Saif M. Mohammad, Veronique Hoste, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 292–298
- Language:
- URL:
- https://aclanthology.org/W18-6243
- DOI:
- 10.18653/v1/W18-6243
- Cite (ACL):
- Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, and Pascale Fung. 2018. Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 292–298, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training (Xu et al., WASSA 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-6243.pdf
- Code
- pxuab/emo2vec_wassa_paper
- Data
- SST, SST-2, SST-5