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Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
2018
Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
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
doi:10.18653/v1/w18-6243
dblp:conf/wassa/XuMWPF18
fatcat:j7nwip3fnvh6hmypnukl37ut5q