Papers › Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, Pascale Fung
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sentiment Analysis | SST-2 Binary classification | GloVe+Emo2Vec | Accuracy | 82.3 | #80 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-2 Binary classification | Emo2Vec | Accuracy | 81.2 | #81 of 87 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | GloVe+Emo2Vec | Accuracy | 43.6 | #28 of 31 | Archive leaderboard | report |
| Sentiment Analysis | SST-5 Fine-grained classification | Emo2Vec | Accuracy | 41.6 | #29 of 31 | Archive leaderboard | report |
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Methods
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