Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Sentiment Analysis SST-2 Binary classification Emo2Vec Accuracy 81.2 # 82
Sentiment Analysis SST-2 Binary classification GloVe+Emo2Vec Accuracy 82.3 # 81
Sentiment Analysis SST-5 Fine-grained classification GloVe+Emo2Vec Accuracy 43.6 # 28
Sentiment Analysis SST-5 Fine-grained classification Emo2Vec Accuracy 41.6 # 29

Methods