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Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

2018-09-12WS 2018Code Available0· sign in to hype

Peng Xu, Andrea Madotto, Chien-Sheng Wu, Ji Ho Park, Pascale Fung

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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.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
SST-2 Binary classificationGloVe+Emo2VecAccuracy82.3Unverified
SST-2 Binary classificationEmo2VecAccuracy81.2Unverified
SST-5 Fine-grained classificationGloVe+Emo2VecAccuracy43.6Unverified
SST-5 Fine-grained classificationEmo2VecAccuracy41.6Unverified

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