Papers › Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training

12 Sep 2018WS 2018 10arXiv:1809.04505archive 2025-07-28

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

pxuab/emo2vec_wassa_paper officialmentioned in paperpytorch report

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Tasks

Abusive LanguageClassificationGeneral ClassificationMulti-Task LearningSentiment Analysisregression

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

Logistic Regression

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