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.

本文提出了 Emo2Vec 模型，将情感语义编码为向量。通过训练多任务的方式进行六种不同的情感相关任务来训练 Emo2Vec，包括情感 / 情绪分析，讽刺分类，压力检测，恶意语言分类，侮辱探测和人格识别。我们的评估表明，Emo2Vec 在比现有的情感相关表示法（例如 Sentiment-Specific Word Embedding 和 DeepMoji embeddings）更小的训练语料库上表现更好。当与 GloVe 拼接时，Emo2Vec 在使用简单逻辑回归分类器的几项任务中取得了与现有最优结果相竞争的表现。