{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/emo2vec-learning-generalized-emotion","title":"Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training","arxiv_id":"1809.04505","date":"2018-09-12","proceeding":"WS 2018 10","authors":["Peng Xu","Andrea Madotto","Chien-Sheng Wu","Ji Ho Park","Pascale Fung"],"abstract":"In this paper, we propose Emo2Vec which encodes emotional semantics into\nvectors. We train Emo2Vec by multi-task learning six different emotion-related\ntasks, including emotion/sentiment analysis, sarcasm classification, stress\ndetection, abusive language classification, insult detection, and personality\nrecognition. Our evaluation of Emo2Vec shows that it outperforms existing\naffect-related representations, such as Sentiment-Specific Word Embedding and\nDeepMoji embeddings with much smaller training corpora. When concatenated with\nGloVe, Emo2Vec achieves competitive performances to state-of-the-art results on\nseveral tasks using a simple logistic regression classifier.","url_abs":"http://arxiv.org/abs/1809.04505v1","url_pdf":"http://arxiv.org/pdf/1809.04505v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"emo2vec-learning-generalized-emotion","repo_url":"https://github.com/pxuab/emo2vec_wassa_paper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"abusive-language","task_name":"Abusive Language"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sentiment-analysis-on-sst-2-binary","task":"Sentiment Analysis","dataset":"SST-2 Binary classification","model":"GloVe+Emo2Vec","rank_in_archive_order":80,"of":87,"metrics":{"Accuracy":"82.3"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-sst-2-binary","task":"Sentiment Analysis","dataset":"SST-2 Binary classification","model":"Emo2Vec","rank_in_archive_order":81,"of":87,"metrics":{"Accuracy":"81.2"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-sst-5-fine-grained","task":"Sentiment Analysis","dataset":"SST-5 Fine-grained classification","model":"GloVe+Emo2Vec","rank_in_archive_order":28,"of":31,"metrics":{"Accuracy":"43.6"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-sst-5-fine-grained","task":"Sentiment Analysis","dataset":"SST-5 Fine-grained classification","model":"Emo2Vec","rank_in_archive_order":29,"of":31,"metrics":{"Accuracy":"41.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.04505","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}