{"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/emosense-at-semeval-2019-task-3-bidirectional","title":"EmoSense at SemEval-2019 Task 3: Bidirectional LSTM Network for Contextual Emotion Detection in Textual Conversations","arxiv_id":null,"date":"2019-06-01","proceeding":"SEMEVAL 2019 6","authors":["Sergey Smetanin"],"abstract":"In this paper, we describe a deep-learning system for emotion detection in textual conversations that participated in SemEval-2019 Task 3 {``}EmoContext{''}. We designed a specific architecture of bidirectional LSTM which allows not only to learn semantic and sentiment feature representation, but also to capture user-specific conversation features. To fine-tune word embeddings using distant supervision we additionally collected a significant amount of emotional texts. The system achieved 72.59{\\%} micro-average F1 score for emotion classes on the test dataset, thereby significantly outperforming the officially-released baseline. Word embeddings and the source code were released for the research community.","url_abs":"https://aclanthology.org/S19-2034","url_pdf":"https://aclanthology.org/S19-2034.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":"emosense-at-semeval-2019-task-3-bidirectional","repo_url":"https://github.com/sismetanin/emosense-semeval2019-task3-emocontext","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}