{"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/context-dependent-sentiment-analysis-in-user","title":"Context-Dependent Sentiment Analysis in User-Generated Videos","arxiv_id":null,"date":"2017-07-01","proceeding":"ACL 2017 7","authors":["Soujanya Poria","Erik Cambria","Devamanyu Hazarika","Navonil Majumder","Amir Zadeh","Louis-Philippe Morency"],"abstract":"Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10{\\%} performance improvement over the state of the art and high robustness to generalizability.","url_abs":"https://aclanthology.org/P17-1081","url_pdf":"https://aclanthology.org/P17-1081.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":"context-dependent-sentiment-analysis-in-user","repo_url":"https://github.com/senticnet/sc-lstm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"context-dependent-sentiment-analysis-in-user","repo_url":"https://github.com/soujanyaporia/multimodal-sentiment-analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"emotion-recognition-in-conversation","task_name":"Emotion Recognition in Conversation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multimodal-emotion-recognition","task_name":"Multimodal Emotion Recognition"},{"task_slug":"multimodal-sentiment-analysis","task_name":"Multimodal Sentiment Analysis"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"sarcasm-detection","task_name":"Sarcasm Detection"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-recognition-in-conversation-on-cped","task":"Emotion Recognition in Conversation","dataset":"CPED","model":"bcLSTM","rank_in_archive_order":3,"of":11,"metrics":{"Accuracy of Sentiment":"49.65","Macro-F1 of Sentiment":"45.40"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on","task":"Emotion Recognition in Conversation","dataset":"IEMOCAP","model":"bc-LSTM+Att","rank_in_archive_order":57,"of":59,"metrics":{"Accuracy":"59.09","Macro-F1":"56.52","Weighted-F1":"58.54"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-recognition-in-conversation-on-meld","task":"Emotion Recognition in Conversation","dataset":"MELD","model":"bc-LSTM+Att","rank_in_archive_order":66,"of":68,"metrics":{"Accuracy":"57.50","Weighted-F1":"56.44"},"uses_additional_data":false},{"leaderboard":"/sota/multimodal-sentiment-analysis-on-mosi","task":"Multimodal Sentiment Analysis","dataset":"MOSI","model":"bc-LSTM","rank_in_archive_order":9,"of":11,"metrics":{"Accuracy":"80.3%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}