{"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/modeling-rich-contexts-for-sentiment","title":"Modeling Rich Contexts for Sentiment Classification with LSTM","arxiv_id":"1605.01478","date":"2016-05-05","proceeding":null,"authors":["Minlie Huang","Yujie Cao","Chao Dong"],"abstract":"Sentiment analysis on social media data such as tweets and weibo has become a\nvery important and challenging task. Due to the intrinsic properties of such\ndata, tweets are short, noisy, and of divergent topics, and sentiment\nclassification on these data requires to modeling various contexts such as the\nretweet/reply history of a tweet, and the social context about authors and\nrelationships. While few prior study has approached the issue of modeling\ncontexts in tweet, this paper proposes to use a hierarchical LSTM to model rich\ncontexts in tweet, particularly long-range context. Experimental results show\nthat contexts can help us to perform sentiment classification remarkably\nbetter.","url_abs":"http://arxiv.org/abs/1605.01478v1","url_pdf":"http://arxiv.org/pdf/1605.01478v1.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":"modeling-rich-contexts-for-sentiment","repo_url":"https://github.com/gladisor/TextGenerator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"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}