{"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/seernet-at-emoint-2017-tweet-emotion","title":"Seernet at EmoInt-2017: Tweet Emotion Intensity Estimator","arxiv_id":"1708.06185","date":"2017-08-21","proceeding":"WS 2017 9","authors":["Venkatesh Duppada","Sushant Hiray"],"abstract":"The paper describes experiments on estimating emotion intensity in tweets\nusing a generalized regressor system. The system combines lexical, syntactic\nand pre-trained word embedding features, trains them on general regressors and\nfinally combines the best performing models to create an ensemble. The proposed\nsystem stood 3rd out of 22 systems in the leaderboard of WASSA-2017 Shared Task\non Emotion Intensity.","url_abs":"http://arxiv.org/abs/1708.06185v1","url_pdf":"http://arxiv.org/pdf/1708.06185v1.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":"seernet-at-emoint-2017-tweet-emotion","repo_url":"https://github.com/SEERNET/EmoInt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}