{"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/cascade-contextual-sarcasm-detection-in","title":"CASCADE: Contextual Sarcasm Detection in Online Discussion Forums","arxiv_id":"1805.06413","date":"2018-05-16","proceeding":"COLING 2018 8","authors":["Devamanyu Hazarika","Soujanya Poria","Sruthi Gorantla","Erik Cambria","Roger Zimmermann","Rada Mihalcea"],"abstract":"The literature in automated sarcasm detection has mainly focused on lexical,\nsyntactic and semantic-level analysis of text. However, a sarcastic sentence\ncan be expressed with contextual presumptions, background and commonsense\nknowledge. In this paper, we propose CASCADE (a ContextuAl SarCasm DEtector)\nthat adopts a hybrid approach of both content and context-driven modeling for\nsarcasm detection in online social media discussions. For the latter, CASCADE\naims at extracting contextual information from the discourse of a discussion\nthread. Also, since the sarcastic nature and form of expression can vary from\nperson to person, CASCADE utilizes user embeddings that encode stylometric and\npersonality features of the users. When used along with content-based feature\nextractors such as Convolutional Neural Networks (CNNs), we see a significant\nboost in the classification performance on a large Reddit corpus.","url_abs":"http://arxiv.org/abs/1805.06413v1","url_pdf":"http://arxiv.org/pdf/1805.06413v1.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":"cascade-contextual-sarcasm-detection-in","repo_url":"https://github.com/SenticNet/CASCADE--ContextuAl-SarCAsm-DEtector","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sarcasm-detection","task_name":"Sarcasm Detection"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sarcasm-detection-on-sarc-all-bal","task":"Sarcasm Detection","dataset":"SARC (all-bal)","model":"CASCADE","rank_in_archive_order":1,"of":2,"metrics":{"Accuracy":"77"},"uses_additional_data":false},{"leaderboard":"/sota/sarcasm-detection-on-sarc-pol-bal","task":"Sarcasm Detection","dataset":"SARC (pol-bal)","model":"CASCADE","rank_in_archive_order":2,"of":2,"metrics":{"Accuracy":"74"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.06413","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}