{"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/a-deeper-look-into-sarcastic-tweets-using","title":"A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks","arxiv_id":"1610.08815","date":"2016-10-27","proceeding":"COLING 2016 12","authors":["Soujanya Poria","Erik Cambria","Devamanyu Hazarika","Prateek Vij"],"abstract":"Sarcasm detection is a key task for many natural language processing tasks.\nIn sentiment analysis, for example, sarcasm can flip the polarity of an\n\"apparently positive\" sentence and, hence, negatively affect polarity detection\nperformance. To date, most approaches to sarcasm detection have treated the\ntask primarily as a text categorization problem. Sarcasm, however, can be\nexpressed in very subtle ways and requires a deeper understanding of natural\nlanguage that standard text categorization techniques cannot grasp. In this\nwork, we develop models based on a pre-trained convolutional neural network for\nextracting sentiment, emotion and personality features for sarcasm detection.\nSuch features, along with the network's baseline features, allow the proposed\nmodels to outperform the state of the art on benchmark datasets. We also\naddress the often ignored generalizability issue of classifying data that have\nnot been seen by the models at learning phase.","url_abs":"http://arxiv.org/abs/1610.08815v2","url_pdf":"http://arxiv.org/pdf/1610.08815v2.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":"a-deeper-look-into-sarcastic-tweets-using","repo_url":"https://github.com/M-S-ALAM/Sarcastic-Comment-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-deeper-look-into-sarcastic-tweets-using","repo_url":"https://github.com/NamanJain2050/sarcasm-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-deeper-look-into-sarcastic-tweets-using","repo_url":"https://github.com/NamanJain2050/semeval-2014-task-9","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sarcasm-detection","task_name":"Sarcasm Detection"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"text-categorization","task_name":"Text Categorization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1610.08815","atlas_url":"https://app.syntology.ai/?focus=1610.08815","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}