{"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/twise-at-semeval-2016-task-4-twitter","title":"TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification","arxiv_id":"1606.04351","date":"2016-06-14","proceeding":"SEMEVAL 2016 6","authors":["Georgios Balikas","Massih-Reza Amini"],"abstract":"This paper describes the participation of the team \"TwiSE\" in the SemEval\n2016 challenge. Specifically, we participated in Task 4, namely \"Sentiment\nAnalysis in Twitter\" for which we implemented sentiment classification systems\nfor subtasks A, B, C and D. Our approach consists of two steps. In the first\nstep, we generate and validate diverse feature sets for twitter sentiment\nevaluation, inspired by the work of participants of previous editions of such\nchallenges. In the second step, we focus on the optimization of the evaluation\nmeasures of the different subtasks. To this end, we examine different learning\nstrategies by validating them on the data provided by the task organisers. For\nour final submissions we used an ensemble learning approach (stacked\ngeneralization) for Subtask A and single linear models for the rest of the\nsubtasks. In the official leaderboard we were ranked 9/35, 8/19, 1/11 and 2/14\nfor subtasks A, B, C and D respectively.\\footnote{We make the code available\nfor research purposes at\n\\url{https://github.com/balikasg/SemEval2016-Twitter\\_Sentiment\\_Evaluation}.}","url_abs":"http://arxiv.org/abs/1606.04351v1","url_pdf":"http://arxiv.org/pdf/1606.04351v1.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":"twise-at-semeval-2016-task-4-twitter","repo_url":"https://github.com/balikasg/SemEval2016-Twitter_Sentiment_Evaluation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}