{"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/multitask-learning-for-fine-grained-twitter","title":"Multitask Learning for Fine-Grained Twitter Sentiment Analysis","arxiv_id":"1707.03569","date":"2017-07-12","proceeding":null,"authors":["Georgios Balikas","Simon Moura","Massih-Reza Amini"],"abstract":"Traditional sentiment analysis approaches tackle problems like ternary\n(3-category) and fine-grained (5-category) classification by learning the tasks\nseparately. We argue that such classification tasks are correlated and we\npropose a multitask approach based on a recurrent neural network that benefits\nby jointly learning them. Our study demonstrates the potential of multitask\nmodels on this type of problems and improves the state-of-the-art results in\nthe fine-grained sentiment classification problem.","url_abs":"http://arxiv.org/abs/1707.03569v1","url_pdf":"http://arxiv.org/pdf/1707.03569v1.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":"multitask-learning-for-fine-grained-twitter","repo_url":"https://github.com/balikasg/sigir2017","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","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"},{"task_slug":"twitter-sentiment-analysis","task_name":"Twitter Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.03569","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}