{"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/improving-tweet-representations-using","title":"Improving Tweet Representations using Temporal and User Context","arxiv_id":"1612.06062","date":"2016-12-19","proceeding":null,"authors":["Ganesh J","Manish Gupta","Vasudeva Varma"],"abstract":"In this work we propose a novel representation learning model which computes\nsemantic representations for tweets accurately. Our model systematically\nexploits the chronologically adjacent tweets ('context') from users' Twitter\ntimelines for this task. Further, we make our model user-aware so that it can\ndo well in modeling the target tweet by exploiting the rich knowledge about the\nuser such as the way the user writes the post and also summarizing the topics\non which the user writes. We empirically demonstrate that the proposed models\noutperform the state-of-the-art models in predicting the user profile\nattributes like spouse, education and job by 19.66%, 2.27% and 2.22%\nrespectively.","url_abs":"http://arxiv.org/abs/1612.06062v1","url_pdf":"http://arxiv.org/pdf/1612.06062v1.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":"improving-tweet-representations-using","repo_url":"https://github.com/ganeshjawahar/tweet2vec","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"torch","reach":null}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}