{"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-clustering-analysis-of-tweet-length-and-its","title":"A Clustering Analysis of Tweet Length and its Relation to Sentiment","arxiv_id":"1406.3287","date":"2014-06-12","proceeding":null,"authors":["Matthew Mayo"],"abstract":"Sentiment analysis of Twitter data is performed. The researcher has made the\nfollowing contributions via this paper: (1) an innovative method for deriving\nsentiment score dictionaries using an existing sentiment dictionary as seed\nwords is explored, and (2) an analysis of clustered tweet sentiment scores\nbased on tweet length is performed.","url_abs":"http://arxiv.org/abs/1406.3287v3","url_pdf":"http://arxiv.org/pdf/1406.3287v3.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-clustering-analysis-of-tweet-length-and-its","repo_url":"https://github.com/mmmayo13/tweet-sentiment-scores","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":null,"task_name":"Relation"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"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}