{"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/direct-parsing-to-sentiment-graphs-1","title":"Direct parsing to sentiment graphs","arxiv_id":"2203.13209","date":"2022-03-24","proceeding":"ACL 2022 5","authors":["David Samuel","Jeremy Barnes","Robin Kurtz","Stephan Oepen","Lilja Øvrelid","Erik Velldal"],"abstract":"This paper demonstrates how a graph-based semantic parser can be applied to the task of structured sentiment analysis, directly predicting sentiment graphs from text. We advance the state of the art on 4 out of 5 standard benchmark sets. We release the source code, models and predictions.","url_abs":"https://arxiv.org/abs/2203.13209v2","url_pdf":"https://arxiv.org/pdf/2203.13209v2.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":"direct-parsing-to-sentiment-graphs-1","repo_url":"https://github.com/jerbarnes/direct_parsing_to_sent_graph","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.13209","atlas_url":"https://app.syntology.ai/?focus=2203.13209","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}