{"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/generating-sentiment-lexicons-for-german","title":"Generating Sentiment Lexicons for German Twitter","arxiv_id":"1610.09995","date":"2016-10-31","proceeding":"WS 2016 12","authors":["Uladzimir Sidarenka","Manfred Stede"],"abstract":"Despite a substantial progress made in developing new sentiment lexicon\ngeneration (SLG) methods for English, the task of transferring these approaches\nto other languages and domains in a sound way still remains open. In this\npaper, we contribute to the solution of this problem by systematically\ncomparing semi-automatic translations of common English polarity lists with the\nresults of the original automatic SLG algorithms, which were applied directly\nto German data. We evaluate these lexicons on a corpus of 7,992 manually\nannotated tweets. In addition to that, we also collate the results of\ndictionary- and corpus-based SLG methods in order to find out which of these\nparadigms is better suited for the inherently noisy domain of social media. Our\nexperiments show that semi-automatic translations notably outperform automatic\nsystems (reaching a macro-averaged F1-score of 0.589), and that\ndictionary-based techniques produce much better polarity lists as compared to\ncorpus-based approaches (whose best F1-scores run up to 0.479 and 0.419\nrespectively) even for the non-standard Twitter genre.","url_abs":"http://arxiv.org/abs/1610.09995v1","url_pdf":"http://arxiv.org/pdf/1610.09995v1.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":"generating-sentiment-lexicons-for-german","repo_url":"https://github.com/WladimirSidorenko/SentiLex","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}