{"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/newsmtsc-a-dataset-for-multi-target-dependent","title":"NewsMTSC: A Dataset for (Multi-)Target-dependent Sentiment Classification in Political News Articles","arxiv_id":null,"date":"2021-04-01","proceeding":"EACL 2021 2","authors":["Felix Hamborg","Karsten Donnay"],"abstract":"Previous research on target-dependent sentiment classification (TSC) has mostly focused on reviews, social media, and other domains where authors tend to express sentiment explicitly. In this paper, we investigate TSC in news articles, a much less researched TSC domain despite the importance of news as an essential information source in individual and societal decision making. We introduce NewsMTSC, a high-quality dataset for TSC on news articles with key differences compared to established TSC datasets, including, for example, different means to express sentiment, longer texts, and a second test-set to measure the influence of multi-target sentences. We also propose a model that uses a BiGRU to interact with multiple embeddings, e.g., from a language model and external knowledge sources. The proposed model improves the performance of the prior state-of-the-art from F1{\\_}m=81.7 to 83.1 (real-world sentiment distribution) and from F1{\\_}m=81.2 to 82.5 (multi-target sentences).","url_abs":"https://aclanthology.org/2021.eacl-main.142","url_pdf":"https://aclanthology.org/2021.eacl-main.142.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":"newsmtsc-a-dataset-for-multi-target-dependent","repo_url":"https://github.com/fhamborg/newsmtsc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[{"method_slug":"bigru","method_name":"BiGRU"}],"datasets_introduced":[{"slug":"newstsc","name":"NewsMTSC","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}