{"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/open-domain-targeted-sentiment-analysis-via","title":"Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification","arxiv_id":"1906.03820","date":"2019-06-10","proceeding":"ACL 2019 7","authors":["Minghao Hu","Yuxing Peng","Zhen Huang","Dongsheng Li","Yiwei Lv"],"abstract":"Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these problems, we propose a span-based extract-then-classify framework, where multiple opinion targets are directly extracted from the sentence under the supervision of target span boundaries, and corresponding polarities are then classified using their span representations. We further investigate three approaches under this framework, namely the pipeline, joint, and collapsed models. Experiments on three benchmark datasets show that our approach consistently outperforms the sequence tagging baseline. Moreover, we find that the pipeline model achieves the best performance compared with the other two models.","url_abs":"https://arxiv.org/abs/1906.03820v1","url_pdf":"https://arxiv.org/pdf/1906.03820v1.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":"open-domain-targeted-sentiment-analysis-via","repo_url":"https://github.com/huminghao16/SpanABSA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"aspect-term-extraction-and-sentiment","task_name":"Aspect Term Extraction and Sentiment Classification"},{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-term-extraction-and-sentiment","task":"Aspect Term Extraction and Sentiment Classification","dataset":"SemEval","model":"SPAN-BERT","rank_in_archive_order":5,"of":6,"metrics":{"Avg F1":"65.74","Laptop 2014 (F1)":"61.25","Restaurant 2014 (F1)":"73.68","Restaurant 2015 (F1)":"62.29"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval-5","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval 2014 Task 4 Laptop","model":"SPAN","rank_in_archive_order":3,"of":9,"metrics":{"F1":"68.06"},"uses_additional_data":false},{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval-6","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval 2014 Task 4 Subtask 1+2","model":"SPAN","rank_in_archive_order":5,"of":10,"metrics":{"F1":"68.06"},"uses_additional_data":false},{"leaderboard":"/sota/sentiment-analysis-on-semeval-2014-task-4","task":"Sentiment Analysis","dataset":"SemEval 2014 Task 4 Subtask 1+2","model":"SPAN","rank_in_archive_order":3,"of":8,"metrics":{"F1":"68.06"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1906.03820","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}