{"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-unified-model-for-opinion-target-extraction","title":"A Unified Model for Opinion Target Extraction and Target Sentiment Prediction","arxiv_id":"1811.05082","date":"2018-11-13","proceeding":null,"authors":["Xin Li","Lidong Bing","Piji Li","Wai Lam"],"abstract":"Target-based sentiment analysis involves opinion target extraction and target\nsentiment classification. However, most of the existing works usually studied\none of these two sub-tasks alone, which hinders their practical use. This paper\naims to solve the complete task of target-based sentiment analysis in an\nend-to-end fashion, and presents a novel unified model which applies a unified\ntagging scheme. Our framework involves two stacked recurrent neural networks:\nThe upper one predicts the unified tags to produce the final output results of\nthe primary target-based sentiment analysis; The lower one performs an\nauxiliary target boundary prediction aiming at guiding the upper network to\nimprove the performance of the primary task. To explore the inter-task\ndependency, we propose to explicitly model the constrained transitions from\ntarget boundaries to target sentiment polarities. We also propose to maintain\nthe sentiment consistency within an opinion target via a gate mechanism which\nmodels the relation between the features for the current word and the previous\nword. We conduct extensive experiments on three benchmark datasets and our\nframework achieves consistently superior results.","url_abs":"http://arxiv.org/abs/1811.05082v2","url_pdf":"http://arxiv.org/pdf/1811.05082v2.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-unified-model-for-opinion-target-extraction","repo_url":"https://github.com/lixin4ever/E2E-TBSA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"aspect-based-sentiment-analysis","task_name":"Aspect-Based Sentiment Analysis (ABSA)"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-on-semeval-5","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset":"SemEval 2014 Task 4 Laptop","model":"E2E-TBSA","rank_in_archive_order":8,"of":9,"metrics":{"F1":"57.9"},"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":"E2E-TBSA","rank_in_archive_order":10,"of":10,"metrics":{"F1":"57.9"},"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":"E2E-TBSA","rank_in_archive_order":8,"of":8,"metrics":{"F1":"57.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.05082","atlas_url":"https://app.syntology.ai/?focus=1811.05082","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}