{"url":"/task/fine-grained-opinion-analysis","name":"Fine-Grained Opinion Analysis","slug":"fine-grained-opinion-analysis","description_markdown":"Fine-Grained Opinion Analysis aims to: (i) detect opinion expressions that convey attitudes such as sentiments, agreements, beliefs, or intentions, (ii) measure their intensity, (iii) identify their holders i.e. entities that express an attitude, (iv) identify their targets i.e. entities or propositions at which the attitude is directed, and (v) classify their target-dependent attitude.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [SRL4ORL](https://arxiv.org/pdf/1711.00768v3.pdf) )</span>","categories":[{"name":"Computer Vision","url":"/area/computer-vision"},{"name":"Natural Language Processing","url":"/area/natural-language-processing"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":19,"papers_with_code":4,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/fine-grained-opinion-analysis-on-mpqa","slug":"fine-grained-opinion-analysis-on-mpqa","dataset":"MPQA","dataset_url":"/dataset/mpqa-opinion-corpus","rows_in_archive":3,"metrics":["Holder Binary F1","Target Binary F1","F1 (Opinion)","F1 (Opinion-Holder Pair)","F1 (Opinion-Role Pair)","F1 (Opinion-Target Pair)"],"first_row_in_archive_order":{"model":"SRL-SAWR","paper_title":"Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling","paper_url":"/paper/enhancing-opinion-role-labeling-with-semantic","paper_date":"2019-06-01","arxiv_id":null,"code_links":[{"title":"zhangmeishan/SRL4ORL","url":"https://github.com/zhangmeishan/SRL4ORL"}],"syntology":null}}],"datasets":[{"url":"/dataset/mpqa-opinion-corpus","name":"MPQA Opinion Corpus","full_name":"Multi-Perspective Question Answering","num_papers_in_archive":313},{"url":"/dataset/state-toxicn","name":"STATE ToxiCN","full_name":"","num_papers_in_archive":1}],"subtasks":[],"parent_tasks":[{"url":"/task/sentiment-analysis","name":"Sentiment Analysis"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":4,"of":4,"tagged_in_all":19,"items":[{"url":"/paper/opinion-mining-using-pre-trained-large","title":"Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States","date":"2024-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/neural-transition-system-for-end-to-end","title":"Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling","date":"2021-10-05","arxiv_id":"2110.02001","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-opinion-role-labeling-with-semantic","title":"Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/srl4orl-improving-opinion-role-labeling-using","title":"SRL4ORL: Improving Opinion Role Labeling using Multi-task Learning with Semantic Role Labeling","date":"2017-11-02","arxiv_id":"1711.00768","repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}