{"url":"/sota/aspect-term-extraction-and-sentiment","task":{"name":"Aspect Term Extraction and Sentiment Classification","url":"/task/aspect-term-extraction-and-sentiment","note":null},"dataset":{"name":"SemEval","url":null},"category":"Computer Vision","categories":["Computer Vision","Natural Language Processing"],"category_note":null,"description":"Extracting the aspect terms as well as the corresponding sentiment polarities simultaneously.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Avg F1","Restaurant 2014 (F1)","Laptop 2014 (F1)","Restaurant 2015 (F1)"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Avg F1":"higher","Restaurant 2014 (F1)":"higher","Laptop 2014 (F1)":"higher","Restaurant 2015 (F1)":"higher"}},"counts":{"rows":6,"rows_with_code":5,"rows_with_paper_page":6,"rows_dated":6,"rows_using_additional_data":1},"rows":[{"rank_in_archive_order":1,"model":"FS-ABSA","metrics":{"Avg F1":"76.73","Laptop 2014 (F1)":"71.16","Restaurant 2014 (F1)":"82.29"},"uses_additional_data":true,"paper_date":"2023-07-18","paper":"/paper/a-simple-yet-effective-framework-for-few-shot","paper_url":"https://dl.acm.org/doi/10.1145/3539618.3591940","paper_title":"A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis","code":"https://github.com/NUSTM/FS-ABSA","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"BARTABSA","metrics":{"Avg F1":"69.18","Laptop 2014 (F1)":"67.37","Restaurant 2014 (F1)":"73.56","Restaurant 2015 (F1)":"66.61"},"uses_additional_data":false,"paper_date":"2021-06-08","paper":"/paper/a-unified-generative-framework-for-aspect","paper_url":"https://arxiv.org/abs/2106.04300v1","paper_title":"A Unified Generative Framework for Aspect-Based Sentiment Analysis","code":"https://github.com/yhcc/BARTABSA","n_code_links":3,"syntology":null},{"rank_in_archive_order":3,"model":"Dual-MRC","metrics":{"Avg F1":"68.99","Laptop 2014 (F1)":"65.94","Restaurant 2014 (F1)":"75.95","Restaurant 2015 (F1)":"65.08"},"uses_additional_data":false,"paper_date":"2021-01-04","paper":"/paper/a-joint-training-dual-mrc-framework-for","paper_url":"https://arxiv.org/abs/2101.00816v2","paper_title":"A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":4,"model":"RACL-BERT","metrics":{"Avg F1":"68.29","Laptop 2014 (F1)":"63.4","Restaurant 2014 (F1)":"75.42","Restaurant 2015 (F1)":"66.05"},"uses_additional_data":false,"paper_date":"2020-07-01","paper":"/paper/relation-aware-collaborative-learning-for","paper_url":"https://aclanthology.org/2020.acl-main.340","paper_title":"Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis","code":"https://github.com/NLPWM-WHU/RACL","n_code_links":1,"syntology":null},{"rank_in_archive_order":5,"model":"SPAN-BERT","metrics":{"Avg F1":"65.74","Laptop 2014 (F1)":"61.25","Restaurant 2014 (F1)":"73.68","Restaurant 2015 (F1)":"62.29"},"uses_additional_data":false,"paper_date":"2019-06-10","paper":"/paper/open-domain-targeted-sentiment-analysis-via","paper_url":"https://arxiv.org/abs/1906.03820v1","paper_title":"Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification","code":"https://github.com/huminghao16/SpanABSA","n_code_links":1,"syntology":null},{"rank_in_archive_order":6,"model":"IMN-BERT","metrics":{"Avg F1":"64.23","Laptop 2014 (F1)":"61.73","Restaurant 2014 (F1)":"70.72","Restaurant 2015 (F1)":"60.22"},"uses_additional_data":false,"paper_date":"2019-06-17","paper":"/paper/an-interactive-multi-task-learning-network","paper_url":"https://arxiv.org/abs/1906.06906v1","paper_title":"An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis","code":"https://github.com/lixin4ever/BERT-E2E-ABSA","n_code_links":3,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}