{"url":"/dataset/tasd","name":"TASD","full_name":"Target Aspect Sentiment Detection","description_markdown":"Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i.e., it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspect-sentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-the-art methods for those subtasks of target-aspect-sentiment detection that they are competent to.","description_withheld":null,"homepage":"https://github.com/sysulic/TAS-BERT","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Aspect-Based Sentiment Analysis (ABSA)","url":"/task/aspect-based-sentiment-analysis","datasets_with_task":"/datasets/task/aspect-based-sentiment-analysis"}],"languages":[],"variants":["TASD"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-tasd","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset_variant":"TASD","rows":11,"metrics":["F1 (R15)","F1 (R16)"],"first_row_in_archive_order":{"model":"MvP (multi-task)","paper":"/paper/mvp-multi-view-prompting-improves-aspect","metrics":{"F1 (R15)":"64.74","F1 (R16)":"70.18"},"code_links":[{"title":"ZubinGou/multi-view-prompting","url":"https://github.com/ZubinGou/multi-view-prompting"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/do-we-still-need-human-annotators-prompting","title":"Do we still need Human Annotators? Prompting Large Language Models for Aspect Sentiment Quad Prediction","date":"2025-02-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/mvp-multi-view-prompting-improves-aspect","title":"MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction","date":"2023-05-22","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/improving-aspect-sentiment-quad-prediction","title":"Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation","date":"2022-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lego-absa-a-prompt-based-task-assemblable","title":"LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis","date":"2022-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/aspect-sentiment-quad-prediction-as","title":"Aspect Sentiment Quad Prediction as Paraphrase Generation","date":"2021-10-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/towards-generative-aspect-based-sentiment","title":"Towards Generative Aspect-Based Sentiment Analysis","date":"2021-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}