{"url":"/dataset/asqp","name":"ASQP","full_name":"Aspect Sentiment Quad Prediction","description_markdown":"Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question and the associated opinion clues in the answer. To this end, we propose a model with a specific design of cross-sentence aspect-opinion interaction modeling to address this task. The proposed method is evaluated on three real-world datasets and the results show that our model outperforms several strong baselines adopted from related state-of-the-art models.","description_withheld":null,"homepage":"","introduced_date":"2021-11-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/aspect-based-sentiment-analysis-in-question","title":"Aspect-based Sentiment Analysis in Question Answering Forums","first_author":"Wenxuan Zhang","url":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":["ASQP"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/aspect-based-sentiment-analysis-absa-on-asqp","task":"Aspect-Based Sentiment Analysis (ABSA)","dataset_variant":"ASQP","rows":12,"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)":"52.21","F1 (R16)":"58.94"},"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/generative-data-augmentation-for-aspect","title":"Generative Data Augmentation for Aspect Sentiment Quad Prediction","date":"2023-07-01","rows_on_this_dataset":1,"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."}