{"url":"/sota/sentiment-analysis-on-dbrd","task":{"name":"Sentiment Analysis","url":"/task/sentiment-analysis","note":null},"dataset":{"name":"DBRD","url":"/dataset/dbrd"},"category":"Computer Vision","categories":["Computer Vision","Natural Language Processing"],"category_note":null,"description":"**Sentiment Analysis** is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either \"positive\", \"negative\", or \"neutral\". Given the text and accompanying labels, a model can be trained to predict the correct sentiment. \r\n\r\n**Sentiment Analysis** techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.\r\n\r\nMore recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.\r\n\r\nFurther readings:\r\n\r\n- [Sentiment Analysis Based on Deep Learning: A Comparative Study](https://paperswithcode.com/paper/sentiment-analysis-based-on-deep-learning-a)","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":["Accuracy","F1"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Accuracy":"higher","F1":"higher"}},"counts":{"rows":4,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"RobBERT v2","metrics":{"Accuracy":"95.144%","F1":"95.144%"},"uses_additional_data":false,"paper_date":"2020-01-17","paper":"/paper/robbert-a-dutch-roberta-based-language-model","paper_url":"https://arxiv.org/abs/2001.06286v2","paper_title":"RobBERT: a Dutch RoBERTa-based Language Model","code":"https://github.com/iPieter/RobBERT","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"RobBERT","metrics":{"Accuracy":"94.422%","F1":"94.422%"},"uses_additional_data":false,"paper_date":"2020-01-17","paper":"/paper/robbert-a-dutch-roberta-based-language-model","paper_url":"https://arxiv.org/abs/2001.06286v2","paper_title":"RobBERT: a Dutch RoBERTa-based Language Model","code":"https://github.com/iPieter/RobBERT","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":0,"n_samples":1,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"BERTje","metrics":{"Accuracy":"93%"},"uses_additional_data":false,"paper_date":"2019-12-19","paper":"/paper/bertje-a-dutch-bert-model","paper_url":"https://arxiv.org/abs/1912.09582v1","paper_title":"BERTje: A Dutch BERT Model","code":"https://github.com/wietsedv/bertje","n_code_links":2,"syntology":{"n_ran":2,"n_unverified":3,"n_samples":5,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"BERT-NL","metrics":{"F1":"84%"},"uses_additional_data":false,"paper_date":null,"paper":null,"paper_url":null,"paper_title":"","code":null,"n_code_links":0,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"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":3,"rows_with_any_sample_ran":3,"distinct_papers_with_graph_line":2,"distinct_papers_with_any_sample_ran":2,"samples_over_distinct_papers":{"n_ran":3,"n_unverified":3,"n_samples":6,"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":4,"n_unverified":3,"n_samples":7,"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"}}}