{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/l3cube-mahasent-md-a-multi-domain-marathi","title":"L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models","arxiv_id":"2306.13888","date":"2023-06-24","proceeding":null,"authors":["Aabha Pingle","Aditya Vyawahare","Isha Joshi","Rahul Tangsali","Raviraj Joshi"],"abstract":"The exploration of sentiment analysis in low-resource languages, such as Marathi, has been limited due to the availability of suitable datasets. In this work, we present L3Cube-MahaSent-MD, a multi-domain Marathi sentiment analysis dataset, with four different domains - movie reviews, general tweets, TV show subtitles, and political tweets. The dataset consists of around 60,000 manually tagged samples covering 3 distinct sentiments - positive, negative, and neutral. We create a sub-dataset for each domain comprising 15k samples. The MahaSent-MD is the first comprehensive multi-domain sentiment analysis dataset within the Indic sentiment landscape. We fine-tune different monolingual and multilingual BERT models on these datasets and report the best accuracy with the MahaBERT model. We also present an extensive in-domain and cross-domain analysis thus highlighting the need for low-resource multi-domain datasets. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .","url_abs":"https://arxiv.org/abs/2306.13888v1","url_pdf":"https://arxiv.org/pdf/2306.13888v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"l3cube-mahasent-md-a-multi-domain-marathi","repo_url":"https://github.com/l3cube-pune/MarathiNLP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2306.13888","atlas_url":"https://app.syntology.ai/?focus=2306.13888","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}