{"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/l3cubemahasent-a-marathi-tweet-based","title":"L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset","arxiv_id":"2103.11408","date":"2021-03-21","proceeding":"EACL (WASSA) 2021 4","authors":["Atharva Kulkarni","Meet Mandhane","Manali Likhitkar","Gayatri Kshirsagar","Raviraj Joshi"],"abstract":"Sentiment analysis is one of the most fundamental tasks in Natural Language Processing. Popular languages like English, Arabic, Russian, Mandarin, and also Indian languages such as Hindi, Bengali, Tamil have seen a significant amount of work in this area. However, the Marathi language which is the third most popular language in India still lags behind due to the absence of proper datasets. In this paper, we present the first major publicly available Marathi Sentiment Analysis Dataset - L3CubeMahaSent. It is curated using tweets extracted from various Maharashtrian personalities' Twitter accounts. Our dataset consists of ~16,000 distinct tweets classified in three broad classes viz. positive, negative, and neutral. We also present the guidelines using which we annotated the tweets. Finally, we present the statistics of our dataset and baseline classification results using CNN, LSTM, ULMFiT, and BERT-based deep learning models.","url_abs":"https://arxiv.org/abs/2103.11408v2","url_pdf":"https://arxiv.org/pdf/2103.11408v2.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":"l3cubemahasent-a-marathi-tweet-based","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":"awd-lstm","method_name":"AWD-LSTM"},{"method_slug":"activation-regularization","method_name":"Activation Regularization"},{"method_slug":"discriminative-fine-tuning","method_name":"Discriminative Fine-Tuning"},{"method_slug":"dropconnect","method_name":"DropConnect"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"embedding-dropout","method_name":"Embedding Dropout"},{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"slanted-triangular-learning-rates","method_name":"Slanted Triangular Learning Rates"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"},{"method_slug":"temporal-activation-regularization","method_name":"Temporal Activation Regularization"},{"method_slug":"ulmfit","method_name":"ULMFiT"},{"method_slug":"variational-dropout","method_name":"Variational Dropout"},{"method_slug":"weight-tying","method_name":"Weight Tying"}],"datasets_introduced":[{"slug":"l3cubemahasent","name":"L3CubeMahaSent","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.11408","atlas_url":"https://app.syntology.ai/?focus=2103.11408","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}