{"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/banglanlp-at-blp-2023-task-2-benchmarking","title":"BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media Posts","arxiv_id":"2310.09238","date":"2023-10-13","proceeding":null,"authors":["Saumajit Saha","Albert Nanda"],"abstract":"Bangla is the 7th most widely spoken language globally, with a staggering 234 million native speakers primarily hailing from India and Bangladesh. This morphologically rich language boasts a rich literary tradition, encompassing diverse dialects and language-specific challenges. Despite its linguistic richness and history, Bangla remains categorized as a low-resource language within the natural language processing (NLP) and speech community. This paper presents our submission to Task 2 (Sentiment Analysis of Bangla Social Media Posts) of the BLP Workshop. We experiment with various Transformer-based architectures to solve this task. Our quantitative results show that transfer learning really helps in better learning of the models in this low-resource language scenario. This becomes evident when we further finetune a model which has already been finetuned on twitter data for sentiment analysis task and that finetuned model performs the best among all other models. We also perform a detailed error analysis where we find some instances where ground truth labels need to be relooked at. We obtain a micro-F1 of 67.02\\% on the test set and our performance in this shared task is ranked at 21 in the leaderboard.","url_abs":"https://arxiv.org/abs/2310.09238v2","url_pdf":"https://arxiv.org/pdf/2310.09238v2.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":"banglanlp-at-blp-2023-task-2-benchmarking","repo_url":"https://github.com/Saumajit/BanglaNLP/tree/main/Task_2","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"task-2","task_name":"Task 2"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}