@inproceedings{saha-nanda-2023-banglanlp-blp,
    title = "{B}angla{NLP} at {BLP}-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of {B}angla Social Media Posts",
    author = "Saha, Saumajit  and
      Nanda, Albert",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Sadeque, Farig  and
      Amin, Ruhul",
    booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.banglalp-1.34/",
    doi = "10.18653/v1/2023.banglalp-1.34",
    pages = "266--272",
    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 experimented 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 finetuned a model that had already been finetuned on Twitter data for sentiment analysis task and that finetuned model performed the best among all other models. We also performed a detailed error analysis where we found some instances where ground truth labels need to be looked at. We obtained a micro-F1 of 67.02{\%} on the test set and our performance in this shared task is ranked at 21 in the leaderboard."
}