  The rapid expansion of the digital world has propelled sentiment analysis
into a critical tool across diverse sectors such as marketing, politics,
customer service, and healthcare. While there have been significant
advancements in sentiment analysis for widely spoken languages, low-resource
languages, such as Bangla, remain largely under-researched due to resource
constraints. Furthermore, the recent unprecedented performance of Large
Language Models (LLMs) in various applications highlights the need to evaluate
them in the context of low-resource languages. In this study, we present a
sizeable manually annotated dataset encompassing 33,606 Bangla news tweets and
Facebook comments. We also investigate zero- and few-shot in-context learning
with several language models, including Flan-T5, GPT-4, and Bloomz, offering a
comparative analysis against fine-tuned models. Our findings suggest that
monolingual transformer-based models consistently outperform other models, even
in zero and few-shot scenarios. To foster continued exploration, we intend to
make this dataset and our research tools publicly available to the broader
research community.
