  Sentiment analysis is the process of identifying and categorizing people's
emotions or opinions regarding various topics. Analyzing political sentiment is
critical for understanding the complexities of public opinion processes,
especially during election seasons. It gives significant information on voter
preferences, attitudes, and current trends. In this study, we investigate
political sentiment analysis during Bangladeshi elections, specifically
examining how effectively Pre-trained Language Models (PLMs) and Large Language
Models (LLMs) capture complex sentiment characteristics. Our study centers on
the creation of the "Motamot" dataset, comprising 7,058 instances annotated
with positive and negative sentiments, sourced from diverse online newspaper
portals, forming a comprehensive resource for political sentiment analysis. We
meticulously evaluate the performance of various PLMs including BanglaBERT,
Bangla BERT Base, XLM-RoBERTa, mBERT, and sahajBERT, alongside LLMs such as
Gemini 1.5 Pro and GPT 3.5 Turbo. Moreover, we explore zero-shot and few-shot
learning strategies to enhance our understanding of political sentiment
analysis methodologies. Our findings underscore BanglaBERT's commendable
accuracy of 88.10% among PLMs. However, the exploration into LLMs reveals even
more promising results. Through the adept application of Few-Shot learning
techniques, Gemini 1.5 Pro achieves an impressive accuracy of 96.33%,
surpassing the remarkable performance of GPT 3.5 Turbo, which stands at 94%.
This underscores Gemini 1.5 Pro's status as the superior performer in this
comparison.
