The field of sentiment analysis is moving towards more advanced and nuanced approaches, with a focus on fine-grained sentiment analysis and the use of pre-trained language models. Researchers are exploring new architectures and techniques to improve the accuracy and interpretability of sentiment analysis models, particularly in specialized domains such as finance and software engineering. One notable trend is the use of prompt-based learning and generative pre-trained transformers to enhance model performance and adaptability. Noteworthy papers include PL-FGSA, which achieves strong performance on benchmark datasets using a unified prompt learning-based framework, and the study on sentiment analysis in software engineering, which finds that fine-tuned GPT-4o-mini performs comparable to BERT on structured datasets. Another notable paper is the benchmarking of LLMs on financial nuance, which evaluates the reliability and accuracy of large language models in sentiment analysis of financial text.