Sentiment Analysis using Large Language Models: Methodologies, Applications, and Challenges

Sentiment analysis is the task of extracting subjective information from textual data.The field of sentiment analysis has seen significant advancements with the emergence of large language models (LLMs).This survey paper provides an overview of sentiment analysis using LLMs, discussing methodologies, applications, challenges, and future directions.The traditional sentiment analysis techniques, such as rulebased approaches and machine learning models are reviewed and the recent advancements in sentiment analysis using pretrained LLMs like BERT, GPT, and XLNet are explored.Key findings include the importance of model interpretability, the impact of biases, and the significance of domain adaptation in sentiment analysis using LLMs.The paper discusses the significance of sentiment analysis using LLMs across various industries, including ecommerce, social media monitoring, healthcare, and finance for the organizations to leverage LLMs to gain insights into customer opinions, brand perception, market trends, and public sentiment, enabling data-driven decision-making and enhanced customer experiences.Finally, recommendations for further research are provided for researchers and practitioners to help unlock new possibilities for understanding human sentiments and emotions, driving positive outcomes across diverse domains and industries.