Optimizing Airline Review Sentiment Analysis: A Comparative Analysis of LLaMA and BERT Models through Fine-Tuning and Few-Shot Learning

In the rapidly evolving landscape of natural language processing (NLP) and sentiment analysis, improving the accuracy and efficiency of sentiment classification models is crucial. This paper investigates the performance of two advanced models, the Large Language Model (LLM) LLaMA model and NLP BERT model, in the context of airline review sentiment analysis. Through fine-tuning, domain adaptation, and the application of few-shot learning, the study addresses the subtleties of sentiment expressions in airline-related text data. Employing predictive modeling and comparative analysis, the research evaluates the effectiveness of Large Language Model Meta AI (LLaMA) and Bidirectional Encoder Representations from Transformers (BERT) in capturing sentiment intricacies. Fine-tuning, including domain adaptation, enhances the models' performance in sentiment classification tasks. Additionally, the study explores the potential of few-shot learning to improve model generalization using minimal annotated data for targeted sentiment analysis. By conducting experiments on a diverse airline review dataset, the research quantifies the impact of fine-tuning, domain adaptation, and few-shot learning on model performance, providing valuable insights for industries aiming to predict recommendations and enhance customer satisfaction through a deeper understanding of sentiment in user-generated content (UGC). This research contributes to refining sentiment analysis models, ultimately fostering improved customer satisfaction in the airline industry.