Fine Tuning Llm For Sentiment Analysis

This study delves into the fine-tuning of large language models (LLMs) like BERT and GPT-3 for sentiment analysis to improve the accuracy and stability of sentiment detection in text data. Sentiment analysis methods traditionally have problems with the subtleties of language, such as sarcasm, context-dependent meanings, and vague sentiments. Fine-tuning LLMs on task-specific datasets enables these models to comprehend the subtleties of sentiment more effectively, thus being able to identify emotional tones in text more accurately. This paper provides the methodology for fine-tuning LLMs, including solutions to issues such as dataset imbalances, overfitting, and the selection of the correct pre-trained models. By empirical findings, the paper illustrates how fine-tuned LLMs can enhance sentiment analysis accuracy and stability, providing insights for practical applications. The study also presents possible enhancements and future research directions for further improving the performance of LLMs in sentiment analysis.