Optimization of Sentiment Analysis using BERT

This study presents a comprehensive exploration of a sentiment analysis algorithm enhanced by the BERT (Bidirectional Encoder Representations from Transformers) model. The BERT model's integration into our sentiment analysis algorithm has resulted in performance that surpasses traditional methods, including Support Vector Machines (SVM), Logistic Regression (LR), and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. The findings of this research are poised to contribute significantly to the field of sentiment analysis, providing a robust framework for future applications in understanding customer feedback.