Leveraging Bidirectional Encoder Representations from Transformers (BERT) for Enhanced Sentiment Analysis

This study investigates how Bidirectional Encoder Representations from Transformers (BERT) can revolutionize sentiment analysis in a variety of fields. Sentiment analysis models can now capture complex emotions and context, thanks to BERT's contextual understanding and cutting-edge machine learning techniques like transfer learning. We group models according to domains and learning paradigms and then display their results. By bridging the gap between human expression and computer comprehension, BERT can transform the precision and nuance of sentiment analysis. This study clarifies the value of BERT in improving sentiment comprehension and provides information for wise decision-making. BERT is an open-source machine learning platform for Natural Language Processing (NLP). BERT aims to help computers understand the meaning of ambiguous words in the text by using the surrounding neighboring text to construct meaning.