Enhancing sentiment analysis with language model adaptation: leveraging large-scale pretrained models

This paper reviews how to enhance the accuracy of sentiment analysis by utilizing language model adaptation and large-scale pre-training models. By introducing language model adaptation, models can better understand and capture emotions in text, thereby improving the accuracy of sentiment analysis. Large-scale pre-training models such as BERT and GPT-3 are widely used in sentiment analysis. They have learned rich language knowledge by training on a large amount of unlabeled text data and can utilize this knowledge in sentiment analysis tasks. Although there are still some challenges, such as data requirements and model interpretability, language model adaptation and large-scale pre-training models bring new possibilities to sentiment analysis and are expected to further improve its accuracy and application scope.