Research and Implementation of Text Sentiment Analysis Model Based on BERT

Abstract

With the deepening development of Web 2.0, user-generated content (UGC) has experienced explosive growth, containing immense
commercial value and social sentiment within massive comment datasets.As one of the core tasks in natural language processing (NLP), sen
timent analysis aims to automatically identify subjective emotional tendencies in texts. Traditional machine learning methods rely on complex
feature engineering, while early deep learning models struggled to capture long-range semantic dependencies. This study proposes and imple
ments a text sentiment analysis model based on BERT (Bidirectional Encoder Representations from Transformers). By leveraging BERT's
powerful bidirectional contextual representation capabilities, combined with domain-specific corpus fine-tuning, and experimental validation
on public datasets, the results demonstrate that the BERT-based model significantly outperforms traditional deep learning models like LSTM
and TextCNN in accuracy, precision, and F1 score metrics. It effectively addresses polysemous word disambiguation and contextual depend
ency challenges,providing an efficient technical pathway for practical sentiment analysis applications.