Research on sentiment recognition in online shopping reviews using an integrated BERT and BiLSTM approach

This study explores a method integrating BERT and BiLSTM technologies aimed at enhancing the accuracy of sentiment recognition in online shopping reviews. Through detailed experimental validation, the fused model significantly surpasses traditional approaches that solely rely on either BERT or BiLSTM across various key performance metrics. This work not only confirms the potential of deep learning techniques in understanding complex textual emotional expressions but also provides new research directions and practical application possibilities for sentiment analysis of social media data.