Textual Sentiment Classification and Mental Health Analysis Based on Bidirectional Gated Recurrent Unit Modeling

This study focuses on the task of text sentiment classification, aiming to lay the foundation for in-depth analysis of user mental health. To achieve this goal, we innovatively introduced and applied the Bidirectional Gated Recurrent Unit (BiGRU) model for modeling and experimentation. This model can effectively capture the contextual dependencies in text sequences, significantly improving the learning and recognition ability of emotional semantic features. In the rigorous model evaluation process, the analysis results based on the test set confusion matrix showed that the model achieved a high prediction accuracy of 94.8%. This outstanding performance metric fully validates the high effectiveness and reliability of the proposed BiGRU model in handling text sentiment classification problems, with accurate and robust classification performance. Therefore, this study not only confirms the enormous potential of BiGRU in this field, but more importantly, its excellent classification performance provides strong core technical support for the subsequent construction of automated and intelligent text emotion recognition and classification systems. More importantly, the successful application of this model in mental health analysis scenarios means that it can efficiently identify the emotions contained in user texts, providing objective and quantifiable analysis basis for timely insight into user psychological states, warning potential risks, and providing personalized psychological support or intervention suggestions. It has important practical significance for improving the intelligence level and response efficiency of mental health services.