Roberta and BERT: Revolutionizing Mental Healthcare Through Natural Language

In recent years, developments in natural language processing (NLP) have made it possible for novel applications to be developed in the field of mental healthcare. Roberta and BERT are two examples of these advances. They are examples of state-of-the-art language models that have exhibited extraordinary skills in comprehending and processing human language. To enhance the efficiency and reduce the error rate, it proposes the integration of PSO in Bert and Roberta models at the time of pre-processing in order to filter useless data. Integration of PSO is done in both Bert and Roberta for filtering. The current study compares the proposed integrated approach with existing approaches on various validation measures like accuracy, Precision, Reccall and error rates. It demonstrates that LSTM achieves an accuracy of 99.2%, Bert with integrated PSO, demonstrates slightly higher accuracy at 99.4%. Impressively, Roberta with PSO surpasses both Hybrid LSTM and Bert with PSO with the highest accuracy of 99.6%. Also, Hybrid LSTM, exhibits an error rate of 0.35%. In contrast, Bert with integrated PSO, achieves a lower error rate of 0.27%. Notably, Roberta with integrated PSO demonstrates the lowest error rate among the three approaches, with an impressive value of 0.12%. This study underscores the transformative impact of advanced NLP models like Roberta and BERT on mental healthcare. By harnessing language data, they enable precise diagnosis and tailored interventions, improving accessibility and efficacy. Integrating PSO enhances accuracy and performance.