Analyzing Textual Data for Mental Health Assessment: Natural Language Processing for Depression and Anxiety

In this study, depression and anxiety markers found in text data are carefully targeted in order to use Natural Language Processing (NLP) techniques to the evaluation of mental health. The work gives a thorough assessment of NLP models that include sentiment analysis, emotion identification, as well as linguistic pattern detection along with shows significant improvements in F1 scores, recall, and accuracy. The varied dataset, gathered from social media, online discussion boards, and healthcare records, is rigorously preprocessed as well as qualitatively evaluated, enhancing the validity of the method. The findings might advance early identification and intervention by bridging the technical and mental health spheres. Cross-cultural datasets, multi-modal data integration, moral concerns, and cooperation with mental health specialists are important future prospects.