Natural Language Processing for Early Depression Detection

Abstract

This paper reviews the role of Natural Language Processing (NLP) in the detection of early symptoms of depression by analyzing text data obtained through clinical transcripts, internet forums and social media.  The mixed-methods design was adopted that combined quantitative machine learning models and qualitative validation of the results with clinical literature.  Preprocessing included tokenization, lemmatization, and embedding generation with models such as Word2Vec and BERT. Such handcrafted characteristics as sentiment polarity, grammatical difficulty and lexical richness were also included.  Our experiments with various supervised learning models tried many of them. The strongest predicted accuracy levels were noted with the transformer-based architectures, and logistic regression and support vector machines were consistent in comparisons of the baseline.  The findings revealed that NLP models would successfully detect signs of depression accurately with good precision, recall and AUC scores. This indicated that mixed linguistic representations are powerful.  The SHAP values allowed perceiving the results by displaying the linguistic indicators that were quite close to the mental diagnostic criteria, including negative self-focus and hopelessness statements.  The findings indicate NLP frameworks can enhance conventional mental health screening with the capacity to present scalable, proactive, and clinically suitable approaches to early intervention.  The research contributes to the field of mental health informatics by integrating computational aspects with psychological concepts to provide a foundation of incorporating explainable AI into the clinical decision-making process.