A Novel Technique for the Early Diagnosis of Mental Health Using Natural Language Processing

In this work, we delve into the critical issue of mental health, particularly focusing on depression, a condition affecting over 264 million individuals of all ages globally, as reported by the World Health Organization (WHO). The pervasive nature of depression establishes it as a major reason of disability on a global scale. Recognizing the immense impact of mental health disorders, our research is centered around the imperative of early diagnosis as a crucial preventive measure to address this global concern. Natural language processing (NLP) emerges as a pivotal field among linguistics and artificial intelligence. Our study seeks to leverage NLP in addressing the challenges associated with the early examination of mental health-related problems. NLP is fundamentally concerned with enabling machines to understand, analyze, and evaluate human speech, offering a sophisticated avenue for understanding the nuances present in mental health-related text data. To contextualize the practical applications of NLP in mental health, we draw attention to the emergence of chatbots such as Woebot, Wysa, Joyable, and Talkspace. These chatbots have the capacity to evaluate mental health assessments using natural conversation. By integrating NLP techniques, these chatbots exemplify the potential for technology to contribute to mental health assessments in an accessible and user-friendly manner. In summary, our research paper aims to contribute to the intersection of mental health and technology, emphasizing the significance of early diagnosis using NLP techniques.