Depression classification based on automatic ontology generation and natural language processing

Introduction Depression is one of the most common mental disorders and one that has a great potential to affect people mentally, physically, and socially. Unfortunately, a majority of people either do not have access to treatment or avoid seeking help. In this context, many platforms have emerged to provide a space for discussion and support where users can interact anonymously. Methods This study presents the results of our research on classifying depression in a specific community of religious people by analyzing texts posted on social media using semantic techniques, such as a comparative analysis of texts from users using ontologies. A supporting objective of the research was to create a natural language processing tool for classifying depression to obtain the corpus necessary for ontology creation. Results The resulting ontologies were analyzed and compared with each other and also with existing ontologies in the literature on general depression. Discussion The comparison was both qualitative and quantitative, taking into consideration similarity ratios for the quantitative comparison.