BACKGROUND
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 large number 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.
OBJECTIVE
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.
METHODS
This study presents the results of our research on classifying depression in the specific community of religious people by analyzing texts posted on social media using semantic techniques, such as comparative analysis of texts from users using ontologies.
RESULTS
There were no significant differences in similarities between the various categories examined in this study. This suggests that, with respect to the characteristics under consideration, the categories did not exhibit substantial variations. Related to the comparison between religious individuals diagnosed with depression and the general depressed population, our analysis indicated a noteworthy distinction. Specifically, the classification based on words utilized by the religious group highlighted the significance of the religious dimension in the context of depression.
CONCLUSIONS
This research underscores the potential of language analysis on social media platforms as a tool for gaining insights into the experience of depression. Additionally, the study has shed light on the distinctive role that religion can play in the coping mechanisms of religious individuals facing depression.