With mental health as a problem domain in NLP, the bulk of contemporary literature revolves around building better mental illness prediction models. The research focusing on the identification of discussion clusters in online mental health communitie
s has been relatively limited. Moreover, as the underlying methodologies used in these studies mainly conform to the traditional machine learning models and statistical methods, the scope for introducing contextualized word representations for topic and theme extraction from online mental health communities remains open. Thus, in this research, we propose topic-infused deep contextualized representations, a novel data representation technique that uses autoencoders to combine deep contextual embeddings with topical information, generating robust representations for text clustering. Investigating the Reddit discourse on Post-Traumatic Stress Disorder (PTSD) and Complex Post-Traumatic Stress Disorder (C-PTSD), we elicit the thematic clusters representing the latent topics and themes discussed in the r/ptsd and r/CPTSD subreddits. Furthermore, we also present a qualitative analysis and characterization of each cluster, unraveling the prevalent discourse themes.
The research aims to detect mental health and emotional stability level in a sample of outstanding students in the province of Damascus and knowledge of the relationship between them, and they have a knowledge of the differences in the level of menta
l health and emotional stability, according to the variable (sex). The research sample consisted of (288) students from outstanding students were chosen the way of intentionality Al-Basel School, and dish them measurements of emotional stability and mental health, and are prepared by the researcher after verifying of validity and reliability.
