Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

Social media are an online means of interaction among individuals. People are increasingly using social media, especially online communities, to discuss health concerns and seek support. Understanding topics, sentiment, and structures of these communities informs important aspects of health-related conditions. There has been growing research interest in analysing online mental health communities; however, analysis of these communities with health concerns has been limited. This paper investigates and identifies latent meta-groups of online communities with and without mental health-related conditions including depression and autism. Large datasets from online communities were crawled. We analyse sentiment-based, psycholinguistics-based and topic-based features from blog posts made by members of these online communities. The work focuses on using nonparametric methods to infer latent topics automatically from the corpus of affective words in the blog posts. The visualization of the discovered meta-communities in their use of latent topics shows a difference between the groups. This presents evidence of the emotion-bearing difference in online mental health-related communities, suggesting a possible angle for support and intervention. The methodology might offer potential machine learning techniques for research and practice in psychiatry.