Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks
This work addresses early detection for mental health intervention, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of detecting suicidal ideation and mental disorders from social content by enhancing text representation with sentiment scores and latent topics, and using attentive relation networks to prioritize critical relational features, achieving superior performance on three real-world datasets.
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without effective treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. However, classifying suicidal ideation and other mental disorders is challenging as they share similar patterns in language usage and sentimental polarity. This paper enhances text representation with lexicon-based sentiment scores and latent topics and proposes using relation networks to detect suicidal ideation and mental disorders with related risk indicators. The relation module is further equipped with the attention mechanism to prioritize more critical relational features. Through experiments on three real-world datasets, our model outperforms most of its counterparts.