Proactive screening for depression through metaphorical and automatic text analysis
Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge. The system implementing the methodology  Pedesis  harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a depression lexicon. The lexicon is used to automatically evaluate the level of depression in texts or whether the text is dealing with depression as a topic. Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p<.001) whether a post includes signs of depression. By comparing the system's prediction to the judgment of human experts we achieved an average 78% precision and 76% recall. Depression can be automatically screened in texts and the mental health system may benefit from this screening ability.
depression automatic screening mental health natural language processing