title: Proactive screening for depression through metaphorical and automatic text analysis.
abstract: OBJECTIVE
  Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  Depression can be automatically screened in texts and the mental health system may benefit from this screening ability.
authors: Department of Education, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel. yneuman@bgu.ac.il
authors: Yair
authors: Y
authors: Neuman
authors: Yohai
authors: Y
authors: Cohen
authors: Dan
authors: D
authors: Assaf
authors: Gabbi
authors: G
authors: Kedma
mesh_terms: D003863:Depression / Q000175:diagnosis*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008403:Mass Screening / Q000379:methods; D019365:Metaphor; D009323:Natural Language Processing