Screening Internet forum participants for depression symptoms by assembling and enhancing multiple NLP methods. Issue 1 (June 2015)

• Record Type: Journal Article
• Authors: Karmen, Christian; Hsiung, Robert C.; Wetter, Thomas
• Is Part Of: Computer methods and programs in biomedicine. Volume 120:Issue 1(2015)
• Page Start: 27
• Page End: 36
• Publication Date: 2015-06
• Subjects: Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.03.008
• Languages: English
• ISSNs: 0169-2607

Abstract

<abstract abstract-type="author" id="abs0005"> <title id="sect0005">Abstract</title> <sec> <p id="spar0005">Depression is a disease that can dramatically lower quality of life. Symptoms of depression can range from temporary sadness to suicide. Embarrassment, shyness, and the stigma of depression are some of the factors preventing people from getting help for their problems. Contemporary social media technologies like Internet forums or micro-blogs give people the opportunity to talk about their feelings in a confidential anonymous environment. However, many participants in such networks may not recognize the severity of their depression and their need for professional help. Our approach is to develop a method that detects symptoms of depression in <italic>free text</italic>, such as posts in Internet forums, chat rooms and the like. This could help people appreciate the significance of their depression and realize they need to seek help. In this work Natural Language Processing methods are used to break the textual information into its grammatical units. Further analysis involves detection of depression symptoms and their frequency with the help of words known as indicators of depression and their synonyms. Finally, similar to common paper-based depression scales, e.g., the CES-D, that information is incorporated into a single depression score.</p> <p id="spar0010">In this evaluation study, our depressive mood detection system, <italic>DepreSD</italic> (<bold>Depre</bold>ssion <bold>S</bold>ymptom <bold>D</bold>etection), had an average precision of 0.84 (range 0.72–1.0 depending on the specific measure) and an average <italic>F</italic> measure of 0.79 (range 0.72–0.9).</p> </sec> </abstract>