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UID:calendar.315646.field_event_date.0@www.wright.edu
DTSTAMP:20260219T215153Z
CREATED:20180411T182645Z
DESCRIPTION:Committee:  Drs. Amit Sheth\, Advisor\, TK Prasad\, and Tanvi B
 anerjeeABSTRACT:'According to the World Health Organization\, more than 30
 0M people suffer from Major Depressive Disorder (MDD) worldwide. PHQ-9 is 
 used to diagnose MDD clinically\, and its severity identification.  With t
 he unprecedented growth of social media like Twitter\, a large number of p
 eople have come to share their feelings and emotions on it. These social m
 edia messages\, specifically tweets can be classified into PHQ-9 symptoms.
  The current approaches classify tweets to PHQ-9 symptoms do not consider 
 text semantics which is crucial for this classification. In this study\, w
 e explore the potential of using Twitter to detect the depression-indicati
 ve symptoms such as depressed mood\, suicidal thoughts. We provide a seman
 tically enhanced approach to achieve this by using machine learning techni
 ques. Our 2-stage (binary class - multi-label) classification model outper
 formed state-of-the-art for depression-indicative symptoms classification 
 model by 20%. We further evaluated our semantically-enhanced approach to f
 ill out the PHQ-9 questionnaire and identify the degree of depression from
  it using the standard guidelines. We show our approach outperforms the ex
 isting approaches by examples.'
DTSTART;TZID=America/New_York:20180416T103000
DTEND;TZID=America/New_York:20180416T123000
LAST-MODIFIED:20180411T190546Z
LOCATION:366 Joshi
SUMMARY:Masters Thesis Defense “A Semantically Enhanced Approach to Identif
 y Depression-Indicative Symptoms Using Twitter Data” By Ankita Saxena
URL;TYPE=URI:/events/masters-thesis-defense-%E2%80%9C
 -semantically-enhanced-approach-identify-depression-indicative
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