A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data

A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data

Bibliographic Details
Main Author: Saxena, Ankita
Language:English
Published: Wright State University / OhioLINK 2018
Subjects:
Computer Science
Major Depressive Disorder
MDD
depression
PHQ-9
social media
Twitter
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=wright152764172911888
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-wright1527641729118882021-08-03T07:07:09Z A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data Saxena, Ankita Computer Science Major Depressive Disorder MDD depression PHQ-9 social media Twitter According to the World Health Organization, more than 300 million people suffer from Major Depressive Disorder (MDD) worldwide. PHQ-9 is used to screen and diagnose MDD clinically and identify its severity. With the unprecedented growth and enthusiastic acceptance of social media such as Twitter, a large number of people have come to share their feelings and emotions on it openly. Each tweet can indicate a user’s opinion, thought or feeling. A tweet can also indicate multiple symptoms related to PHQ-9. Identifying PHQ-9 symptoms indicated by a tweet can provide crucial information about a user regarding his/her depression diagnosis. The current state-of-the-art approach using supervised machine learning to classify a tweet regarding PHQ-9 symptoms relies on explicit reference to a particular PHQ-9 symptom, i.e., it considers an exact string matching-based feature representation. This approach of explicit referencing falls short on classifying tweets having an implicit symptom indicator in several possible PHQ-9 symptoms. This thesis proposes a semantically enhanced approach that considers explicit as well as implicit depression-indicative symptoms. We better capture the semantics of a word in a tweet as it relates to depression condition by employing the context of the word indicated by the surrounding words using Word2Vec model trained on a corpus of ~3 million tweets. Using a two-stage (binary class - multi-label) classification model, we demonstrate that our approach outperforms the baseline model for depression-indicative symptoms by around 20% on f-measure. We further evaluated our semantically-enhanced approach to fill in the PHQ-9 questionnaire and identify the severity of depression by standard guidelines by considering a dataset of 932,108 self-reported users. 2018-06-25 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright152764172911888 http://rave.ohiolink.edu/etdc/view?acc_num=wright152764172911888 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center.
collection NDLTD
language English
sources NDLTD
topic Computer Science
Major Depressive Disorder
MDD
depression
PHQ-9
social media
Twitter
spellingShingle Computer Science
Major Depressive Disorder
MDD
depression
PHQ-9
social media
Twitter
Saxena, Ankita
A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data
author Saxena, Ankita
author_facet Saxena, Ankita
author_sort Saxena, Ankita
title A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data
title_short A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data
title_full A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data
title_fullStr A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data
title_full_unstemmed A Semantically Enhanced Approach to Identify Depression-Indicative Symptoms Using Twitter Data
title_sort semantically enhanced approach to identify depression-indicative symptoms using twitter data
publisher Wright State University / OhioLINK
publishDate 2018
url http://rave.ohiolink.edu/etdc/view?acc_num=wright152764172911888
work_keys_str_mv AT saxenaankita asemanticallyenhancedapproachtoidentifydepressionindicativesymptomsusingtwitterdata
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