{"id":{"repo_id":"houston","oai_identifier":"oai:uh-ir.tdl.org:10657/3089"},"canonical_url":"https://search.dev.ndltd.org/etd/houston/oai:uh-ir.tdl.org:10657/3089","repository":{"repo_id":"houston","name":"University of Houston","base_url":"https://uh-ir.tdl.org/server/oai/request"},"display":{"title":"Early Detection of Depression","abstract":"Depression is a mental disorder that affects more than 300 million people worldwide. An individual suffering from depression functions poorly in life, is prone to other diseases and in the worst-case, depression leads to suicide. There are many impediments that prevent expert care from reaching people suffering from depression in time. Impediments such as social stigma associated with mental disorders, lack of trained health-care professionals and ignorance of the signs of depression owing to a lack of awareness of the disease. Moreover, the World Health Organization (WHO) claims that individuals who are depressed are often not correctly diagnosed and others who are misdiagnosed are prescribed antidepressants. Thus, there is a strong need to automatically assess the risk of depression. Identification of depression from social media has been framed as a classification problem in the field of Natural Language Processing (NLP). In this work we study NLP approaches that can successfully extract information from textual data to enhance identification of depression. These NLP approaches perform feature extraction to build document representations. The issues of detecting depression in a social media environment is data scarcity for users with depression and the inherent noise associated with social media data. We attempt to address those issues by using representations that can naturally cope with a social media environment. Specifically, we propose the usage of Distributed Term Representations (DTRs) to capture information that can be used by supervised machine learning methods for learning and classifying users suffering from depression. Experimental evaluation provides evidence that DTRs are more effective for depression detection than traditional representations such as Bag of Words (BOW) and representations based on neural word embeddings. In fact, we have obtained state-of-the-art results with Document Occurrence Representation (DOR) for depression detection (F1-Score 0.66 on the depressed class). For early detection of depression, we have obtained the lowest reported Early Risk Detection Error (ERDE) using Pyramidal a newly adapted method that is used for computing document representations.","abstract_html":"Depression is a mental disorder that affects more than 300 million people worldwide. An individual suffering from depression functions poorly in life, is prone to other diseases and in the worst-case, depression leads to suicide. There are many impediments that prevent expert care from reaching people suffering from depression in time. Impediments such as social stigma associated with mental disorders, lack of trained health-care professionals and ignorance of the signs of depression owing to a lack of awareness of the disease. Moreover, the World Health Organization (WHO) claims that individuals who are depressed are often not correctly diagnosed and others who are misdiagnosed are prescribed antidepressants. Thus, there is a strong need to automatically assess the risk of depression. Identification of depression from social media has been framed as a classification problem in the field of Natural Language Processing (NLP). In this work we study NLP approaches that can successfully extract information from textual data to enhance identification of depression. These NLP approaches perform feature extraction to build document representations. The issues of detecting depression in a social media environment is data scarcity for users with depression and the inherent noise associated with social media data. We attempt to address those issues by using representations that can naturally cope with a social media environment. Specifically, we propose the usage of Distributed Term Representations (DTRs) to capture information that can be used by supervised machine learning methods for learning and classifying users suffering from depression. Experimental evaluation provides evidence that DTRs are more effective for depression detection than traditional representations such as Bag of Words (BOW) and representations based on neural word embeddings. In fact, we have obtained state-of-the-art results with Document Occurrence Representation (DOR) for depression detection (F1-Score 0.66 on the depressed class). For early detection of depression, we have obtained the lowest reported Early Risk Detection Error (ERDE) using Pyramidal a newly adapted method that is used for computing document representations.","abstract_has_math":false,"creators":["Kulkarni, Akshay Bhavani Kumar 1994-"],"institution":"University of Houston","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Solorio, Thamar"],"committee_chairs":[],"committee_members":["Gonzalez, Fabio A.","Eick, Christoph F."],"year":2018,"date_issued":"2018-05","date_published":"2018-05","updated_at":"2026-07-24T02:32:58Z","subjects":["Natural Language Processing","Health care"],"languages":["eng"],"rights":["The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10657/3089","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Solorio, Thamar"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Gonzalez, Fabio A.","Eick, Christoph F."]},{"key":"dc:creator","label":"Author","values":["Kulkarni, Akshay Bhavani Kumar 1994-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-06-22T21:51:49Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-06-22T21:51:49Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Houston"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Natural Language Processing","Health care"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["The author of this work is the copyright owner. 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Impediments such as social stigma associated with mental disorders, lack of trained health-care professionals and ignorance of the signs of depression owing to a lack of awareness of the disease. Moreover, the World Health Organization (WHO) claims that individuals who are depressed are often not correctly diagnosed and others who are misdiagnosed are prescribed antidepressants. Thus, there is a strong need to automatically assess the risk of depression. Identification of depression from social media has been framed as a classification problem in the field of Natural Language Processing (NLP). In this work we study NLP approaches that can successfully extract information from textual data to enhance identification of depression. These NLP approaches perform feature extraction to build document representations. The issues of detecting depression in a social media environment is data scarcity for users with depression and the inherent noise associated with social media data. We attempt to address those issues by using representations that can naturally cope with a social media environment. Specifically, we propose the usage of Distributed Term Representations (DTRs) to capture information that can be used by supervised machine learning methods for learning and classifying users suffering from depression. Experimental evaluation provides evidence that DTRs are more effective for depression detection than traditional representations such as Bag of Words (BOW) and representations based on neural word embeddings. In fact, we have obtained state-of-the-art results with Document Occurrence Representation (DOR) for depression detection (F1-Score 0.66 on the depressed class). For early detection of depression, we have obtained the lowest reported Early Risk Detection Error (ERDE) using Pyramidal a newly adapted method that is used for computing document representations."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Early Detection of Depression"]}]}],"canonical_facts":{"dc:contributor.advisor":["Solorio, Thamar"],"dc:contributor.committeemember":["Gonzalez, Fabio A.","Eick, Christoph F."],"dc:creator":["Kulkarni, Akshay Bhavani Kumar 1994-"],"dc:date.accessioned":["2018-06-22T21:51:49Z"],"dc:date.available":["2018-06-22T21:51:49Z"],"dc:date.issued":["2018-05"],"dc:description.abstract":["Depression is a mental disorder that affects more than 300 million people worldwide. An individual suffering from depression functions poorly in life, is prone to other diseases and in the worst-case, depression leads to suicide. 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The issues of detecting depression in a social media environment is data scarcity for users with depression and the inherent noise associated with social media data. We attempt to address those issues by using representations that can naturally cope with a social media environment. Specifically, we propose the usage of Distributed Term Representations (DTRs) to capture information that can be used by supervised machine learning methods for learning and classifying users suffering from depression. Experimental evaluation provides evidence that DTRs are more effective for depression detection than traditional representations such as Bag of Words (BOW) and representations based on neural word embeddings. In fact, we have obtained state-of-the-art results with Document Occurrence Representation (DOR) for depression detection (F1-Score 0.66 on the depressed class). For early detection of depression, we have obtained the lowest reported Early Risk Detection Error (ERDE) using Pyramidal a newly adapted method that is used for computing document representations."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/10657/3089"],"dc:language.iso":["eng"],"dc:rights":["The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s)."],"dc:subject":["Natural Language Processing","Health care"],"dc:title":["Early Detection of Depression"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["University of Houston"]},"updated_at":"2026-07-24T02:32:58Z"}