{"id":"https://openalex.org/W3041365740","doi":null,"title":"Automatic Detection of Emotions and Distress in Textual Data","display_name":"Automatic Detection of Emotions and Distress in Textual Data","publication_year":2019,"publication_date":"2019-10-10","ids":{"openalex":"https://openalex.org/W3041365740","mag":"3041365740"},"language":"en","primary_location":{"id":"pmh:oai:https://spectrum.library.concordia.ca:986153","doi":null,"is_oa":true,"landing_page_url":"https://spectrum.library.concordia.ca/986153/1/Mohammadi_MCS_S2020.pdf","pdf_url":"https://spectrum.library.concordia.ca/986153/1/Mohammadi_MCS_S2020.pdf","source":{"id":"https://openalex.org/S4306400871","display_name":"Spectrum Research Repository (Concordia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I60158472","host_organization_name":"Concordia University","host_organization_lineage":["https://openalex.org/I60158472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://spectrum.library.concordia.ca/986153/1/Mohammadi_MCS_S2020.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101512542","display_name":"Elham Mohammadi","orcid":"https://orcid.org/0000-0002-9386-3299","observed_orcids":["https://orcid.org/0000-0002-9386-3299"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Elham Mohammadi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-9386-3299","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5101512542"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.02539999969303608,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/emotion-recognition","display_name":"emotion recognition","score":0.8560000061988831},{"id":"https://openalex.org/keywords/distress-detection","display_name":"distress detection","score":0.9850000143051147},{"id":"https://openalex.org/keywords/online-discussion-forums","display_name":"online forums","score":0.6570000052452087},{"id":"https://openalex.org/keywords/attention-based-recurrent-neural-network","display_name":"attention-based recurrent neural network","score":0.7749999761581421},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.6830000281333923},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.8619999885559082},{"id":"https://openalex.org/keywords/mental-health-assessment","display_name":"mental health assessment","score":0.7450000047683716},{"id":"https://openalex.org/keywords/suicide-risk-assessment","display_name":"suicide risk assessment","score":0.9520000219345093},{"id":"https://openalex.org/keywords/low-resource-classification","display_name":"low-resource classification","score":0.45399999618530273}],"concepts":[{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7379890084266663},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6913343667984009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6351163387298584},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.610283374786377},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6075280904769897},{"id":"https://openalex.org/C2988148770","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion detection","level":3,"score":0.5989826321601868},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4856525659561157},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39265066385269165},{"id":"https://openalex.org/C2777438025","wikidata":"https://www.wikidata.org/wiki/Q1339090","display_name":"Emotion recognition","level":2,"score":0.2594890594482422}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:https://spectrum.library.concordia.ca:986153","doi":null,"is_oa":true,"landing_page_url":"https://spectrum.library.concordia.ca/986153/1/Mohammadi_MCS_S2020.pdf","pdf_url":"https://spectrum.library.concordia.ca/986153/1/Mohammadi_MCS_S2020.pdf","source":{"id":"https://openalex.org/S4306400871","display_name":"Spectrum Research Repository (Concordia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I60158472","host_organization_name":"Concordia University","host_organization_lineage":["https://openalex.org/I60158472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},{"id":"mag:3041365740","doi":null,"is_oa":false,"landing_page_url":"https://spectrum.library.concordia.ca/986153/","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:https://spectrum.library.concordia.ca:986153","doi":null,"is_oa":true,"landing_page_url":"https://spectrum.library.concordia.ca/986153/1/Mohammadi_MCS_S2020.pdf","pdf_url":"https://spectrum.library.concordia.ca/986153/1/Mohammadi_MCS_S2020.pdf","source":{"id":"https://openalex.org/S4306400871","display_name":"Spectrum Research Repository (Concordia University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I60158472","host_organization_name":"Concordia University","host_organization_lineage":["https://openalex.org/I60158472"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},"sustainable_development_goals":[{"score":0.504800021648407,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3041365740.pdf","grobid_xml":"https://content.openalex.org/works/W3041365740.grobid-xml"},"referenced_works_count":60,"referenced_works":["https://openalex.org/W1133916940","https://openalex.org/W1423339008","https://openalex.org/W1566376227","https://openalex.org/W1614298861","https://openalex.org/W1632114991","https://openalex.org/W1815076433","https://openalex.org/W2019759670","https://openalex.org/W2050730017","https://openalex.org/W2064675550","https://openalex.org/W2094934653","https://openalex.org/W2104518905","https://openalex.org/W2123442489","https://openalex.org/W2131744502","https://openalex.org/W2132339004","https://openalex.org/W2133046612","https://openalex.org/W2146089916","https://openalex.org/W2166183437","https://openalex.org/W2250539671","https://openalex.org/W2252031683","https://openalex.org/W2294703018","https://openalex.org/W2323015674","https://openalex.org/W2493916176","https://openalex.org/W2512306956","https://openalex.org/W2575276796","https://openalex.org/W2577646479","https://openalex.org/W2582664174","https://openalex.org/W2595653137","https://openalex.org/W2607154244","https://openalex.org/W2739681832","https://openalex.org/W2741447225","https://openalex.org/W2741937156","https://openalex.org/W2750747353","https://openalex.org/W2758424050","https://openalex.org/W2787560479","https://openalex.org/W2787579701","https://openalex.org/W2794557536","https://openalex.org/W2805744755","https://openalex.org/W2806393999","https://openalex.org/W2806752996","https://openalex.org/W2807452501","https://openalex.org/W2885726350","https://openalex.org/W2889497047","https://openalex.org/W2892118011","https://openalex.org/W2899771611","https://openalex.org/W2911964244","https://openalex.org/W2919115771","https://openalex.org/W2953786528","https://openalex.org/W2954778211","https://openalex.org/W2955429306","https://openalex.org/W2962772361","https://openalex.org/W2963341956","https://openalex.org/W2963777125","https://openalex.org/W2964121744","https://openalex.org/W2964308564","https://openalex.org/W2966773117","https://openalex.org/W2991136969","https://openalex.org/W3088524227","https://openalex.org/W3089014067","https://openalex.org/W3197497981","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W3197199054","https://openalex.org/W2991136969","https://openalex.org/W7046484342","https://openalex.org/W4312037052","https://openalex.org/W4414500452","https://openalex.org/W4205601613","https://openalex.org/W4293234659","https://openalex.org/W4394885742","https://openalex.org/W4320836716","https://openalex.org/W4220883868"],"abstract_inverted_index":{"Online":[0,18],"data":[1,19],"can":[2,20,40,104,125,212],"be":[3,22,41,105],"analyzed":[4],"for":[5,27,69,145,153],"many":[6],"purposes,":[7],"including":[8],"the":[9,28,154,191,216,275],"prediction":[10],"of":[11,36,57,89,156,210,218,247,259,266,278,283],"stock":[12],"market,":[13],"business,":[14],"and":[15,32,120,148,173,234,243,257,268,271,285,290],"political":[16],"planning.":[17],"also":[21],"used":[23,42,180],"to":[24,134,170,184],"develop":[25],"systems":[26,39],"automatic":[29],"emotion":[30,71,74,146],"detection":[31,75,158,246],"mental":[33],"health":[34],"assessment":[35],"users.":[37],"These":[38],"as":[43,127,181],"complementary":[44],"measures":[45],"in":[46,55,76,132,215,254,274,281],"monitoring":[47],"online":[48,160],"forums":[49],"by":[50,99,116],"detecting":[51],"users":[52],"who":[53],"are":[54,178],"need":[56],"attention.":[58],"\\n":[59,139,223],"\\nIn":[60],"this":[61],"thesis,":[62],"we":[63],"first":[64,253,273,276],"present":[65],"a":[66,84,90,100,118,128,135,185,219],"new":[67],"approach":[68,80,152,164,201],"contextual":[70],"detection,":[72,147],"i.e.":[73],"short":[77],"conversations.":[78],"The":[79,109,250],"is":[81],"based":[82],"on":[83,229],"neural":[85,92,106,137,168],"feature":[86],"extractor,":[87],"composed":[88],"recurrent":[91],"network":[93],"with":[94],"an":[95,150,199],"attention":[96],"mechanism,":[97],"followed":[98],"final":[101,130,192],"classifier,":[102],"that":[103,197],"or":[107],"SVM-based.":[108],"results":[110],"from":[111,159],"our":[112,143],"experiments":[113,195],"showed":[114],"that,":[115],"providing":[117],"higher":[119],"more":[121],"robust":[122],"performance,":[123],"SVM":[124],"act":[126],"better":[129],"classifier":[131],"comparison":[133],"feed-forward":[136],"network.":[138],"\\nWe":[140],"then":[141],"extended":[142,163,225],"model":[144,226,251],"created":[149],"ensemble":[151,200],"task":[155,277],"distress":[157],"data.":[161],"This":[162],"utilizes":[165],"several":[166],"attention-based":[167],"sub-models":[169,206],"extract":[171],"features":[172,183],"predict":[174],"class":[175],"probabilities,":[176],"which":[177,202,237],"later":[179],"input":[182],"Support":[186],"Vector":[187],"Machine":[188],"(SVM)":[189],"making":[190],"classification.":[193],"Our":[194],"show":[196],"using":[198],"makes":[203],"use":[204],"different":[205],"accessing":[207],"diverse":[208],"sources":[209],"information":[211],"improve":[213],"classification":[214],"absence":[217],"large":[220],"annotated":[221],"dataset.":[222],"\\nThe":[224],"was":[227],"evaluated":[228],"two":[230],"shared":[231],"tasks,":[232],"CLPsych":[233,260],"eRisk":[235,279],"2019,":[236],"aim":[238],"at":[239],"suicide":[240],"risk":[241,245],"assessment,":[242],"early":[244],"anorexia,":[248],"respectively.":[249],"ranked":[252,272],"tasks":[255],"A":[256],"C":[258],"2019":[261,280],"(with":[262],"macro-average":[263],"F1":[264,284,287],"scores":[265,288],"0.481":[267],"0.268,":[269],"respectively),":[270],"terms":[282],"latency-weighted":[286],"(0.71":[289],"0.69,":[291],"respectively).":[292]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2020-07-16T00:00:00"}
