{"id":"https://openalex.org/W4415251867","doi":"https://doi.org/10.1109/icmla66185.2025.00027","title":"Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models","display_name":"Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models","publication_year":2025,"publication_date":"2025-12-03","ids":{"openalex":"https://openalex.org/W4415251867","doi":"https://doi.org/10.1109/icmla66185.2025.00027"},"language":"en","primary_location":{"id":"doi:10.1109/icmla66185.2025.00027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.16542","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101619752","display_name":"Khalid Hasan","orcid":"https://orcid.org/0000-0003-0448-9344"},"institutions":[{"id":"https://openalex.org/I119942284","display_name":"Missouri State University","ror":"https://ror.org/01d2sez20","country_code":"US","type":"education","lineage":["https://openalex.org/I119942284"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Khalid Hasan","raw_affiliation_strings":["Missouri State University,Dept. of Computer Science,Springfield,MO,USA"],"affiliations":[{"raw_affiliation_string":"Missouri State University,Dept. of Computer Science,Springfield,MO,USA","institution_ids":["https://openalex.org/I119942284"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078131344","display_name":"Jamil Saquer","orcid":"https://orcid.org/0009-0006-7948-8409"},"institutions":[{"id":"https://openalex.org/I119942284","display_name":"Missouri State University","ror":"https://ror.org/01d2sez20","country_code":"US","type":"education","lineage":["https://openalex.org/I119942284"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jamil Saquer","raw_affiliation_strings":["Missouri State University,Dept. of Computer Science,Springfield,MO,USA"],"affiliations":[{"raw_affiliation_string":"Missouri State University,Dept. of Computer Science,Springfield,MO,USA","institution_ids":["https://openalex.org/I119942284"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102860798","display_name":"Yifan Zhang","orcid":"https://orcid.org/0000-0002-5832-2622"},"institutions":[{"id":"https://openalex.org/I119942284","display_name":"Missouri State University","ror":"https://ror.org/01d2sez20","country_code":"US","type":"education","lineage":["https://openalex.org/I119942284"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yifan Zhang","raw_affiliation_strings":["Missouri State University,Dept. of Computer Science,Springfield,MO,USA"],"affiliations":[{"raw_affiliation_string":"Missouri State University,Dept. of Computer Science,Springfield,MO,USA","institution_ids":["https://openalex.org/I119942284"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101619752"],"corresponding_institution_ids":["https://openalex.org/I119942284"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.22548891,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"160","last_page":"167"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9272000193595886,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.9272000193595886,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9024999737739563,"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/transformer","display_name":"Transformer","score":0.6841999888420105},{"id":"https://openalex.org/keywords/mental-health","display_name":"Mental health","score":0.6226000189781189},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5892000198364258},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5800999999046326},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.44519999623298645},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.36399999260902405},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.35120001435279846},{"id":"https://openalex.org/keywords/exploratory-research","display_name":"Exploratory research","score":0.35089999437332153}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7360000014305115},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6841999888420105},{"id":"https://openalex.org/C134362201","wikidata":"https://www.wikidata.org/wiki/Q317309","display_name":"Mental health","level":2,"score":0.6226000189781189},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5892000198364258},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5800999999046326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5690000057220459},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5562000274658203},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.44519999623298645},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.38440001010894775},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.35120001435279846},{"id":"https://openalex.org/C85973986","wikidata":"https://www.wikidata.org/wiki/Q1091731","display_name":"Exploratory research","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C2992695702","wikidata":"https://www.wikidata.org/wiki/Q317309","display_name":"Mental healthcare","level":3,"score":0.34299999475479126},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.260699987411499},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C3018260909","wikidata":"https://www.wikidata.org/wiki/Q1322871","display_name":"Exploratory analysis","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icmla66185.2025.00027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla66185.2025.00027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2509.16542","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.16542","pdf_url":"https://arxiv.org/pdf/2509.16542","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.16542","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.16542","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.16542","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.16542","pdf_url":"https://arxiv.org/pdf/2509.16542","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Millions":[0],"of":[1,17,45,65,82,92,217],"people":[2],"openly":[3],"share":[4],"mental":[5,53,76,83,97,200,218],"health":[6,54,77,84,98,201,219],"struggles":[7],"on":[8,35,206],"social":[9],"media,":[10],"providing":[11],"rich":[12],"data":[13],"for":[14,49,198,214],"early":[15],"detection":[16],"conditions":[18,99],"such":[19],"as":[20],"depression,":[21],"bipolar":[22],"disorder,":[23],"etc.":[24],"However,":[25],"most":[26],"prior":[27],"Natural":[28],"Language":[29],"Processing":[30],"(NLP)":[31],"research":[32],"has":[33],"focused":[34],"single-disorder":[36],"identification,":[37],"leaving":[38],"a":[39,61,89,101],"gap":[40],"in":[41],"understanding":[42],"the":[43,151,194],"efficacy":[44],"advanced":[46],"NLP":[47,220],"techniques":[48],"distinguishing":[50],"among":[51],"multiple":[52],"conditions.":[55,85,142],"In":[56],"this":[57],"work,":[58],"we":[59],"present":[60],"large-scale":[62],"comparative":[63],"study":[64],"state-of-the-art":[66],"transformer":[67,119,147,170],"versus":[68],"Long":[69],"Short-Term":[70],"Memory":[71],"(LSTM)-based":[72],"models":[73,148],"to":[74,111,173,187],"classify":[75],"posts":[78,94],"into":[79],"exclusive":[80],"categories":[81],"We":[86,115],"first":[87,195],"curate":[88],"large":[90],"dataset":[91],"Reddit":[93],"spanning":[95],"six":[96],"and":[100,107,125,158,209],"control":[102],"group,":[103],"using":[104,135,183],"rigorous":[105],"filtering":[106],"statistical":[108],"exploratory":[109],"analysis":[110],"ensure":[112],"annotation":[113],"quality.":[114],"then":[116],"evaluate":[117],"five":[118],"architectures":[120],"(BERT,":[121],"RoBERTa,":[122],"DistilBERT,":[123],"ALBERT,":[124],"ELECTRA)":[126],"against":[127],"several":[128],"LSTM":[129],"variants":[130],"(with":[131],"or":[132,137],"without":[133],"attention,":[134],"contextual":[136],"static":[138,184],"embeddings)":[139],"under":[140],"identical":[141],"Experimental":[143],"results":[144],"show":[145],"that":[146],"consistently":[149],"outperform":[150],"alternatives,":[152],"with":[153,166],"RoBERTa":[154],"achieving":[155],"91-99%":[156],"F1-scores":[157],"accuracies":[159],"across":[160],"all":[161],"classes.":[162],"Notably,":[163],"attention-augmented":[164],"LSTMs":[165,182],"BERT":[167],"embeddings":[168,185],"approach":[169],"performance":[171],"(up":[172],"97%":[174],"F1-score)":[175],"while":[176],"training":[177],"2-3.5":[178],"times":[179],"faster,":[180],"whereas":[181],"fail":[186],"learn":[188],"useful":[189],"signals.":[190],"These":[191],"findings":[192],"represent":[193],"comprehensive":[196],"benchmark":[197],"multi-class":[199],"detection,":[202],"offering":[203],"practical":[204],"guidance":[205],"model":[207],"selection":[208],"highlighting":[210],"an":[211],"accuracy\u2013efficiency":[212],"trade-off":[213],"real-world":[215],"deployment":[216],"systems.":[221]},"counts_by_year":[],"updated_date":"2026-04-09T06:08:40.794217","created_date":"2025-10-16T00:00:00"}