{"id":"https://openalex.org/W3014803340","doi":"https://doi.org/10.1109/access.2020.2985228","title":"Identifying Emotion Labels From Psychiatric Social Texts Using a Bi-Directional LSTM-CNN Model","display_name":"Identifying Emotion Labels From Psychiatric Social Texts Using a Bi-Directional LSTM-CNN Model","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3014803340","doi":"https://doi.org/10.1109/access.2020.2985228","mag":"3014803340"},"language":"en","primary_location":{"id":"doi:10.1109/access.2020.2985228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2985228","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09055024.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09055024.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087778670","display_name":"Jheng-Long Wu","orcid":"https://orcid.org/0000-0003-3494-5507"},"institutions":[{"id":"https://openalex.org/I185940356","display_name":"Soochow University","ror":"https://ror.org/05kvm7n82","country_code":"TW","type":"education","lineage":["https://openalex.org/I185940356"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jheng-Long Wu","raw_affiliation_strings":["School of Big Data Management, Soochow University, Taipei City, Taiwan"],"raw_orcid":"https://orcid.org/0000-0003-3494-5507","affiliations":[{"raw_affiliation_string":"School of Big Data Management, Soochow University, Taipei City, Taiwan","institution_ids":["https://openalex.org/I185940356"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076711937","display_name":"Yuanye He","orcid":"https://orcid.org/0000-0002-3431-904X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210156404","display_name":"Institute of Information Engineering","ror":"https://ror.org/04r53se39","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]},{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["CN","TW"],"is_corresponding":false,"raw_author_name":"Yuanye He","raw_affiliation_strings":["Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan","Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3431-904X","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan","institution_ids":["https://openalex.org/I99908691"]},{"raw_affiliation_string":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210156404"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085633092","display_name":"Liang-Chih Yu","orcid":"https://orcid.org/0000-0003-1443-4347"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Liang-Chih Yu","raw_affiliation_strings":["Department of Information Management, Yuan Ze University, Taoyuan City, Taiwan"],"raw_orcid":"https://orcid.org/0000-0003-1443-4347","affiliations":[{"raw_affiliation_string":"Department of Information Management, Yuan Ze University, Taoyuan City, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044292096","display_name":"K. Robert Lai","orcid":"https://orcid.org/0000-0002-3365-3927"},"institutions":[{"id":"https://openalex.org/I99908691","display_name":"Yuan Ze University","ror":"https://ror.org/01fv1ds98","country_code":"TW","type":"education","lineage":["https://openalex.org/I99908691"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"K. Robert Lai","raw_affiliation_strings":["Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-3365-3927","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City, Taiwan","institution_ids":["https://openalex.org/I99908691"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":4.6802,"has_fulltext":true,"cited_by_count":58,"citation_normalized_percentile":{"value":0.95651221,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"8","issue":null,"first_page":"66638","last_page":"66646"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9980999827384949,"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"}},"topics":[{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9980999827384949,"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/T10028","display_name":"Topic Modeling","score":0.9980000257492065,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9961000084877014,"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/computer-science","display_name":"Computer science","score":0.7356635332107544},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.531139075756073},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.46318694949150085},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3332976698875427}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7356635332107544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.531139075756073},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46318694949150085},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3332976698875427}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2020.2985228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2985228","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09055024.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:60690229247340a68bcf5c4a2e0eddf5","is_oa":true,"landing_page_url":"https://doaj.org/article/60690229247340a68bcf5c4a2e0eddf5","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 8, Pp 66638-66646 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2020.2985228","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2020.2985228","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09055024.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4699999988079071}],"awards":[{"id":"https://openalex.org/G1762151700","display_name":"Topic Adaptation for Dimensional Sentiment Recognition","funder_award_id":"MOST107-2628-E155-002-MY3","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"},{"id":"https://openalex.org/G6438829638","display_name":"An Stock Trading Prediction System Using Deep Generative Adversarial Neural Netowrk","funder_award_id":"MOST107-2218-E031-002-MY2","funder_id":"https://openalex.org/F4320322795","funder_display_name":"Ministry of Science and Technology, Taiwan"}],"funders":[{"id":"https://openalex.org/F4320322795","display_name":"Ministry of Science and Technology, Taiwan","ror":"https://ror.org/02kv4zf79"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3014803340.pdf","grobid_xml":"https://content.openalex.org/works/W3014803340.grobid-xml"},"referenced_works_count":58,"referenced_works":["https://openalex.org/W1503259811","https://openalex.org/W1576514601","https://openalex.org/W1614298861","https://openalex.org/W1753402186","https://openalex.org/W1953606363","https://openalex.org/W1981313612","https://openalex.org/W2017637319","https://openalex.org/W2022328512","https://openalex.org/W2031115546","https://openalex.org/W2046297116","https://openalex.org/W2052684427","https://openalex.org/W2056290938","https://openalex.org/W2114315281","https://openalex.org/W2121029939","https://openalex.org/W2131774270","https://openalex.org/W2166912588","https://openalex.org/W2186575060","https://openalex.org/W2250539671","https://openalex.org/W2468785836","https://openalex.org/W2511592266","https://openalex.org/W2514588627","https://openalex.org/W2573123177","https://openalex.org/W2610135452","https://openalex.org/W2618530766","https://openalex.org/W2751576135","https://openalex.org/W2754051771","https://openalex.org/W2759645571","https://openalex.org/W2776249353","https://openalex.org/W2792280837","https://openalex.org/W2792763200","https://openalex.org/W2794259165","https://openalex.org/W2801716390","https://openalex.org/W2803881474","https://openalex.org/W2806806521","https://openalex.org/W2810576820","https://openalex.org/W2838248257","https://openalex.org/W2885554300","https://openalex.org/W2889149926","https://openalex.org/W2890623737","https://openalex.org/W2890733607","https://openalex.org/W2894089136","https://openalex.org/W2894196255","https://openalex.org/W2896457183","https://openalex.org/W2901737885","https://openalex.org/W2920873208","https://openalex.org/W2926264417","https://openalex.org/W2945943453","https://openalex.org/W2963874170","https://openalex.org/W2998704965","https://openalex.org/W6636510571","https://openalex.org/W6677758222","https://openalex.org/W6680532216","https://openalex.org/W6682691769","https://openalex.org/W6683557909","https://openalex.org/W6691326280","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6756449097"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W3204019825"],"abstract_inverted_index":{"Discussion":[0],"features":[1,95],"in":[2,24,39,99,107],"online":[3],"communities":[4,46],"can":[5,30,118],"be":[6],"effectively":[7,33],"used":[8],"to":[9,18,22,53,79,122,136,154],"diagnose":[10],"depression":[11],"and":[12,32,74,187],"allow":[13],"other":[14,169],"users":[15],"or":[16],"experts":[17],"provide":[19,48],"self-help":[20],"resources":[21],"those":[23],"need.":[25],"Automatic":[26],"emotion":[27,81],"identification":[28],"models":[29,170],"quickly":[31],"highlight":[34],"indicators":[35,157],"of":[36,42,104,158],"emotional":[37,159],"stress":[38],"the":[40],"text":[41],"such":[43,175],"discussions.":[44],"Such":[45],"also":[47,131],"patients":[49],"with":[50,151],"important":[51,124],"knowledge":[52],"help":[54],"better":[55],"understand":[56],"their":[57],"condition.":[58],"This":[59],"study":[60],"proposes":[61],"a":[62,90,101,108],"deep":[63,128],"learning":[64,129],"framework":[65,130,167],"combining":[66],"word":[67,133,152],"embeddings,":[68],"bi-directional":[69],"long":[70],"short-term":[71],"memory":[72],"(Bi-LSTM),":[73],"convolutional":[75],"neural":[76],"networks":[77],"(CNN)":[78],"identify":[80,156],"labels":[82],"from":[83,96],"psychiatric":[84],"social":[85],"texts.":[86],"The":[87,142],"Bi-LSTM":[88],"is":[89,112],"powerful":[91,114,147],"mechanism":[92],"for":[93],"extracting":[94],"sequential":[97],"data":[98],"which":[100,117],"sentence":[102],"consists":[103],"multiple":[105],"words":[106],"particular":[109],"sequence.":[110],"CNN":[111],"another":[113],"feature":[115,148,173],"extractor":[116],"convolute":[119],"many":[120],"blocks":[121],"capture":[123],"features.":[125],"Our":[126],"proposed":[127,166],"applies":[132],"representation":[134],"techniques":[135],"represent":[137],"semantic":[138,180],"relationships":[139],"between":[140],"words.":[141],"paper":[143],"thus":[144],"combines":[145],"two":[146],"extraction":[149,174],"methods":[150],"embedding":[153],"automatically":[155],"stress.":[160],"Experimental":[161],"results":[162],"show":[163],"that":[164],"our":[165],"outperformed":[168],"using":[171],"traditional":[172],"as":[176],"bag-of-words":[177],"(BOW),":[178],"latent":[179],"analysis":[181,185],"(LSA),":[182],"independent":[183],"component":[184],"(ICA),":[186],"LSA+ICA.":[188]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":2}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}
