{"id":"https://openalex.org/W4390044993","doi":"https://doi.org/10.1109/icast57874.2023.10359279","title":"Predicting Depression on Twitter with Word Embedding by Pretrained Language Model","display_name":"Predicting Depression on Twitter with Word Embedding by Pretrained Language Model","publication_year":2023,"publication_date":"2023-11-09","ids":{"openalex":"https://openalex.org/W4390044993","doi":"https://doi.org/10.1109/icast57874.2023.10359279"},"language":"en","primary_location":{"id":"doi:10.1109/icast57874.2023.10359279","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icast57874.2023.10359279","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 12th International Conference on Awareness Science and Technology (iCAST)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093548456","display_name":"Shen Zheqi","orcid":null},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shen Zheqi","raw_affiliation_strings":["University of Aizu,Aizu-Wakamatsu, Fukushima,Japan","University of Aizu, Aizu-Wakamatsu, Fukushima, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Aizu,Aizu-Wakamatsu, Fukushima,Japan","institution_ids":["https://openalex.org/I141591182"]},{"raw_affiliation_string":"University of Aizu, Aizu-Wakamatsu, Fukushima, Japan","institution_ids":["https://openalex.org/I141591182"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071160608","display_name":"Incheon Paik","orcid":"https://orcid.org/0000-0002-7554-8180"},"institutions":[{"id":"https://openalex.org/I141591182","display_name":"University of Aizu","ror":"https://ror.org/02pg0e883","country_code":"JP","type":"education","lineage":["https://openalex.org/I141591182"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Paik Incheon","raw_affiliation_strings":["University of Aizu,Aizu-Wakamatsu, Fukushima,Japan","University of Aizu, Aizu-Wakamatsu, Fukushima, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Aizu,Aizu-Wakamatsu, Fukushima,Japan","institution_ids":["https://openalex.org/I141591182"]},{"raw_affiliation_string":"University of Aizu, Aizu-Wakamatsu, Fukushima, Japan","institution_ids":["https://openalex.org/I141591182"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I141591182"],"apc_list":null,"apc_paid":null,"fwci":0.5102,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.69575393,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"1","issue":null,"first_page":"247","last_page":"252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12488","display_name":"Mental Health via Writing","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9990000128746033,"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":0.9745000004768372,"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/word-embedding","display_name":"Word embedding","score":0.8684581518173218},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7398501634597778},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.7157991528511047},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.676594614982605},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.6354089379310608},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.629984438419342},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.6063213348388672},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5726462006568909},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5626083612442017},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.5449317097663879},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5414242148399353},{"id":"https://openalex.org/keywords/depression","display_name":"Depression (economics)","score":0.4843655526638031},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3953092694282532},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.21063163876533508},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.1542680859565735},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08565157651901245}],"concepts":[{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.8684581518173218},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7398501634597778},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.7157991528511047},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.676594614982605},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.6354089379310608},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.629984438419342},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.6063213348388672},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5726462006568909},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5626083612442017},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.5449317097663879},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5414242148399353},{"id":"https://openalex.org/C2776867660","wikidata":"https://www.wikidata.org/wiki/Q1814941","display_name":"Depression (economics)","level":2,"score":0.4843655526638031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3953092694282532},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.21063163876533508},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.1542680859565735},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08565157651901245},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C139719470","wikidata":"https://www.wikidata.org/wiki/Q39680","display_name":"Macroeconomics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icast57874.2023.10359279","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/icast57874.2023.10359279","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 12th International Conference on Awareness Science and Technology (iCAST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2402700","https://openalex.org/W2803564469","https://openalex.org/W2806881496","https://openalex.org/W2896457183","https://openalex.org/W2962848499","https://openalex.org/W3010476030","https://openalex.org/W3012362537","https://openalex.org/W3083151008","https://openalex.org/W3120655260","https://openalex.org/W3138407803","https://openalex.org/W4301581299","https://openalex.org/W6755207826","https://openalex.org/W6773820404","https://openalex.org/W6774970780","https://openalex.org/W6782310419"],"related_works":["https://openalex.org/W947140380","https://openalex.org/W3186997021","https://openalex.org/W4200618314","https://openalex.org/W4308088897","https://openalex.org/W4286432911","https://openalex.org/W4230884544","https://openalex.org/W4245453790","https://openalex.org/W3194985222","https://openalex.org/W3216571906","https://openalex.org/W4214830338"],"abstract_inverted_index":{"Depression":[0],"is":[1],"a":[2,28,36,40,110],"common":[3],"mental":[4],"illness.":[5],"In":[6],"the":[7,13,46,71,75,86,92,114],"field":[8],"of":[9,15,48,95],"Natural":[10],"Language":[11],"Processing,":[12],"research":[14,32],"how":[16],"to":[17,34,43,66,84,90],"detect":[18],"and":[19,61,64,82],"analyze":[20],"users'":[21],"sentiment":[22],"from":[23,105],"social":[24],"media":[25],"has":[26],"been":[27],"hot":[29],"topic.":[30],"This":[31],"aims":[33],"achieve":[35],"prediction":[37,115],"on":[38,97],"whether":[39],"user":[41],"tends":[42],"depression":[44],"by":[45],"content":[47],"his":[49],"or":[50],"her":[51],"tweets.":[52],"We":[53],"selected":[54],"several":[55],"different":[56],"pretrained":[57,106],"word":[58],"embedding":[59,103],"methods":[60],"classification":[62],"models":[63,108],"tried":[65],"apply":[67],"them":[68],"for":[69],"analyzing":[70],"sentiments":[72],"contained":[73],"in":[74,113],"Twitter":[76],"dataset.":[77],"The":[78],"results":[79],"were":[80],"compared":[81],"evaluated":[83],"find":[85],"most":[87],"efficient":[88],"method":[89],"predict":[91],"depressive":[93],"tendencies":[94],"users":[96],"Twitter.":[98],"Our":[99],"experiments":[100],"show":[101],"that":[102],"vectors":[104],"BERT":[107],"have":[109],"definite":[111],"advantage":[112],"task.":[116]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
