{"id":"https://openalex.org/W4224021698","doi":"https://doi.org/10.1080/08839514.2022.2064047","title":"Identifying Labor Market Competitors with Machine Learning Based on Maimai Platform","display_name":"Identifying Labor Market Competitors with Machine Learning Based on Maimai Platform","publication_year":2022,"publication_date":"2022-04-18","ids":{"openalex":"https://openalex.org/W4224021698","doi":"https://doi.org/10.1080/08839514.2022.2064047"},"language":"en","primary_location":{"id":"doi:10.1080/08839514.2022.2064047","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2022.2064047","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2022.2064047?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2022.2064047?needAccess=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088938639","display_name":"Yu Zheng","orcid":"https://orcid.org/0000-0003-0661-0777"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zheng","raw_affiliation_strings":["Renmin University of China","School of Mathematics, Renmin University of China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China","institution_ids":["https://openalex.org/I78988378"]},{"raw_affiliation_string":"School of Mathematics, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051166273","display_name":"Yonghong Long","orcid":null},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yonghong Long","raw_affiliation_strings":["Renmin University of China","School of Mathematics, Renmin University of China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China","institution_ids":["https://openalex.org/I78988378"]},{"raw_affiliation_string":"School of Mathematics, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053382960","display_name":"Honggang Fan","orcid":"https://orcid.org/0000-0002-6609-6864"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Honggang Fan","raw_affiliation_strings":["Renmin University of China","School of Mathematics, Renmin University of China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Renmin University of China","institution_ids":["https://openalex.org/I78988378"]},{"raw_affiliation_string":"School of Mathematics, Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5053382960"],"corresponding_institution_ids":["https://openalex.org/I78988378"],"apc_list":{"value":2195,"currency":"USD","value_usd":2195},"apc_paid":{"value":2195,"currency":"USD","value_usd":2195},"fwci":0.7873,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.75643479,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"36","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9768999814987183,"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":0.9768999814987183,"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/T14139","display_name":"E-commerce and Technology Innovations","score":0.9067000150680542,"subfield":{"id":"https://openalex.org/subfields/1403","display_name":"Business and International Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/competitor-analysis","display_name":"Competitor analysis","score":0.855972170829773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7305480241775513},{"id":"https://openalex.org/keywords/sentiment-analysis","display_name":"Sentiment analysis","score":0.6586589217185974},{"id":"https://openalex.org/keywords/competition","display_name":"Competition (biology)","score":0.6513093709945679},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5530343055725098},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5094433426856995},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4958425462245941},{"id":"https://openalex.org/keywords/predictive-power","display_name":"Predictive power","score":0.4900580644607544},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46341460943222046},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.4400615096092224},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3437940776348114},{"id":"https://openalex.org/keywords/marketing","display_name":"Marketing","score":0.20400935411453247},{"id":"https://openalex.org/keywords/business","display_name":"Business","score":0.11015927791595459}],"concepts":[{"id":"https://openalex.org/C127576917","wikidata":"https://www.wikidata.org/wiki/Q624630","display_name":"Competitor analysis","level":2,"score":0.855972170829773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7305480241775513},{"id":"https://openalex.org/C66402592","wikidata":"https://www.wikidata.org/wiki/Q2271421","display_name":"Sentiment analysis","level":2,"score":0.6586589217185974},{"id":"https://openalex.org/C91306197","wikidata":"https://www.wikidata.org/wiki/Q45767","display_name":"Competition (biology)","level":2,"score":0.6513093709945679},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5530343055725098},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5094433426856995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4958425462245941},{"id":"https://openalex.org/C2778136018","wikidata":"https://www.wikidata.org/wiki/Q10350689","display_name":"Predictive power","level":2,"score":0.4900580644607544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46341460943222046},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.4400615096092224},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3437940776348114},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.20400935411453247},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.11015927791595459},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/08839514.2022.2064047","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2022.2064047","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2022.2064047?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:96eb4829f64f4a978762464cab0985c8","is_oa":false,"landing_page_url":"https://doaj.org/article/96eb4829f64f4a978762464cab0985c8","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Applied Artificial Intelligence, Vol 36, Iss 1 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/08839514.2022.2064047","is_oa":true,"landing_page_url":"https://doi.org/10.1080/08839514.2022.2064047","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/08839514.2022.2064047?needAccess=true","source":{"id":"https://openalex.org/S125501549","display_name":"Applied Artificial Intelligence","issn_l":"0883-9514","issn":["0883-9514","1087-6545"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Applied Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4224021698.pdf","grobid_xml":"https://content.openalex.org/works/W4224021698.grobid-xml"},"referenced_works_count":54,"referenced_works":["https://openalex.org/W1998271713","https://openalex.org/W2005624335","https://openalex.org/W2022427793","https://openalex.org/W2054397708","https://openalex.org/W2077563243","https://openalex.org/W2082205840","https://openalex.org/W2084046180","https://openalex.org/W2086670327","https://openalex.org/W2087821294","https://openalex.org/W2089337643","https://openalex.org/W2104021674","https://openalex.org/W2129285443","https://openalex.org/W2137365826","https://openalex.org/W2152839203","https://openalex.org/W2163759112","https://openalex.org/W2174706414","https://openalex.org/W2252926514","https://openalex.org/W2262482865","https://openalex.org/W2577339472","https://openalex.org/W2581845629","https://openalex.org/W2586733088","https://openalex.org/W2605595296","https://openalex.org/W2742985510","https://openalex.org/W2751539017","https://openalex.org/W2809382919","https://openalex.org/W2896741190","https://openalex.org/W2941524612","https://openalex.org/W2964649370","https://openalex.org/W3004206374","https://openalex.org/W3012500520","https://openalex.org/W3021143228","https://openalex.org/W3040291805","https://openalex.org/W3096320564","https://openalex.org/W3113335300","https://openalex.org/W3116492707","https://openalex.org/W3123724460","https://openalex.org/W3125370119","https://openalex.org/W3129712087","https://openalex.org/W3138199476","https://openalex.org/W3153719856","https://openalex.org/W3166219158","https://openalex.org/W3195994896","https://openalex.org/W3217501503","https://openalex.org/W4200122912","https://openalex.org/W4200238055","https://openalex.org/W4205930438","https://openalex.org/W4206134861","https://openalex.org/W4237337958","https://openalex.org/W4237791300","https://openalex.org/W4238144524","https://openalex.org/W4239890301","https://openalex.org/W4243369280","https://openalex.org/W4245769868","https://openalex.org/W4251238685"],"related_works":["https://openalex.org/W2768297557","https://openalex.org/W4317653575","https://openalex.org/W4362544966","https://openalex.org/W4281692210","https://openalex.org/W3192794374","https://openalex.org/W4362613237","https://openalex.org/W2045133795","https://openalex.org/W2517139124","https://openalex.org/W4309192283","https://openalex.org/W4312922676"],"abstract_inverted_index":{"The":[0,162],"demand":[1],"for":[2],"skilled":[3],"labor":[4,21,36,71,92],"has":[5,107],"increased":[6],"dramatically":[7],"in":[8,20,74,110,154],"the":[9,17,42,48,54,63,70,75,144,150,155,178,187],"current":[10],"knowledge-based":[11],"economy,":[12],"which":[13],"is":[14,102],"characterized":[15],"by":[16,148],"growing":[18],"intensity":[19],"market":[22,37,72,93],"competition":[23,73,94,190],"between":[24],"firms.":[25],"Therefore,":[26],"it":[27,106],"would":[28],"be":[29],"of":[30,45,114,120,131,181],"special":[31],"interest":[32],"to":[33,68,89,118,137],"identify":[34],"future":[35],"competitors.":[38],"At":[39],"present,":[40],"with":[41,62,85,95],"vast":[43],"amount":[44],"textual":[46],"data,":[47,121],"existing":[49,125],"study":[50],"focuses":[51],"on":[52,79,159],"constructing":[53],"human":[55],"capital":[56],"overlap":[57,60],"and":[58,105,176],"product":[59],"metrics":[61,168],"text":[64,97],"data":[65],"as":[66],"predictors":[67],"predict":[69,90],"United":[76],"States.":[77],"Based":[78],"these":[80],"metrics,":[81],"this":[82,138,141],"paper":[83,142],"experiments":[84],"machine":[86],"learning":[87],"methods":[88],"Chinese":[91,96],"data.":[98],"Furthermore,":[99],"sentiment":[100,128,145,183],"analysis":[101,129,146,184,191],"becoming":[103],"popular":[104],"been":[108],"used":[109],"a":[111],"wide":[112],"variety":[113],"fields.":[115],"However,":[116],"due":[117],"lack":[119],"there":[122],"are":[123],"few":[124],"studies":[126],"using":[127],"approach":[130],"firms\u2019":[132,156],"online":[133,157],"reviews.":[134],"In":[135],"response":[136],"research":[139],"gap,":[140],"constructs":[143],"metric":[147],"mining":[149],"emotional":[151],"content":[152],"expressed":[153],"reviews":[158],"Maimai\u2019s":[160],"platform.":[161],"results":[163],"show":[164],"that":[165],"our":[166],"proposed":[167,182],"have":[169],"superior":[170],"predictive":[171,179],"power":[172],"over":[173],"conventional":[174],"measures":[175],"highlight":[177],"utility":[180],"metric.":[185],"Moreover,":[186],"nuanced":[188],"two-dimensional":[189],"gives":[192],"some":[193],"interesting":[194],"results.":[195]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-04T06:09:54.619538","created_date":"2025-10-10T00:00:00"}
