Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.23919/CISTI.2017.7975730 http://hdl.handle.net/11449/175024 |
Resumo: | Social networks are of significant analytical interest. This is because their data are generated in great quantity, and intermittently, besides that, the data are from a wide variety, and it is widely available to users. Through such data, it is desired to extract knowledge or information that can be used in decision-making activities. In this context, we have identified the lack of methods that apply data mining techniques to the task of analyzing the professional profile of employees. The aim of such analyses is to detect competencies that are of greater interest by being more required and also, to identify their associative relations. Thus, this work introduces MineraSkill methodology that deals with methods to infer the desired profile of a candidate for a job vacancy. In order to do so, we use keyword detection via natural language processing techniques; which are related to others by inferring their association rules. The results are presented in the form of a case study, which analyzed data from LinkedIn, demonstrating the potential of the methodology in indicating trending competencies that are required together. |
id |
UNSP_f8a56681b5b22f6c933cccc4b4272043 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/175024 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkillData mining on LinkedIn data to define professional profile via MineraSkill methodologyData MiningMineraSkillNatural Language ProcessingProfessional ProfileSocial networks are of significant analytical interest. This is because their data are generated in great quantity, and intermittently, besides that, the data are from a wide variety, and it is widely available to users. Through such data, it is desired to extract knowledge or information that can be used in decision-making activities. In this context, we have identified the lack of methods that apply data mining techniques to the task of analyzing the professional profile of employees. The aim of such analyses is to detect competencies that are of greater interest by being more required and also, to identify their associative relations. Thus, this work introduces MineraSkill methodology that deals with methods to infer the desired profile of a candidate for a job vacancy. In order to do so, we use keyword detection via natural language processing techniques; which are related to others by inferring their association rules. The results are presented in the form of a case study, which analyzed data from LinkedIn, demonstrating the potential of the methodology in indicating trending competencies that are required together.Departamento de Matemática e Computação (DMC) Universidade Estadual Paulista (FCT/UNESP) Presidente PrudenteDepartamento de Matemática e Computação (DMC) Universidade Estadual Paulista (FCT/UNESP) Presidente PrudenteUniversidade Estadual Paulista (Unesp)Caldeira, Dayane C. M. F. [UNESP]Correia, Ronaldo C. M. [UNESP]Spadon, Gabriel [UNESP]Eler, Danilo M. [UNESP]Olivete-Jr, Celso [UNESP]Garcia, Rogerio E. [UNESP]2018-12-11T17:13:53Z2018-12-11T17:13:53Z2017-07-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.23919/CISTI.2017.7975730Iberian Conference on Information Systems and Technologies, CISTI.2166-07352166-0727http://hdl.handle.net/11449/17502410.23919/CISTI.2017.79757302-s2.0-850270688922616135175972629Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporIberian Conference on Information Systems and Technologies, CISTI0,136info:eu-repo/semantics/openAccess2024-06-19T14:32:26Zoai:repositorio.unesp.br:11449/175024Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:55:52.765858Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill Data mining on LinkedIn data to define professional profile via MineraSkill methodology |
title |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill |
spellingShingle |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill Caldeira, Dayane C. M. F. [UNESP] Data Mining MineraSkill Natural Language Processing Professional Profile |
title_short |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill |
title_full |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill |
title_fullStr |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill |
title_full_unstemmed |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill |
title_sort |
Mineração de Dados no LinkedIn para Definição do Perfil Profissional com a Metodologia MineraSkill |
author |
Caldeira, Dayane C. M. F. [UNESP] |
author_facet |
Caldeira, Dayane C. M. F. [UNESP] Correia, Ronaldo C. M. [UNESP] Spadon, Gabriel [UNESP] Eler, Danilo M. [UNESP] Olivete-Jr, Celso [UNESP] Garcia, Rogerio E. [UNESP] |
author_role |
author |
author2 |
Correia, Ronaldo C. M. [UNESP] Spadon, Gabriel [UNESP] Eler, Danilo M. [UNESP] Olivete-Jr, Celso [UNESP] Garcia, Rogerio E. [UNESP] |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Caldeira, Dayane C. M. F. [UNESP] Correia, Ronaldo C. M. [UNESP] Spadon, Gabriel [UNESP] Eler, Danilo M. [UNESP] Olivete-Jr, Celso [UNESP] Garcia, Rogerio E. [UNESP] |
dc.subject.por.fl_str_mv |
Data Mining MineraSkill Natural Language Processing Professional Profile |
topic |
Data Mining MineraSkill Natural Language Processing Professional Profile |
description |
Social networks are of significant analytical interest. This is because their data are generated in great quantity, and intermittently, besides that, the data are from a wide variety, and it is widely available to users. Through such data, it is desired to extract knowledge or information that can be used in decision-making activities. In this context, we have identified the lack of methods that apply data mining techniques to the task of analyzing the professional profile of employees. The aim of such analyses is to detect competencies that are of greater interest by being more required and also, to identify their associative relations. Thus, this work introduces MineraSkill methodology that deals with methods to infer the desired profile of a candidate for a job vacancy. In order to do so, we use keyword detection via natural language processing techniques; which are related to others by inferring their association rules. The results are presented in the form of a case study, which analyzed data from LinkedIn, demonstrating the potential of the methodology in indicating trending competencies that are required together. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-07-11 2018-12-11T17:13:53Z 2018-12-11T17:13:53Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.23919/CISTI.2017.7975730 Iberian Conference on Information Systems and Technologies, CISTI. 2166-0735 2166-0727 http://hdl.handle.net/11449/175024 10.23919/CISTI.2017.7975730 2-s2.0-85027068892 2616135175972629 |
url |
http://dx.doi.org/10.23919/CISTI.2017.7975730 http://hdl.handle.net/11449/175024 |
identifier_str_mv |
Iberian Conference on Information Systems and Technologies, CISTI. 2166-0735 2166-0727 10.23919/CISTI.2017.7975730 2-s2.0-85027068892 2616135175972629 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Iberian Conference on Information Systems and Technologies, CISTI 0,136 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129140561281024 |