Improving international attractiveness of higher education institutions based on text mining and sentiment analysis
Main Author: | |
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Publication Date: | 2018 |
Other Authors: | , |
Format: | Article |
Language: | eng |
Source: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
Download full: | http://hdl.handle.net/10071/15669 |
Summary: | Purpose: The increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the Internet. HEIs also benefit from such information online. This paper aims to provide an understanding of the drivers of HEIs success online. Design/methodology/approach: Due to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analyzing such information manually. The present paper uses text mining and sentiment analysis to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with AACSB accreditation. Findings: Results show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment. Research limitations/implications Despite the use of a major platform with a broad number of reviews from students around the world, other sources focused on other types of HEIs may have been used to reinforce the findings in the current paper. Originality/value: The study pioneers the use of text mining and sentiment analysis to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions. |
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Improving international attractiveness of higher education institutions based on text mining and sentiment analysisHigher educationSentiment analysisText miningInternational student mobilityTopic modellingPurpose: The increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the Internet. HEIs also benefit from such information online. This paper aims to provide an understanding of the drivers of HEIs success online. Design/methodology/approach: Due to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analyzing such information manually. The present paper uses text mining and sentiment analysis to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with AACSB accreditation. Findings: Results show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment. Research limitations/implications Despite the use of a major platform with a broad number of reviews from students around the world, other sources focused on other types of HEIs may have been used to reinforce the findings in the current paper. Originality/value: The study pioneers the use of text mining and sentiment analysis to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions.Emerald2018-04-19T16:12:58Z2018-01-01T00:00:00Z20182019-03-20T12:16:49Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/15669eng0951-354X10.1108/IJEM-01-2017-0027Santos, C. L.Rita, P.Guerreiro, J.info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-07-07T03:04:43Zoai:repositorio.iscte-iul.pt:10071/15669Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T18:15:21.617603Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
dc.title.none.fl_str_mv |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis |
title |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis |
spellingShingle |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis Santos, C. L. Higher education Sentiment analysis Text mining International student mobility Topic modelling |
title_short |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis |
title_full |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis |
title_fullStr |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis |
title_full_unstemmed |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis |
title_sort |
Improving international attractiveness of higher education institutions based on text mining and sentiment analysis |
author |
Santos, C. L. |
author_facet |
Santos, C. L. Rita, P. Guerreiro, J. |
author_role |
author |
author2 |
Rita, P. Guerreiro, J. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Santos, C. L. Rita, P. Guerreiro, J. |
dc.subject.por.fl_str_mv |
Higher education Sentiment analysis Text mining International student mobility Topic modelling |
topic |
Higher education Sentiment analysis Text mining International student mobility Topic modelling |
description |
Purpose: The increasing competition among higher education institutions (HEI) has led students to conduct a more in-depth analysis to choose where to study abroad. Since students are usually unable to visit each HEIs before making their decision, they are strongly influenced by what is written by former international students (IS) on the Internet. HEIs also benefit from such information online. This paper aims to provide an understanding of the drivers of HEIs success online. Design/methodology/approach: Due to the increasing amount of information published online, HEIs have to use automatic techniques to search for patterns instead of analyzing such information manually. The present paper uses text mining and sentiment analysis to study online reviews of IS about their HEIs. The paper studied 1938 reviews from 65 different business schools with AACSB accreditation. Findings: Results show that HEIs may become more attractive online if they financially support students cost of living, provide courses in English, and promote an international environment. Research limitations/implications Despite the use of a major platform with a broad number of reviews from students around the world, other sources focused on other types of HEIs may have been used to reinforce the findings in the current paper. Originality/value: The study pioneers the use of text mining and sentiment analysis to highlight topics and sentiments mentioned in online reviews by students attending HEIs, clarifying how such opinions are correlated with satisfaction. Using such information, HEIs’ managers may focus their efforts on promoting international attractiveness of their institutions. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-04-19T16:12:58Z 2018-01-01T00:00:00Z 2018 2019-03-20T12:16:49Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
format |
article |
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publishedVersion |
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http://hdl.handle.net/10071/15669 |
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http://hdl.handle.net/10071/15669 |
dc.language.iso.fl_str_mv |
eng |
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eng |
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0951-354X 10.1108/IJEM-01-2017-0027 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Emerald |
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Emerald |
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