Improving international attractiveness of higher education institutions based on text mining and sentiment analysis

Bibliographic Details
Main Author: Santos, C. L.
Publication Date: 2018
Other Authors: Rita, P., Guerreiro, J.
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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spelling 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
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language eng
dc.relation.none.fl_str_mv 0951-354X
10.1108/IJEM-01-2017-0027
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dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Emerald
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