Analyzing user reviews of messaging Apps for competitive analysis

Detalhes bibliográficos
Autor(a) principal: Liang, Wenyi
Data de Publicação: 2022
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/133017
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Analyzing user reviews of messaging Apps for competitive analysisCompetitive analysisTopic modelingSentiment analysisText miningUser reviewsMessaging appsDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe rise of various messaging apps has resulted in intensively fierce competition, and the era of Web 2.0 enables business managers to gain competitive intelligence from user-generated content (UGC). Text-mining UGC for competitive intelligence has been drawing great interest of researchers. However, relevant studies mostly focus on industries such as hospitality and products, and few studies applied such techniques to effectively perform competitive analysis for messaging apps. Here, we conducted a competitive analysis based on topic modeling and sentiment analysis by text-mining 27,479 user reviews of four iOS messaging apps, namely Messenger, WhatsApp, Signal and Telegram. The results show that the performance of topic modeling and sentiment analysis is encouraging, and that a combination of the extracted app aspect-based topics and the adjusted sentiment scores can effectively reveal meaningful competitive insights into user concerns, competitive strengths and weaknesses as well as changes of user sentiments over time. We anticipate that this study will not only advance the existing literature on competitive analysis using text mining techniques for messaging apps but also help existing players and new entrants in the market to sharpen their competitive edge by better understanding their user needs and the industry trends.Castelli, MauroRUNLiang, Wenyi2022-02-16T18:28:08Z2022-01-282022-01-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/133017TID:202942694enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:11:43Zoai:run.unl.pt:10362/133017Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:41.024848Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Analyzing user reviews of messaging Apps for competitive analysis
title Analyzing user reviews of messaging Apps for competitive analysis
spellingShingle Analyzing user reviews of messaging Apps for competitive analysis
Liang, Wenyi
Competitive analysis
Topic modeling
Sentiment analysis
Text mining
User reviews
Messaging apps
title_short Analyzing user reviews of messaging Apps for competitive analysis
title_full Analyzing user reviews of messaging Apps for competitive analysis
title_fullStr Analyzing user reviews of messaging Apps for competitive analysis
title_full_unstemmed Analyzing user reviews of messaging Apps for competitive analysis
title_sort Analyzing user reviews of messaging Apps for competitive analysis
author Liang, Wenyi
author_facet Liang, Wenyi
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Liang, Wenyi
dc.subject.por.fl_str_mv Competitive analysis
Topic modeling
Sentiment analysis
Text mining
User reviews
Messaging apps
topic Competitive analysis
Topic modeling
Sentiment analysis
Text mining
User reviews
Messaging apps
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2022
dc.date.none.fl_str_mv 2022-02-16T18:28:08Z
2022-01-28
2022-01-28T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/133017
TID:202942694
url http://hdl.handle.net/10362/133017
identifier_str_mv TID:202942694
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
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