Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing
Autor(a) principal: | |
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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/10400.26/43366 |
Resumo: | The technological expansion in recent years has created opportunities for competitive advantage by applying new Data-driven approaches to Data-driven Marketing practices. Large and small companies from diverse sectors can take advantage of the enormous amount of data generated to develop new business models, open new sources of revenue and initiate disruptive innovations. This dissertation seeks to analyse the use of different sentiment analysis models for data disclosed on social networks, specifically on Twitter, in a set of disclosures classified as Nonprofit Marketing. This study seeks to innovate in the contribution to knowledge by studying for the first time the relationship between message predictors and user Engagement on Twitter by Nonprofit Organisations. In this study, the quantitative method is used with the application of Machine Learning techniques for classifying tweets based on Positive, Negative and Neutral sentiments. The collected data were subordinated to statistical studies, namely Spearman's correlation. This research in the Nonprofit Marketing sector demonstrates that it is possible to predict which sentiment expressed in the message will have the best Engagement, thus generating innovative communications for followers that will lead to increased interaction. It should be noted that Positive messages tend to affect user Engagement negatively. |
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Data-Driven Marketing: a sentiment alalysis study in Nonprofit MarketingData-driven marketingNonprofit marketingNonprofit organisationsMachine learningSentiment analysisThe technological expansion in recent years has created opportunities for competitive advantage by applying new Data-driven approaches to Data-driven Marketing practices. Large and small companies from diverse sectors can take advantage of the enormous amount of data generated to develop new business models, open new sources of revenue and initiate disruptive innovations. This dissertation seeks to analyse the use of different sentiment analysis models for data disclosed on social networks, specifically on Twitter, in a set of disclosures classified as Nonprofit Marketing. This study seeks to innovate in the contribution to knowledge by studying for the first time the relationship between message predictors and user Engagement on Twitter by Nonprofit Organisations. In this study, the quantitative method is used with the application of Machine Learning techniques for classifying tweets based on Positive, Negative and Neutral sentiments. The collected data were subordinated to statistical studies, namely Spearman's correlation. This research in the Nonprofit Marketing sector demonstrates that it is possible to predict which sentiment expressed in the message will have the best Engagement, thus generating innovative communications for followers that will lead to increased interaction. It should be noted that Positive messages tend to affect user Engagement negatively.Pedrosa, Isabel Maria MendesMoro, Sérgio Miguel CarneiroRepositório ComumBorges, Marcus Vinicius Estrela2023-12-22T01:30:45Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.26/43366TID:203195582enginfo: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:RCAAP2023-12-28T02:15:28Zoai:comum.rcaap.pt:10400.26/43366Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T16:45:59.064608Repositório 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 |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing |
title |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing |
spellingShingle |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing Borges, Marcus Vinicius Estrela Data-driven marketing Nonprofit marketing Nonprofit organisations Machine learning Sentiment analysis |
title_short |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing |
title_full |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing |
title_fullStr |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing |
title_full_unstemmed |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing |
title_sort |
Data-Driven Marketing: a sentiment alalysis study in Nonprofit Marketing |
author |
Borges, Marcus Vinicius Estrela |
author_facet |
Borges, Marcus Vinicius Estrela |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pedrosa, Isabel Maria Mendes Moro, Sérgio Miguel Carneiro Repositório Comum |
dc.contributor.author.fl_str_mv |
Borges, Marcus Vinicius Estrela |
dc.subject.por.fl_str_mv |
Data-driven marketing Nonprofit marketing Nonprofit organisations Machine learning Sentiment analysis |
topic |
Data-driven marketing Nonprofit marketing Nonprofit organisations Machine learning Sentiment analysis |
description |
The technological expansion in recent years has created opportunities for competitive advantage by applying new Data-driven approaches to Data-driven Marketing practices. Large and small companies from diverse sectors can take advantage of the enormous amount of data generated to develop new business models, open new sources of revenue and initiate disruptive innovations. This dissertation seeks to analyse the use of different sentiment analysis models for data disclosed on social networks, specifically on Twitter, in a set of disclosures classified as Nonprofit Marketing. This study seeks to innovate in the contribution to knowledge by studying for the first time the relationship between message predictors and user Engagement on Twitter by Nonprofit Organisations. In this study, the quantitative method is used with the application of Machine Learning techniques for classifying tweets based on Positive, Negative and Neutral sentiments. The collected data were subordinated to statistical studies, namely Spearman's correlation. This research in the Nonprofit Marketing sector demonstrates that it is possible to predict which sentiment expressed in the message will have the best Engagement, thus generating innovative communications for followers that will lead to increased interaction. It should be noted that Positive messages tend to affect user Engagement negatively. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-12-22T01:30:45Z |
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/10400.26/43366 TID:203195582 |
url |
http://hdl.handle.net/10400.26/43366 |
identifier_str_mv |
TID:203195582 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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