Customer Review Analysis
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/10362/145481 |
Resumo: | Internship Report 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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Customer Review AnalysisBERTopicSentence EmbeddingsText MiningTopic ModelingUnsupervised LearningInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceOver the last years, Cerascreen has grown rapidly and expanded into more than 20 countries, always focusing on offering more diverse products, supplements, and services. Unfortunately, it collected a lot of data during these years, which was not yet stored, losing valuable insights. In a new initiative Cerascreen wants to be the most trusted digital predictive health platform. Therefore, it intends to utilize its data to understand its customers better and offer superior products and services according to the customer’s needs. The focus of this internship report was to find a way to analyze Cerascreen’s customers’ reviews to understand its customers better and respond to properly the given feedback. In addition, since the reviews have not been stored before, this report also deals with review retrieval. An exploratory data analysis of the reviews’ ratings and texts was conducted to find the first significant insights. The investigation found that although the overall review consensus was positive, it differed by country, while the reviews’ length was related to their ratings. A topic model was developed to find more information on what customers are talking about. The Model was able to find several different topics, including product-, supplement-, and servicespecific reviews. Lastly, a newly created key performance indicator about customers satisfaction uses the new insights about the ratings and the review topics, which a dashboard partially visualized through a dashboard.Pinheiro, Flávio Luís PortasRUNTueschen, Philipp2022-11-14T16:48:09Z2022-10-242022-10-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/145481TID:203097416enginfo: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:RCAAP2024-03-11T05:25:53Zoai:run.unl.pt:10362/145481Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:52:06.117078Repositório 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 |
Customer Review Analysis |
title |
Customer Review Analysis |
spellingShingle |
Customer Review Analysis Tueschen, Philipp BERTopic Sentence Embeddings Text Mining Topic Modeling Unsupervised Learning |
title_short |
Customer Review Analysis |
title_full |
Customer Review Analysis |
title_fullStr |
Customer Review Analysis |
title_full_unstemmed |
Customer Review Analysis |
title_sort |
Customer Review Analysis |
author |
Tueschen, Philipp |
author_facet |
Tueschen, Philipp |
author_role |
author |
dc.contributor.none.fl_str_mv |
Pinheiro, Flávio Luís Portas RUN |
dc.contributor.author.fl_str_mv |
Tueschen, Philipp |
dc.subject.por.fl_str_mv |
BERTopic Sentence Embeddings Text Mining Topic Modeling Unsupervised Learning |
topic |
BERTopic Sentence Embeddings Text Mining Topic Modeling Unsupervised Learning |
description |
Internship Report 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 |
2022-11-14T16:48:09Z 2022-10-24 2022-10-24T00:00:00Z |
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/10362/145481 TID:203097416 |
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http://hdl.handle.net/10362/145481 |
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TID:203097416 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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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) |
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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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