Customer Review Analysis

Detalhes bibliográficos
Autor(a) principal: Tueschen, Philipp
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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spelling 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
url http://hdl.handle.net/10362/145481
identifier_str_mv TID:203097416
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
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