Online Reviews Analysis with Large Language Models

Bibliographic Details
Main Author: Ferreira, Henrique Marques
Publication Date: 2024
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/175580
Summary: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
id RCAP_831d8aa15a3b8e90810f80442dbbd4a6
oai_identifier_str oai:run.unl.pt:10362/175580
network_acronym_str RCAP
network_name_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository_id_str https://opendoar.ac.uk/repository/7160
spelling Online Reviews Analysis with Large Language ModelsLarge Language Models (LLMs)Sentiment AnalysisText miningCustomer feedbackOnline Reviews AnalysisSDG 4 - Quality educationSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for MarketingWith the rapid evolution of digital technologies, online reviews have become a pivotal source of customer feedback, creating a need for more efficient tools to rapidly analyze customer opinions. This need motivates the exploration of advanced Artificial Intelligence (AI) techniques to enhance customer feedback analysis. This study investigates the efficacy of utilizing Large Language Models (LLMs), for the comprehensive analysis of online reviews. It focuses on evaluating customer feedback through reviews collected from TripAdvisor for hotels in the Algarve region of Portugal. Using the Azure OpenAI API to implement the GPT-3.5-Turbo-16K model for Text Mining tasks such as sentiment analysis and topic modeling. The primary goal of the study is to demonstrate how LLMs can accurately classify sentiments, identify recurring themes, and extract actionable insights from large datasets of unstructured text. The findings highlight LLMs’ ability to provide detailed real-time insights, easily correctly classifying most of the reviews with Positive classifications and highlighting the most frequent pros and cons mentioned, such as “friendly and helpful staff” and “issues with bar service”, providing detailed suggestions to address negative classifications. The research addresses the limitations of LLMs, such as potential biases and the challenge of maintaining accuracy across different languages and cultural contexts. These findings demonstrate the ability to automatically analyze and extract insights from online reviews, enabling more informed strategic decisions, refined marketing strategies, improved product offerings, and enhanced customer satisfaction.António, Nuno Miguel da ConceiçãoRUNFerreira, Henrique Marques2024-10-262026-10-26T00:00:00Z2024-10-26T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/175580TID:203778502enginfo:eu-repo/semantics/embargoedAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-01-13T01:43:46Zoai:run.unl.pt:10362/175580Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T19:15:55.258987Repositórios 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 Online Reviews Analysis with Large Language Models
title Online Reviews Analysis with Large Language Models
spellingShingle Online Reviews Analysis with Large Language Models
Ferreira, Henrique Marques
Large Language Models (LLMs)
Sentiment Analysis
Text mining
Customer feedback
Online Reviews Analysis
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Online Reviews Analysis with Large Language Models
title_full Online Reviews Analysis with Large Language Models
title_fullStr Online Reviews Analysis with Large Language Models
title_full_unstemmed Online Reviews Analysis with Large Language Models
title_sort Online Reviews Analysis with Large Language Models
author Ferreira, Henrique Marques
author_facet Ferreira, Henrique Marques
author_role author
dc.contributor.none.fl_str_mv António, Nuno Miguel da Conceição
RUN
dc.contributor.author.fl_str_mv Ferreira, Henrique Marques
dc.subject.por.fl_str_mv Large Language Models (LLMs)
Sentiment Analysis
Text mining
Customer feedback
Online Reviews Analysis
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Large Language Models (LLMs)
Sentiment Analysis
Text mining
Customer feedback
Online Reviews Analysis
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
publishDate 2024
dc.date.none.fl_str_mv 2024-10-26
2024-10-26T00:00:00Z
2026-10-26T00:00:00Z
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/10362/175580
TID:203778502
url http://hdl.handle.net/10362/175580
identifier_str_mv TID:203778502
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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