Publicação
Online Reviews Analysis with Large Language Models
| Resumo: | With 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. |
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| Autores principais: | Ferreira, Henrique Marques |
| Assunto: | 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 |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | With 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. |
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