Bacharelado em Sistemas de Informação (Sede)

URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/12

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APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso

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Autor: search.filters.author.Albuquerque, Júlia de MeloData inicial: 2020

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    Análise de sentimentos em reviews de jogos digitais da Plataforma Steam
    (2024-09-26) Albuquerque, Júlia de Melo; Albuquerque Júnior, Gabriel Alves de; http://lattes.cnpq.br/1399502815770584
    Sentiment analysis is an area that investigates the emotional expressions of human language, aiming to understand the underlying needs and opinions expressed in texts. Its complexity lies in the ability to discern not only the textual content but also the implicit emotional matrices. With technological advancements, the ease of publicly expressing opinions is disseminated through various means, with online gaming being a sector that attracts numerous player posts about various available titles. However, this diversity of audiences and topics makes it challenging to understand the expressed sentiment that pervades this universe. The aim of this study is to apply sentiment analysis techniques to digital game reviews, adopting an approach focused on supervised machine learning algorithms and pre-polarized libraries, in order to identify the best classification path capable of discerning the sentiments expressed by users in the reviews. This operation considers an approach with all opinions and another focused on each game’s specific genre. This analysis was conducted by exploring data from an online game distribution company (Steam), followed by data preparation due to the peculiarities present in the records. The results reveal that machine learning models outperform traditional approaches, such as using the VADER library, showing a higher precision by approximately 10% in captures. A difference of 20% more was observed in metrics such as recall and F1-score. This study represents an analytical contribution to the field of sentiment analysis, highlighting the model’s ability to deal with the complexity of human language.