Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media
Archivos
Fecha
2021
Profesor Guía
Formato del documento
Articulo
ORCID Autor
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Título del volumen
Editor
Front Big Data
Ubicación
https://doi.org/10.3389/fdata.2021.640868
ISBN
ISSN
2624-909x
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Departamento o Escuela
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Recolector
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Nota general
Resumen
With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people's perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features.
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Palabras clave
5G, MACHINE LEARNING, MOBILE NETWORK QUALITY, OPINION, MINING, SENTIMENT ANALYSIS