Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorVieira, Samuel Terra-
Autor(es): dc.creatorRosa, Renata Lopes-
Autor(es): dc.creatorRodríguez, Demóstenes Zegarra-
Autor(es): dc.creatorArjona Ramírez, Miguel-
Autor(es): dc.creatorSaadi, Muhammad-
Autor(es): dc.creatorWuttisittikulkij, Lunchakorn-
Data de aceite: dc.date.accessioned2026-02-09T11:53:10Z-
Data de disponibilização: dc.date.available2026-02-09T11:53:10Z-
Data de envio: dc.date.issued2022-05-06-
Data de envio: dc.date.issued2022-05-06-
Data de envio: dc.date.issued2021-03-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/49880-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1149613-
Descrição: dc.descriptionA quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users’ quality-of-experience (QoE). In this context, this article proposes a quality monitoring system, named Q-Meter, whose main objective is to improve subscriber complaint detection about telecommunication services using online-social-networks (OSNs). The complaint is detected by sentiment analysis performed by a deep learning algorithm, and the subscriber’s geographical location is extracted to evaluate the signal strength. The regions in which users posted a complaint in OSN are analyzed using a freeware application, which uses the radio base station (RBS) information provided by an open database. Experimental results demonstrated that sentiment analysis based on a convolutional neural network (CNN) and a bidirectional long short-term memory (BLSTM)-recurrent neural network (RNN) with the soft-root-sign (SRS) activation function presented a precision of 97% for weak signal topic classification. Additionally, the results showed that 78.3% of the total number of complaints are related to weak coverage, and 92% of these regions were proved that have coverage problems considering a specific cellular operator. Moreover, a Q-Meter is low cost and easy to integrate into current and next-generation cellular networks, and it will be useful in sensing and monitoring tasks.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)-
Direitos: dc.rightsAttribution 4.0 International-
Direitos: dc.rightsAttribution 4.0 International-
Direitos: dc.rightsacesso aberto-
Direitos: dc.rightsAn error occurred getting the license - uri.-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
Direitos: dc.rightsAn error occurred getting the license - uri.-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
???dc.source???: dc.sourceSensors-
Palavras-chave: dc.subjectTelecommunication services-
Palavras-chave: dc.subjectOnline social network-
Palavras-chave: dc.subjectSentiment analysis-
Palavras-chave: dc.subjectQuality-of-experience (QoE)-
Palavras-chave: dc.subjectSensing-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectServiços de telecomunicação-
Palavras-chave: dc.subjectRede social on-line-
Palavras-chave: dc.subjectAnálise de sentimento-
Palavras-chave: dc.subjectQualidade da Experiência (QoE)-
Palavras-chave: dc.subjectAprendizado profundo-
Título: dc.titleQ-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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