Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/49880 |
Resumo: | A 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. |
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Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learningTelecommunication servicesOnline social networkSentiment analysisQuality-of-experience (QoE)SensingDeep learningServiços de telecomunicaçãoRede social on-lineAnálise de sentimentoQualidade da Experiência (QoE)Aprendizado profundoA 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.Multidisciplinary Digital Publishing Institute (MDPI)2022-05-06T20:08:37Z2022-05-06T20:08:37Z2021-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfVIEIRA, S. T. et al. Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning. Sensors, [S.I.], v. 21, n. 5, 2021. DOI: 10.3390/s21051880.http://repositorio.ufla.br/jspui/handle/1/49880Sensorsreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 InternationalAn error occurred getting the license - uri.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessVieira, Samuel TerraRosa, Renata LopesRodríguez, Demóstenes ZegarraArjona Ramírez, MiguelSaadi, MuhammadWuttisittikulkij, Lunchakorneng2023-05-03T13:19:24Zoai:localhost:1/49880Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T13:19:24Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning |
title |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning |
spellingShingle |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning Vieira, Samuel Terra Telecommunication services Online social network Sentiment analysis Quality-of-experience (QoE) Sensing Deep learning Serviços de telecomunicação Rede social on-line Análise de sentimento Qualidade da Experiência (QoE) Aprendizado profundo |
title_short |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning |
title_full |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning |
title_fullStr |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning |
title_full_unstemmed |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning |
title_sort |
Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning |
author |
Vieira, Samuel Terra |
author_facet |
Vieira, Samuel Terra Rosa, Renata Lopes Rodríguez, Demóstenes Zegarra Arjona Ramírez, Miguel Saadi, Muhammad Wuttisittikulkij, Lunchakorn |
author_role |
author |
author2 |
Rosa, Renata Lopes Rodríguez, Demóstenes Zegarra Arjona Ramírez, Miguel Saadi, Muhammad Wuttisittikulkij, Lunchakorn |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Vieira, Samuel Terra Rosa, Renata Lopes Rodríguez, Demóstenes Zegarra Arjona Ramírez, Miguel Saadi, Muhammad Wuttisittikulkij, Lunchakorn |
dc.subject.por.fl_str_mv |
Telecommunication services Online social network Sentiment analysis Quality-of-experience (QoE) Sensing Deep learning Serviços de telecomunicação Rede social on-line Análise de sentimento Qualidade da Experiência (QoE) Aprendizado profundo |
topic |
Telecommunication services Online social network Sentiment analysis Quality-of-experience (QoE) Sensing Deep learning Serviços de telecomunicação Rede social on-line Análise de sentimento Qualidade da Experiência (QoE) Aprendizado profundo |
description |
A 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. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-03 2022-05-06T20:08:37Z 2022-05-06T20:08:37Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
VIEIRA, S. T. et al. Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning. Sensors, [S.I.], v. 21, n. 5, 2021. DOI: 10.3390/s21051880. http://repositorio.ufla.br/jspui/handle/1/49880 |
identifier_str_mv |
VIEIRA, S. T. et al. Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning. Sensors, [S.I.], v. 21, n. 5, 2021. DOI: 10.3390/s21051880. |
url |
http://repositorio.ufla.br/jspui/handle/1/49880 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 4.0 International An error occurred getting the license - uri. http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 4.0 International An error occurred getting the license - uri. http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
dc.source.none.fl_str_mv |
Sensors reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835124328300544 |