{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T17:07:31Z","timestamp":1787072851399,"version":"3.56.0"},"reference-count":96,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A quality monitoring system for telecommunication services is relevant for network operators because it can help to improve users\u2019 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s21051880","type":"journal-article","created":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T12:12:18Z","timestamp":1615205538000},"page":"1880","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8734-6578","authenticated-orcid":false,"given":"Samuel","family":"Terra Vieira","sequence":"first","affiliation":[{"name":"Department of Computer Science, Federal University of Lavras, Minas Gerais 37202-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7595-7187","authenticated-orcid":false,"given":"Renata","family":"Lopes Rosa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Lavras, Minas Gerais 37202-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5401-7551","authenticated-orcid":false,"given":"Dem\u00f3stenes","family":"Zegarra Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Federal University of Lavras, Minas Gerais 37202-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7107-0888","authenticated-orcid":false,"given":"Miguel","family":"Arjona Ram\u00edrez","sequence":"additional","affiliation":[{"name":"Department of Electronic Systems Engineering, University of S\u00e3o Paulo, S\u00e3o Paulo 05508-010, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7901-7435","authenticated-orcid":false,"given":"Muhammad","family":"Saadi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Central Punjab, Lahore 54590, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3033-3020","authenticated-orcid":false,"given":"Lunchakorn","family":"Wuttisittikulkij","sequence":"additional","affiliation":[{"name":"Wireless Communication Ecosystem Research Unit, Department of Electrical Engineering, Chulalongkorn University, Bangkok 10330, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9109","DOI":"10.1109\/ACCESS.2017.2706084","article-title":"A Dataset for Psychological Human Needs Detection From Social Networks","volume":"5","author":"Alharthi","year":"2017","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rosa, R., Rodriguez, D.Z., and Bressan, G. 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