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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning

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Terra Vieira, Samuel [1] ; Lopes Rosa, Renata [1] ; Zegarra Rodriguez, Demostenes [1] ; Arjona Ramirez, Miguel [2] ; Saadi, Muhammad [3] ; Wuttisittikulkij, Lunchakorn [4]
Total Authors: 6
[1] Univ Fed Lavras, Dept Comp Sci, BR-37202000 Lavras, MG - Brazil
[2] Univ Sao Paulo, Dept Elect Syst Engn, BR-05508010 Sao Paulo - Brazil
[3] Univ Cent Punjab, Dept Elect Engn, Lahore 54590 - Pakistan
[4] Chulalongkorn Univ, Dept Elect Engn, Wireless Commun Ecosyst Res Unit, Bangkok 10330 - Thailand
Total Affiliations: 4
Document type: Journal article
Source: SENSORS; v. 21, n. 5 MAR 2021.
Web of Science Citations: 0

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. (AU)

FAPESP's process: 18/26455-8 - Audio-Visual Speech Processing by Machine Learning
Grantee:Miguel Arjona Ramírez
Support type: Regular Research Grants