| Full text | |
| Author(s): |
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
|
| Affiliation: | [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 |
| Abstract | |
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 Opportunities: | Regular Research Grants |