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Q-meter: Quality Monitoring System For Telecommunication Services Based On Sentiment Analysis Using Deep Learning.

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Q-meter: Quality Monitoring System For Telecommunication Services Based On Sentiment Analysis Using Deep Learning.

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

Samuel Terra Vieira¹, Renata Lopes Rosa¹, Demóstenes Zegarra Rodríguez¹

¹Department of Computer Science, Federal University of Lavras, Minas Gerais 37202-000, Brazil.

Sensors (Basel, Switzerland)

|April 3, 2021

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces Q-Meter, a system using deep learning on social media to detect telecommunication complaints. It accurately identifies weak signal issues, improving user quality-of-experience (QoE) and network monitoring.

Keywords:

QoE deep learning online social network sensing sentiment analysis telecommunication services

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Area of Science:

Telecommunication Network Monitoring
Quality of Experience (QoE) Analysis
Social Media Data Analytics

Background:

Network operators need effective Quality of Experience (QoE) monitoring systems.
Subscriber complaints are a key indicator of service quality issues.
Online Social Networks (OSNs) offer a rich source of user feedback.

Purpose of the Study:

To propose Q-Meter, a novel system for enhanced subscriber complaint detection in telecommunication services.
To leverage Online Social Networks (OSNs) and deep learning for real-time quality monitoring.
To identify and analyze geographical areas with poor signal strength and coverage.

Main Methods:

Utilizing deep learning, specifically Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BLSTM)-Recurrent Neural Network (RNN) with soft-root-sign (SRS) activation, for sentiment analysis of OSN posts.

Extracting subscriber geographical locations from OSN data.

Analyzing complaint regions using a freeware application with Radio Base Station (RBS) information from open databases.

Main Results:

Achieved 97% precision in classifying weak signal topics using the developed deep learning model.
Determined that 78.3% of complaints are related to weak coverage.
Validated coverage problems in 92% of identified complaint regions for a specific cellular operator.

Conclusions:

Q-Meter offers a cost-effective and easily integrable solution for telecommunication quality monitoring.
The system effectively detects subscriber complaints related to signal strength and coverage issues using OSN data.
Q-Meter enhances network monitoring capabilities for current and future cellular networks, improving user QoE.