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

机译：Q型米：基于深度学习的情感分析的电信服务质量监测系统

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摘要

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.
机译：电信服务的质量监测系统与网络运营商相关，因为它可以有助于提高用户的体验质量（QoE）。在这种情况下，本文提出了一个质量监测系统，名为Q-Meter的，其主要目标是使用在线社交网络（OSNS）来改善关于电信服务的用户投诉检测。通过深入学习算法执行的情绪分析来检测投诉，提取用户的地理位置以评估信号强度。使用自由软件应用程序分析用户在OSN中发布申诉的区域，其使用由打开数据库提供的无线基站（RB）信息。实验结果表明，基于卷积神经网络（CNN）和双向长短期存储器（BLSTM） - Recurrent神经网络（RNN）的情感分析，具有软根标志（SRS）激活功能，提出了97的精度弱信号主题分类的％。此外，结果表明，78.3％的投诉总数与弱覆盖率有关，并证明了92％的这些地区认为，在考虑特定的细胞运营商的覆盖问题。此外，Q型计是低成本且易于集成到电流和下一代蜂窝网络中，并且它将用于感测和监控任务。

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