Q-Meter: quality monitoring system for telecommunication services based on sentiment analysis using deep learning

• Autor(a) principal: Vieira, Samuel Terra
• Data de Publicação: 2021
• Outros Autores: Rosa, Renata Lopes, Rodríguez, Demóstenes Zegarra, Arjona Ramírez, Miguel, Saadi, Muhammad, Wuttisittikulkij, Lunchakorn
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFLA
• Texto Completo: http://repositorio.ufla.br/jspui/handle/1/49880
• topic: Telecommunication services; Online social network; Sentiment analysis; Quality-of-experience (QoE); Sensing; Deep learning; Serviços de telecomunicação; Rede social on-line; Análise de sentimento; Qualidade da Experiência (QoE); Aprendizado profundo

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.