Evaluating User Satisfaction Using Deep-Learning-Based Sentiment Analysis for Social Media Data in Saudi Arabia's Telecommunication Sector

Social media has become common as a means to convey opinions and express the extent of satisfaction and dissatisfaction with a service or product. In the Kingdom of Saudi Arabia specifically, most social media users share positive and negative opinions about a service or product, especially regarding communication services, which is one of the most important services for citizens who use it to communicate with the world. This research aimed to analyse and measure user satisfaction with the services provided by the Saudi Telecom Company (STC), Mobily, and Zain. This type of sentiment analysis is an important measure and is used to make important business decisions to succeed in increasing customer loyalty and satisfaction. In this study, the authors developed advanced methods based on deep learning (DL) to analyse and reveal the percentage of customer satisfaction using the publicly available dataset AraCust. Several DL models have been utilised in this study, including long short-term memory (LSTM), gated recurrent unit (GRU), and BiLSTM, on the AraCust dataset. The LSTM model achieved the highest performance in text classification, demonstrating a 98.04% training accuracy and a 97.03% test score. The study addressed the biggest challenge that telecommunications companies face: that the company's services influence customers' decisions due to their dissatisfaction with the provided services.