Almuqren, Latifah and Cristea, Alexandra I. (2022) 'Predicting STC Customers' Satisfaction Using Twitter.', IEEE Transactions on Computational Social Systems .
The telecom field has changed accordingly with the emergence of new technologies. This is the case with the telecom market in Saudi Arabia, which expanded in 2003 by attracting new investors. As a result, the Saudi telecom market became a viable market . The prevalence of mobile voice service among the population in Saudi Arabia for that, this research aims at mining Arabic tweets to measure customer satisfaction toward Telecom company in Saudi Arabia. This research is a use case for the Saudi Telecom Company (STC) in Saudi Arabia. The contribution of this study will be capitalized as recommendations to the company, based on monitoring in real-time their customers' satisfaction on Twitter and from questionnaire analysis. It is the first work to evaluate customers' satisfaction with telecommunications (telecom) company in Saudi Arabia by using both social media mining and a quantitative method. It has been built by a corpus of Arabic tweets, using a Python script searching for real-time tweets that mention Telecom company using the hashtags to monitor the latest sentiments of Telecom customers continuously. The subset is 20,000 tweets that are randomly selected from the dataset, for training the machine- classifier. In addition, we have done the experimented using deep learning network. The results show that the satisfaction for each service ranges between 31.50% and 49.25%. One of the proposed recommendations is using 5G to solve the ``internet speed'' problem, which showed the lowest customer satisfaction, with 31.50%.This article's main contributions are defining the traceable measurable criteria for customer satisfaction with telecom companies in Saudi Arabia and providing telecom companies' recommendations based on monitoring real-time customers' satisfaction through Twitter.
|Full text:||(AM) Accepted Manuscript|
Download PDF (412Kb)
|Publisher Web site:||https://doi.org/10.1109/TCSS.2021.3135719|
|Publisher statement:||© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||No date available|
|Date deposited:||05 December 2022|
|Date of first online publication:||10 January 2022|
|Date first made open access:||05 December 2022|
Save or Share this output
|Look up in GoogleScholar|