Twitter Analysis to Predict the Satisfaction of Saudi Telecommunication Companies’ Customers

The flexibility in mobile communications allows customers to quickly switch from one service provider to another, making customer churn one of the most critical challenges for the data and voice telecommunication service industry. In 2019, the percentage of post-paid telecommunication customers in Saudi Arabia decreased; this represents a great deal of customer dissatisfaction and subsequent corporate fiscal losses. Many studies correlate customer satisfaction with customer churn. The Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. Current methods of analysing churn rates are inadequate and faced some issues, particularly in the Saudi market. This research was conducted to realize the relationship between customer satisfaction and customer churn and how to use social media mining to measure customer satisfaction and predict customer churn. This research conducted a systematic review to address the churn prediction models problems and their relation to Arabic Sentiment Analysis. The findings show that the current churn models lack integrating structural data frameworks with real-time analytics to target customers in real-time. In addition, the findings show that the specific issues in the existing churn prediction models in Saudi Arabia relate to the Arabic language itself, its complexity, and lack of resources. As a result, I have constructed the first gold standard corpus of Saudi tweets related to telecom companies, comprising 20,000 manually annotated tweets. It has been generated as a dialect sentiment lexicon extracted from a larger Twitter dataset collected by me to capture text characteristics in social media. I developed a new ASA prediction model for telecommunication that fills the detected gaps in the ASA literature and fits the telecommunication field. The proposed model proved its effectiveness for Arabic sentiment analysis and churn prediction. This is the first work using Twitter mining to predict potential customer loss (churn) in Saudi telecom companies, which has not been attempted before. Different fields, such as education, have different features, making applying the proposed model is interesting because it based on text-mining

COVID-19’s Impact on the Telecommunications Companies

Now the world is witnessing most significant challenges due the Covid-19 crisis. Beyond health effects, it has social and economic effects. With the enormous amount of data available and the widespread use of social web globally, research can and should use it to provide solutions. Customer satisfaction is known to affect customer churn (customers leaving companies), which is a problem affecting many companies in competitive and volatile markets – like the current one. One easily available open source of customer opinions are tweets – more relevant now in the online world. Whilst Natural Language Processing (NLP) on tweets is not new, few studies target customer satisfaction, and NLP body of research on Arabic tweets is modest; we are not aware of any other study on this during a global pandemic. Our research thus aims to propose a new model based on Twitter mining to measure customer satisfaction during Covid-19, as well as compare customer satisfaction before and during the crisis. This is a use case for the largest Telecom companies in Saudi Arabia, and we involve the popular method of Sentiment Analysis (SA) for the task. We additionally propose a new Saudi lexicon and apply it to monitor real-time customer satisfaction on Twitter using three different transfer network models on Arabic sentiment analysis. Also, this research evaluates using these models on Arabic Sentiment Analysis as the first study comparing between three different transfer network models on Arabic text

Predicting STC Customers' Satisfaction Using Twitter

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 [1]. 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

Using Deep Learning Networks to Predict Telecom Company Customer Satisfaction Based on Arabic Tweets

Information systems are transforming businesses, which are using modern technologies towards new business models based on digital solutions, which ultimately lead to the design of novel socio-economic systems. Sentiment analysis is, in this context, a thriving research area. This paper is a case study of Saudi telecommunications (telecom) companies, using sentiment analysis for customer satisfaction based on a corpus of Arabic tweets. This paper compares, for the first time for Saudi social media in telecommunication, the most popular machine learning approach, support vector machine (SVM), with two deep learning approaches: long short-term memory (LSTM) and gated recurrent unit (GRU). This study used LSTM and GRU with two different implementations, adding attention mechanism and character encoding. The study concluded that the bidirectional-GRU with attention mechanism achieved a better performance in the telecommunication domain and allowed detection of customer satisfaction in the telecommunication domain with high accuracy

Ownership structure, risk taking and firm performance: a case for an integrative framework

Studies on ownership structure and firm performance are inexhaustible. This is due to the growing contradictory, inconclusive and inconsistent empirical findings which give rise to growing concerns and suggestions for the integration of latent variables to best explain the observed unclear relationship. This would mean adopting more of an integrated (moderation or mediation) rather than direct research framework in an attempt to explain the relationship between ownership structure and firm's performance better. This paper thus examined the ownership structure and firm performance with risk-taking behaviour as a moderator. As conceptual paper, a review of agency and stewardship theories as well as prospect theory along with each dimension of ownership structure is conducted. From the extensive review, it was found that risk taking is an integral part of organizational life. Consequently, an integrative framework that incorporates risk-taking as a moderator in the relationship between ownership structure and firm performance is proposed. This paper concluded that the application of this framework would offer better explanation of the relationship between ownership structure and firm performance taking into account the risk preferences of the enterprise

Dimension of ownership structure, risk-taking behaviour and performance of non-financial firms in gulf cooperation council countries

Empirical evidences on the influence of ownership structure on firm performance are not only inexhaustible, there are contradictions that give rise to growing concerns for further studies using an integrated framework that include latent variables to best explain the observed unclear relationship. This paper examined dimensions of ownership structure and firm performance with risk-taking behaviour or level as a moderating variable. Data were drawn from 280 listed non-financial firms in GCC over a ten-year (10) years (2008 – 2017) period, giving 2,520 observations. Ownership structure studied were government, managerial, family, foreign and concentrated ownership in relation to three performance measures namely priceearnings ratio (PERATIO), return on asset (ROA) and operating income (OPINC). Results reveal that government and foreign ownership structures have a significant positive effect on price-earnings ratio, and operating income and not ROA. Managerial ownership also has a significant positive effect on price-earnings ratio and operating income but a significant negative effect on ROA. Family ownership has only a significant positive effect on price-earnings ratio. Ownership concentration has a significant negative effect on price-earnings ratio, and operating income but no effect on ROA. Further, higher risk-taking in firms with government and concentrated ownership significantly improved price-earnings ratio and operating income. Managerial and family ownership improved only ROA and PER respectively, while foreign ownership led to reduction in PER and ROA. Finally, firms in manufacturing do not significantly improved, on average all performance measures except price-earnings ratio than non-manufacturing firms with three of the forms of ownership structure. The study concludes that ownership structure, on average leads to positive effect on performance of non-financial institutions in GCC. Also, risk-taking level, on average, moderates the relationship between ownership structure and performance of non-financial firms in GCC. This means that more risk taking leads to more returns for GCC firms. Nevertheless, manufacturing firms do not perform better except in price-earnings ratio than non-manufacturing firms in GCC region. Practically, drift toward government, foreign or managerial ownership structure could become an ideal movement as these forms of ownership structure contribute to improving performance measures. Firms with concentrated ownership, government, family and managerial ownership could take higher risk for higher returns. Therefore, management could embark on re-rationalizing and re-distributing ownership percentages among government, management or foreign ownership especially in non-manufacturing sector. This way, high market valuation (priceearnings ratio) and efficiency (operating income) could be achieved

AraCust: a Saudi Telecom Tweets corpus for sentiment analysis

Comparing Arabic to other languages, Arabic lacks large corpora for Natural Language Processing (Assiri, Emam & Al-Dossari, 2018; Gamal et al., 2019). A number of scholars depended on translation from one language to another to construct their corpus (Rushdi-Saleh et al., 2011). This paper presents how we have constructed, cleaned, pre-processed, and annotated our 20,0000 Gold Standard Corpus (GSC) AraCust, the first Telecom GSC for Arabic Sentiment Analysis (ASA) for Dialectal Arabic (DA). AraCust contains Saudi dialect tweets, processed from a self-collected Arabic tweets dataset and has been annotated for sentiment analysis, i.e.,manually labelled (k=0.60). In addition, we have illustrated AraCust's power, by performing an exploratory data analysis, to analyse the features that were sourced from the nature of our corpus, to assist with choosing the right ASA methods for it. To evaluate our Golden Standard corpus AraCust, we have first applied a simple experiment, using a supervised classifier, to offer benchmark outcomes for forthcoming works. In addition, we have applied the same supervised classifier on a publicly available Arabic dataset created from Twitter, ASTD (Nabil, Aly & Atiya, 2015). The result shows that our dataset AraCust outperforms the ASTD result with 91% accuracy and 89% F1avg score. The AraCust corpus will be released, together with code useful for its exploration, via GitHub as a part of this submission

An Empirical Study on Customer Churn Behaviours Prediction Using Arabic Twitter Mining Approach

With the rising growth of the telecommunication industry, the customer churn problem has grown in significance as well. One of the most critical challenges in the data and voice telecommunication service industry is retaining customers, thus reducing customer churn by increasing customer satisfaction. Telecom companies have depended on historical customer data to measure customer churn. However, historical data does not reveal current customer satisfaction or future likeliness to switch between telecom companies. The related research reveals that many studies have focused on developing churner prediction models based on historical data. These models face delay issues and lack timelines for targeting customers in real-time. In addition, these models lack the ability to tap into Arabic language social media for real-time analysis. As a result, the design of a customer churn model based on real-time analytics is needed. Therefore, this study offers a new approach to using social media mining to predict customer churn in the telecommunication field. This represents the first work using Arabic Twitter mining to predict churn in Saudi Telecom companies. The newly proposed method proved its efficiency based on various standard metrics and based on a comparison with the ground-truth actual outcomes provided by a telecom company

Leveraging Software-Defined Networking for a QoS-Aware Mobility Architecture for Named Data Networking

The internet’s future architecture, known as Named Data Networking (NDN), is a creative way to offer content-based services. NDN is more appropriate for content distribution because of its special characteristics, such as naming conventions for packets and methods for in-network caching. Mobility is one of the main study areas for this innovative internet architecture. The software-defined networking (SDN) method, which is employed to provide mobility management in NDN, is one of the feasible strategies. Decoupling the network control plane from the data plane creates an improved programmable platform and makes it possible for outside applications to specify how a network behaves. The SDN is a straightforward and scalable network due to its key characteristics, including programmability, flexibility, and decentralized control. To address the problem of consumer mobility, we proposed an efficient SDPCACM (software-defined proactive caching architecture for consumer mobility) in NDN that extends the SDN model to allow mobility control for the NDN architecture (NDNA), through which the MC (mobile consumer) receives the data proactively after handover while the MC is moving. When an MC is watching a real-time video in a state of mobility and changing their position from one attachment point to another, the controllers in the SDN preserve the network layout and topology as well as link metrics to transfer updated routes with the occurrence of the handoff or handover scenario, and through the proactive caching mechanism, the previous access router proactively sends the desired packets to the new connected routers. Furthermore, the intra-domain and inter-domain handover processing situations in the SDPCACM for NDNA are described here in detail. Moreover, we conduct a simulation of the proposed SDPCACM for NDN that offers an illustrative methodology and parameter configuration for virtual machines (VMs), OpenFlow switches, and an ODL controller. The simulation result demonstrates that the proposed scheme has significant improvements in terms of CPU usage, reduced delay time, jitter, throughput, and packet loss ratio

Blockchain-Assisted Secure Smart Home Network Using Gradient-Based Optimizer With Hybrid Deep Learning Model

The Internet of Things (IoT) refers to a technology enabler to enhance the urban physical architecture and render public services. But, public access to accumulated heterogeneous IoT urban information is prone to hackers attacking connected devices to the internet intellectual property as well. IoT security serves a dynamic part in the smart city. Some IoT devices are connected in smart homes, and these connections were centred on gateways. In smart homes, the gateways gain a lot of significance; but their centralized structure causes many security vulnerabilities like availability, integrity, and certification. Unified “cloud-like” computing networks and Blockchain (BC) type systems should be used to sort out these problems. Therefore, this article develops a Blockchain-Assisted Secure Smart Home Network using Gradient Based Optimizer with Hybrid Deep Learning (BSSHN-GBOHDL) model. The presented BSSHN-GBOHDL technique employs BC technology to improve the confidentiality of the data in the smart home environment. In addition, the BSSHN-GBOHDL technique identifies malicious activities in the smart home environment via three sub-processes namely data preprocessing, hybrid deep learning (HDL)-based malicious activity classification, and GBO-based hyperparameter tuning. The GBO algorithm assists in the proficient hyperparameter selection of the HDL model, which aids in accomplishing increased detection efficiency. The experimental validation of the BSSHN-GBOHDL approach is tested on a benchmark NSL-KDD dataset with 65495 normal and 60743 attack samples. The results highlight the betterment of the BSSHN-GBOHDL approach over other recent methods with maximum accuracy of 98.29%