Sentiment analysis of social media discourse on public perception of online courier services in Saudi Arabia using machine learning - Mohamed Shenify (International Journal of Data and Network Science)
The Kingdom of Saudi Arabia has witnessed a significant surge in online shopping in recent years, fueled by factors like growing internet penetration, smartphone adoption, and government initiatives supporting e-commerce growth. This rise in online activity has led to a corresponding increase in the utilization of online courier services, playing a crucial role in ensuring timely and efficient delivery of goods In this context, understanding public perception of online courier services becomes crucial for businesses to improve their offerings, address customer concerns, and maintain a competitive edge. Social media platforms have emerged as a valuable source of customer feedback and user-generated content, offering insights into customer experiences and opinions. This paper presents a sentiment analysis on online couriers in Saudi Arabia using natural language processing techniques combined with Decision Tree and Support Vector Machine (SVM) classifiers of machine learning. A dataset on customers’ sentiments was created by a crawling process from X social media. Both classifiers perform well, with Decision Tree classifier performs slightly better on accuracy, i.e. 95.01% compared to 93.60% of the Support Vector Machine. Other metrics support the robustness of the classification.