Sentiment and Emotional Trajectory Analysis to Customer Feedback on Saudi Arabian Food Delivery Apps

Traditional sentiment analysis often falls short in capturing complex emotional elements influencing customer satisfaction. This study presents an emotion-aware feedback analysis system to convert customer reviews into actionable suggestions for restaurant owners and food delivery services.

Using a transformer-based deep learning model, the system classifies reviews at the sentence level. Batch-based inference and lightweight text preprocessing ensure computational efficiency. Detected emotional patterns are combined with domain-specific service rules to generate recommendations focusing on key areas like food quality and delivery performance.