This paper investigates the role of emotion-driven predictive modeling in airline user reviews and its importance in the context of the highly competitive aviation industry. The study utilizes the NRCLex emotion analysis and assesses the efficacy of three Machine Learning (ML) models, namely Random Forest (RF), Decision Tree (DT), and Multilayer Perceptron (MLP), in predicting passenger sentiments. Our findings indicate that the MLP model outperforms the other models, achieving the highest accuracy and F1-score. This research focuses on understanding passenger sentiment in airline user reviews and its significance for service quality and customer satisfaction in the aviation industry. Using NRCLex and comparative ML analysis, our findings emphasize the MLP's potential for sentiment analysis, benefiting airlines and industry stakeholders by enhancing the passenger experience and customer loyalty while addressing predictive accuracy and model complexity.