Over the past twenty years, the competitive airline industry has expanded at an exponential rate. As more people travel through various airlines, they encounter more amenities and issues, and based on those encounters, they offer their personal reviews. This study significantly advances the area of sentiment analysis in the airline industry by giving businesses the means of extracting insightful information from customer reviews and use that information to inform data-driven service improvement choices. The review of airline programs effectiveness heavily relies on feedback from consumers. An extensively gathered dataset of 67,993 reviews from the Google Play Store and the App Store based on the ten most well-known airlines, was divided into three categories: “Positive,” “Negative,” and “Neutral”. Word embedding technique was integrated with deep learning models, such as CNN, LSTM, and BiLSTM to improve sentiment analysis accuracy. Both the LSTM and BiLSTM models achieved remarkable accuracy rates at 90% and 91% respectively, which is intriguing. However, BiLSTM was found to outperform other deep learning models regarding precision at 92%, recall at 91%, and F1-score at 91% among other outcomes.