Bangladeshi Airlines Customer Review Analysis Using ML Boosting and Hybrid Techniques

Abstract

Airlines must understand customer feelings and preferences to stay competitive and successful in today's dynamic and fiercely competitive airline market. Customer reviews provide extensive information about passengers' experiences, attitudes, and expectations. Analysing these evaluations using modern machine learning (ML) techniques allows airlines to extract actionable insight from massive amounts of unstructured data to make informed decisions and better serve customers. This study analyses Bangladeshi airline customer reviews using boosting and voting ML. This study uses 3000 reviews from official airline websites like Novo Air, Bangladesh Biman, and US Bangla and social media traveller groups to understand passenger opinions of Bangladeshi airlines' services. This research uses advanced ML algorithms like CatBoosting, AdaBoosting, XGBoosting, and Gradient Boosting and ensemble techniques like Voting RM, SVM, and Naive Bayes to find hidden patterns, trends, and insights in customer feedback. highest accuracy 87% achieved by SVM and random-forest voting techniques.