Social Media is a major part of human life in the current era. People posts their regular activities, self-indulgent feelings, and real-life experiences on various platforms such as Twitter, Instagram, Facebook, YouTube etc. For social media surveillance, Twitter is considered to be the most widely used platform (about 64%). Twitter data is a valuable method to gather tweets and analyses of user perspectives. Along with the other industries the airline industry also wants to be up to date and keep its sectors alive having the current scenario. Airlines use traditional types of customer feedback that are very common and require a great deal of time. To minimize the problems, the analysis of feelings is considered to be a mandatory approach. After the pandemic when traveling is again resuming and flights are finally taking off, the airline industry is also giving its best to keep in touch with their customers more than ever. The dynamic evolution of the airline industry is commendable over the last decade. Millions of people share their experiences related to different airline companies every day where happy customers are posting their pictures with clouds and staff, some angry customers are complaining about bad services and difficulties they faced like missing baggage, delayed flights, changes in boarding schedules, an IT system failure, etc. This kind of real-time feedback not only helps the passengers to decide which flight they have to choose but also helps the management team and staff of airlines to analyze the situation and take immediate action regarding it to improve their services for passengers' better experience. In the research paper, a hybrid model composed of Machine Learning algorithms including the classifiers of Random Forest and Logistic Regression named as HMRFLR is proposed to analyze the tweets of Airlines in the US for categorization of the posts according to positivity, neutrality, and negativity of the posts. For revealing the current level of customer satisfaction towards the airlines, sentiment analysis is undertaken. This hybrid model achieves a better accuracy score of 88.16%, however, the individual accuracy score of Logistic Regression is 79.1% and Random Forest is 76.87% respectively.