HMRFLR: A Hybrid Model for Sentiment Analysis of Social Media Surveillance on Airlines

Social media dominates modern life. People publish their daily activities, self-indulgent sentiments, and real-life experiences on Twitter, Instagram, Facebook, YouTube, etc. Twitter dominates social media surveillance (64%). Twitter data is useful for user viewpoints and tweets. Like other companies, the airline industry seeks to stay current and survive in the present climate. Airlines employ widespread, time-consuming consumer feedback. Feelings must be analyzed to reduce issues. When flights resume after the epidemic, the airline industry is trying harder than ever to stay in contact with consumers. The airline industry's decade-long growth is impressive. Every day, millions of users share their airline experiences, from joyful consumers uploading photographs with clouds and employees and unhappy customers complaining about unsatisfactory services and issues like lost bags, delayed flights, changes in boarding schedules, IT system failures, etc. Real-time feedback helps consumers pick a flight and helps airline management and employees assess the situation and take prompt action to enhance services for passengers. 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.