Over the last two decades, social media has become an integral part of people’s daily lives, serving as a crucial tool for gathering information, tracking trends, and exploring collective feelings. In this research paper, the power of Machine Learning (ML) models was harnessed to conduct an extensive analysis of sentiments expressed in Twitter data. By analyzing tweets, people can access a wealth of sentiment-related information, which in turn enhances their understanding of public opinions on a wide range of social media topics and issues. The main objective was to determine whether tweets convey negative, positive, or neutral emotions, a process commonly known as sentiment analysis. This analytical approach allows for the identification and categorization of emotions conveyed in text, providing valuable insights into sentiments. In this research paper, Sentiment Analysis is conducted using the MATLAB statistical and machine learning toolbox. The analysis focuses on calculating and visually illustrating both positive and negative sentiments, over time for various airlines. The study utilizes three distinct classifiers: Decision Tree, K-Nearest Neighbors (KNN), along with efficient Logistic Regression and efficient Linear Support Vector Machine (SVM) techniques. These models underwent training utilizing Twitter data pertaining to the aviation industry, and their performance was assessed through a 10-fold cross-validation process. The outcome revealed a maximum accuracy of 80.1%. This sophisticated sentiment analysis and loyalty projection framework transcends its initial aviation focus, providing businesses across various sectors with the capability to augment customer retention and cultivate brand loyalty.

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