Sentiment Vectorization and Predictive Modeling of Aviation Feedback using Advanced NLP Architectures

The increasing ubiquity of social media sites has made customer opinions a cornerstone of business intelligence. This paper presents an end-to-end sentiment analysis system for Twitter, examining consumer opinions for U.S. carriers. Using cutting-edge natural language processing techniques and machine learning algorithms, the system suggested in this paper analyzes and categorizes tweets based on their sentiment orientation—supportive, neutral, or critical. The system also discovers the underlying reasons for dissatisfaction, e.g., delays, complaints regarding baggage, and poor customer service, thus providing unambiguous insights into consumer complaints. The analysis pipeline comprises data preprocessing, TF-IDF-based feature engineering, and Random Forest classifier-based model construction with an accuracy rate of 79%. For interpretability, word clouds, bar graphs, and confusion matrices provide a clear indication of sentiment directions and drivers. The extensible and modular structure of the system makes it plausible for deployment across industries for reputation monitoring, competitor monitoring, and customer experience optimization. This paper showcases the potency of AI-driven sentiment analysis as a business disruption tool, enabling proactive decision-making and improved customer satisfaction.