A Comprehensive Study of Deep Learning and Traditional Machine Learning Models for Twitter Sentiment Analysis

This study presents a comprehensive evaluation of eight different algorithms for sentiment analysis on Twitter data, focusing on the airline industry domain. Using the Twitter US Airline Sentiment dataset, we implemented and compared traditional machine learning methods, lexicon-based approaches and state-of-the-art deep learning models. Our experimental results demonstrate that BERT achieves the highest accuracy of 77.1%, followed by SVM at 74.2% and LSTM at 71.4%. The study provides insights into the strengths and limitations of different approaches for social media sentiment classification and offers practical guidance for selecting appropriate algorithms based on computational requirements and accuracy needs. The research contributes to the growing body of literature on social media analytics and provides actionable insights for practitioners implementing sentiment analysis systems in real-world applications.