This study aims to perform a systematic comparison of traditional machine learning and advanced methodologies for sentiment analysis of Twitter (X) data. The methodology used a structured workflow that encompassed data collection, pre-processing with NLTK, and the application of various classifiers, including Logistic Regression, RandomForest, GradientBoosting, SupportVectorMachine (SVM), and AdaBoost, on a dataset comprising 74,681 tweets. A notable finding was that the conventional SVM model gives the best result for all other models, achieving a maximum accuracy of 92. 31%. The study concludes that while advanced ensemble methods, such as Gradient Boosting, are robust, well-tuned conventional algorithms can attain state-of-the-art performance in classifying noisy social media text, underscoring their continued relevance for real-world applications, such as marketing and public opinion monitoring.