The advancement of social media has generated extensive user content, making sentiment analysis crucial for organizations to understand public opinion and make decisions. This work uses advanced machine learning techniques to analyze sentiment and views from social media effectively. We Employed the Sentiment Analysis: Emotion in text tweets dataset from Kaggle, which involved several preprocessing stages to ensure clean and structured data for analysis. We also used two critical feature extraction techniques, TF-IDF and word embeddings. We take advantage of four crucial machine learning models, including Random Forest (RF), Logistic Regression (LR), XGBoost, and Support Vector Machine (SVM), each optimized to capture sentiment analysis. Among these models, LR performed remarkably well, achieving an impressive accuracy of 95.61%. This study enhances sentiment analysis methodologies and highlights the importance of integrating opinion-mining techniques within Management Information Systems (MIS), allowing organizations to make timely, data-driven decisions.