Understanding sentiment in movie reviews provides valuable insights for both movie producers and viewers. This study analyzes sentiment in movie reviews with a focus on age and gender demographics. Using Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes (NB) classifiers, we explored sentiment differences across six age groups: young adults, early adults, middle adults, late adults, senior adults, and the elderly. The movie reviews sourced from a hotel review dataset were cleaned, preprocessed, and labeled accordingly. We trained models with and without demographic features and applied hyperparameter tuning to optimize accuracy, precision, and recall. Our findings indicate that age and gender significantly influence sentiment expression, with implications for personalized movie recommendations.