The rapid rise of mobile technology paired with the steady growth of the internet, has led to a massive increase in the amount of user generated content, such as online consumer reviews, accessible through the browser. As the volume of user-generated content continues to rise, it becomes increasingly important to develop sophisticated methods for performing sentiment analysis on the texts collected from users, especially those that have been generated in relation to restaurants and similar types of service establishments. In this paper, we will present a new approach to sentiment analysis which incorporates Latent Dirichlet Allocation topic models, Term Frequency- Inverse Document Frequency vector representations and XGBoost Classifiers into a unified framework. Unlike conventional implementations, this study integrates probabilistic topic distributions from LDA with multi-level n-gram TF-IDF features and evaluates their combined impact using XGBoost for enhanced classification performance. Using three distinct n-gram levels (unigrams, bigrams, and trigrams), we will evaluate various aspects of text-based data including common linguistic patterns and sentiment trends. Higher-order n-grams were included to capture contextual dependencies beyond single-word features. Overall, our results demonstrate that the performance of our proposed framework is superior to traditional corpus-based models on multiple evaluation metrics, including: classification accuracy 96.07%, classification sensitivity 95.43%, classification specificity 97.12% and F1-Score 96.16%.