Sentiment analysis involves analyzing text to determine whether the sentiment expressed is positive, negative, or neutral. In the context of online reviews, such as those on Yelp, sentiment analysis helps businesses assess customer satisfaction and identify areas for improvement. Given the large volume of user-generated content, restaurants often struggle to extract actionable insights from feedback, making sentiment analysis an efficient tool for categorizing reviews and highlighting customer concerns. This study focuses on sentiment analysis of Yelp reviews. The main research question is: How can Natural Language Processing (NLP) combined with statistical machine learning methods be applied to classify sentiment in Yelp reviews and provide actionable insights for improving customer satisfaction, service quality, and business performance? The study used 21,000 Yelp reviews, utilizing NLP approaches - tokenization, stop-word removal, and vectorization. Comparative classification predictive modeling and analysis were done across traditional machine learning (Logistic Regression, Support Vector Machine (SVM), Naïve Bayes, Random Forest), deep learning methods (CNN, LSTM, BiLSTM, GRU, RNN), and an advanced transformer-based (RoBERTa) model. Results showed that RoBERTa outperformed the other candidate methods. These findings highlight the potential of advanced NLP techniques to offer businesses practical ways to address customer complaints, enhance service quality, and drive overall business performance.