The use of online consumer reviews (OCRs) websites like Yelp or TripAdvisor has increasingly gained popularity over the years. However, many products or services now have a large number of online reviews, which makes it difficult for consumers to decide which reviews to pay attention to, or for businesses to identify which critical areas to improve on. The use of text mining and sentiment mining techniques is deemed important to automatically process the content of online reviews and help improve review usefulness. Applying a four-phase research model, our study demonstrated data extraction and cleaning, topic modeling using Latent Dirichlet allocation (LDA) to extract five topics (Price, Time, Food, Service, and Location), sentiment analysis using Python TextBlob to aggregate consumer sentiment per topic from a Yelp restaurant reviews dataset, and model performance evaluation. We proposed that the design of recommender systems for OCRs or business decision-making systems can be faster, simpler, and more useful by integrating automatic topics extraction and sentiment analysis.