Nowadays, the popular online review sites like Yelp have greatly affected the user purchase behaviors. Users either search for reviews to judge the quality of interested businesses or get recommendations of businesses they might like. Accordingly, this paper explores review rating predictions with two approaches. Binary sentiment analysis used vectorized review documents to predict general positive or negative attitudes in the text reviews. It gives higher prediction accuracy and adds on real-word interpretability of text reviews. A nearest neighbor collaborative filtering recommender predicts five-star ratings based on similar users and similar businesses. It predicts more comparable results to actual ratings with decent model accuracy and can make user-specific recommendations.