Consumers continuously review products and services on the internet. Others have frequently relied on those reviews in making purchasing decisions. Review texts are usually free-form and associated with a star rating on a 5-point scale. The majority of restaurants receive a 3.5 or 4 star rating on average, so a standalone star rating does not provide adequate information for readers to make a decision. Many researchers have approached the problem with sentiment analysis to classify a sentence or a text as expressing a positive or a negative review. Sentiment analysis, even at the fine-grained level, can only provide classification of positive and negative judgments on any particular aspect under consideration. The novel method proposed in this thesis provides insight into what aspects reviewers deem as relevant when assigning star rating to restaurants. This is accomplished by using an interpretable star rating classification method that predicts star rating based on aspect and polarity score from the review. The model first assigns a polarity score for each aspect in the review text, then predicts a star rating, and outputs a ranked list of aspect importance according to a widely used restaurant reviews dataset. The result from this thesis suggests that the classification model is able to output a reliable ranking from the review texts.