In this study, we created a machine learning database that contains several thousands of samples in three categories, by collecting the review quotes and the corresponding scores from Metacritic. As a second step, we extracted the words used in these quotes from reviews of music albums, movies and TV shows. Then, according to a few statistics, we selected a small set of words that could carry the most amount of score information. This selection procedure is confirmed by the lists of positive and negative words we generated. These lists are very meaningful for a human examiner, and shows a clear relation between the words and the corresponding mean scores. In the last step, we applied linear regression and SVM regression for machine learning. The experiments show that we can achieve an
