  User-generated texts such as reviews and social media are valuable sources of
information. Online reviews are important assets for users to buy a product,
see a movie, or make a decision. Therefore, rating of a review is one of the
reliable factors for all users to read and trust the reviews. This paper
analyzes the texts of the reviews to evaluate and predict the ratings.
Moreover, we study the effect of lexical features generated from text as well
as sentimental words on the accuracy of rating prediction. Our analysis show
that words with high information gain score are more efficient compared to
words with high TF-IDF value. In addition, we explore the best number of
features for predicting the ratings of the reviews.
