  Yelp online reviews are invaluable source of information for users to choose
where to visit or what to eat among numerous available options. But due to
overwhelming number of reviews, it is almost impossible for users to go through
all reviews and find the information they are looking for. To provide a
business overview, one solution is to give the business a 1-5 star(s). This
rating can be subjective and biased toward users personality. In this paper, we
predict a business rating based on user-generated reviews texts alone. This not
only provides an overview of plentiful long review texts but also cancels out
subjectivity. Selecting the restaurant category from Yelp Dataset Challenge, we
use a combination of three feature generation methods as well as four machine
learning models to find the best prediction result. Our approach is to create
bag of words from the top frequent words in all raw text reviews, or top
frequent words/adjectives from results of Part-of-Speech analysis. Our results
show Root Mean Square Error (RMSE) of 0.6 for the combination of Linear
Regression with either of the top frequent words from raw data or top frequent
adjectives after Part-of-Speech (POS).
