  In current times, the importance of online hotel review sites has become more
and more apparent. Users of these sites reference of reviews strongly
influences their purchase behavior and as such, reviews are important to
companies and researchers alike. The majority of review sites offer both text
reviews and numerical hotel ratings, and both information sources are widely
used by researchers as a representation of a customer's sentiment and opinion.
However, an opinion is a difficult concept to measure, and as such, depending
on the relation these two sources have, it would be apparent whether or not it
is safe to consider them equally in research. In this study we utilize an
entropy-based Support Vector Machine to classify positive and negative
sentiments in hotel reviews from the site Ctrip, then calculating the ratio of
positive and negative sentiment in each review and examine their correlation
with said review's rating score using Spearman and Kendall Correlation
coefficients and Maximal Information Coefficient (MIC).
