Simplicity of Positive Reviews and Diversity of Negative Reviews in Hotel Reputation

User's review on products and services is valuable information for both users and providers. The present paper conducted a polarity estimation of 73,589 reviews on hotels in Europe. Users rated one to five points for seven aspects (Value, Rooms, Location, Cleanliness, Check-in, Service, Business, Overall). In this paper, we predicted the polarity (positive/negative) of each aspect by using a machine learning method, SVM (Support Vector Machine), and feature selection, with more than 4 points being positive and less than 3 being negative. As a result, positive reviews with respect to six aspects, other than Business, were able to achieve 74
^%
prediction performance (F-measure) with only 20 feature words. On the other hand, for negative reviews, optimal prediction performance could not be obtained unless almost all words were used, and on average F-measure was only 27%. The results indicate that positive reviews are simple, meanwhile negative reviews are diverse and hard to predict mechanically.