Featured based sentiment classification for hotel reviews using NLP and Bayesian classification

The internet revolution has brought about a new way of expressing an individual´s opinion. It has become a medium through which people openly express their views on various subjects. These opinions contain useful information which can be utilized in many sectors which require constant customer feedback. Analysis of the opinion and it´s classification into different sentiment classes is gradually emerging as a key factor in decision making. There has been extensive research on automatic text analysis for sentiments such as sentiment classifiers, affect analysis, automatic survey analysis, opinion extraction, or recommender systems. These methods typically try to extract the overall sentiment revealed in a sentence or document, either positive or negative, or somewhere in between. However, a drawback of these methods is that the information can be degraded, especially in texts where a loss of information can also occur. The proposed method attempts to overcome the problem of the loss of text information by using well trained training sets. Also, recommendation of a product or request for a product as per the user´s requirements have achieved with the proposed method.