An Improved sentiment classification for objective word

Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and na�¯ve bays.