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Visual and Auditory Representation of Sentiment Classified Data Using SVM
Vishal T.V.,Srinidhi S.,Dr. G. Muneeswari 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.4
In the past few years, microblogging websites have evolved to become a source of varied kind of information. Twitter is a popular microblogging website where users create short status messages called ‘tweets’. In this paper, we present a state-of-the art model trained using a support vector machine with Bag-Of-Words and TF-IDF features for each tweet. The proposed model provides a visual and an auditory representation of the sentiments that the tweets have been classified into. The results show a state-of-the art performance achieved by the model with a F1 measure of 77.47 and an accuracy of 77.93% which is better than the existing models.