The public sentiment and emotional variations in social media using twitter dataset

Abstract

The collection of applications (internet followed) that provide way to create communication of user-generated matter by the social media (Twitter, Facebook, Whatsapp, etc.,). Twitter is the micro-blogging platform. Thoughts and opinions about different aspects are shared by users. Analysis on sentiment expressed in a piece of text which expresses opinions, towards a particular topic, product, etc. (positive, negative, or neutral). Primary issues are previous techniques that have biased classification accuracy, due to data distribution in a non-balanced way. The existing methods are applied over small dataset which cannot be extended for generalization with expected accuracy. "Curse of dimensionality” still exists with higher number of attributes in existing methods. Weaker classification in non-linear context. Limited use of transforms (kernels) for linearity in higher dimensional spaces and lack of parameter tuning. In most of the real time dataset the neutral class is very high. The proposed system is the framework for text mining to handle theme extraction from twitter opinion dataset. The Learning models to be built using the Support Vector Tool (SVT) classification method with a kernel trick applied with composition using unigram, bigram and hybrid (unigram + bigram) features. The performance to be obtained by tuning the internal parameters. The result shows that SVT linear kernel with hybrid features are the best classifier when compare to other classifiers with maximum accuracy from the twitter opinion dataset.