Feature Subsumption for Sentiment Classification of Dynamic Data in Social Networks using SCDDF

Abstract

The analysis of opinions till now is done mostly on static data rather than on the dynamic data. Opinions may vary in time. Earlier methods concentrated on opinions expressed in an individual site. But on a given concept opinions may vary from site to site. Also the past works did not consider the opinions at aggregate level.This paper proposes a novel method for Sentiment Classification that uses Dynamic Data Features (SCDDF). Experiments were conducted on various product reviews collected from different sites using QTP. Opinions were aggregated using Bayesian networks and Natural Language Processing techniques. Bulk amount of dynamic data is considered rather than the static one. Our method takes as input a collection of comments from the social networks and outputs ranks to the comments within each site and finally classifies all comments irrespective of the site it belongs to. Thus the user is presented with overall evaluation of the product and its features.