Analysis and Classification of Multi-opinionated Content in the Era of Cyber Activism

Today, the use of Online Social Media (OSM) is not restricted to merely networking and socializing. Recent events all around the globe attest to the prevalence of use of OSM sites for bringing about dramatic and drastic reforms in real world, phenomenon being referred as Cyber Activism. The real world is marred with various turmoils and people hold myriad variety of views and judgments regarding various issues. Their opinions are often poorly backed by facts. We refer to such inconclusive judgements that users generate and propagate on OSM platforms as Multi-Opinionated Content. One of the greatest challenge in such an environment is to analyze and classify such content into multiple opinion classes. In this work, we propose a generic semi-supervised classification based methodology for analyzing and classifying multi-opinionated content. We have used widely known off-the-shelf classifiers namely KNN, decision tree and random forest in our approach. To implement and validate our methodology, we have mined opinions on content in various forms, namely videos, tweets and posts on three popular social media platforms namely Youtube, Twitter and Facebook, respectively. In our validation, we have taken the Kashmir conflict between India and Pakistan as our case study. We have used plethora of features in building the classification model. Our experiments show that Random Forest classifier gives maximum accuracy of 90.02% and user level features give the best results. Our work can be used to process large amount of multi-opinionated content for effective and accurate decision making in the era of cyber activism generating multi-opinionated content.