Ontology Driven Social Big Data Analytics for Fog enabled Sentic-Social Governance

Abstract

Conventional e-government has many practical infrastructure development and implementation challenges. The recent surge of SMAC (Social media, Mobile, Analytics, Cloud) technologies re-defines the e-governance ecosystem. Cloud-based e-governance has numerous operational challenges which range from development to implementation. Moreover, the contemplation and vocalization of public opinion about any government initiative is quintessential to be cognizant of how citizens perceives and get benefitted from cloud/fog enabled governance. This research puts forward a semantic knowledge model for investigating public opinion towards adaption of fog enabled services for governance and comprehending the significance of two s-components (sentic and social) in aforesaid structure that specifically visualize fog enabled Sentic-Social Governance. The results using conventional TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction are empirically compared with ontology driven TF-IDF feature extraction to find the best opinion mining model with optimal accuracy. The results depict that the implementation of ontology driven opinion mining for feature extraction in polarity classification outperforms the traditional TF-IDF method validated over baseline supervised learning algorithms. An average of 7.3% improvement in accuracy and approximately 38% reduction in features has been reported.