SKEM++: SEMANTIC KEYWORD EXTRACTION MODEL USING COLLECTIVE CENTRALITY MEASURE ON BIG SOCIAL DATA

Abstract

In recent times, Online Social Network (OSN) has accumulated a massive volume of user-generated data available in an unstructured format. It consists of user ideas, responses, and opinions on various topics. It extracts essential keywords in OSN, which is endowed with many exciting applications such as information recommendation or viral marketing. This paper emphasizes the importance of semantic graph-based methods for extracting vital keywords experimentally using a novel SKEM++ method. It is an innovative method for keyword extraction from OSN based on centrality measures. It utilizes a distributed computing approach to calculate the network Collective Centrality Measure (CCM) for each node and improve the semantics of keywords. The distributed approach is more scalable and computationally efficient than the conventional system, making it more suitable for large-scale real-time data sets such as the OSN. Experimental outcomes on the real-time Twitter Data set to infer the dominance of the proposed Collective Centrality Measure(CCM) method in evaluation with contemporary schemes in terms of F-score by 81% and recall by 80% and precision by 80% using Semantic Analysis.