A semantic graph-based keyword extraction model using ranking method on big social data

Abstract

Identification of an influential node is essential to control Online Social Networks. It is a crucial task with various kinds of real-time usages such as information retrieval and recommendation, instinctive keyword indexing, viral marketing, instinctive classification, and filtering. Existing graph-based methods consider only numeric measures such as centrality, degree, and betweenness for keyword extraction. In this research, a relatively effective method called Semantic graph-based Keyword Extraction Method (SKEM) from Twitter using ranking methods is proposed. In the proposed model, the exhaustive preprocessing is carried out, and a semantic graph-based model has been constructed. The numeric graph metrics are then used to weigh the nodes of the semantic graph. Page Rank algorithm is applied to arrange the nodes, and the top ten nodes that are found to be very relevant and effectively represent the most influential node. Combining both semantic as well as numeric graph metrics, it greatly enhances the quality of keywords extracted. The extensive preprocessing enhances the quality of the input and minimizes the noise in the input. The keywords extracted by the proposed model have been more relevant and meaningful. The performance of the proposed SKEM model is validated with real-time tweets of Twitter API. The experimental results are confirmed that the proposed method is achieving high performance in terms of precision, recall, and F-measure.