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News media presents professionally verified occurrences or events, whereas social media presents the interests of the audience in these areas, and should therefore give insight into their quality. Social media services like Twitter can also provide additional or supporting information to a particular news media topic. Meanwhile, truly valuable information may be thought of as the area in which these two media sources topically intersect with each other. Unfortunately, even after elimination of unimportant content, there is still information overload in remaining news-related data, which must be prioritized for utilization. To assist in prioritization of news information, news must be ranked in order of estimated importance. At first, preprocessing is carried out. Key terms are extracted and filtered from news and social information admires a selected amount of your time. A graph is made (which is known as Key Term graph) from the antecedently extracted key term set, whose vertices represent the key terms and edges represent the co-occurrence similarity between them. The graph, when process and pruning, contains slightly joint clusters of topics in style in each print media and social media. Then the graph is clustered so as to get well-defined and disjoint sub graphs. The sub graphs from the main graph are selected and ranked based on user attention. Thus the thesis effectively identifies news topics that are prevalent in both social media and the news media, and then ranks them. The Louvain algorithm is used for communication detection for social media. Finally, the results are validated by using the Validation metrics Modularity and Edge density. Clique method is provides the best result, to compare with Louvain algorithm.

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How to Cite
C. Mani, & M.Mugilan. (2019). Effective ranking approach for E-data using extensible learning . International Journal of Intellectual Advancements and Research in Engineering Computations, 7(1), 732–743. Retrieved from