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Influence Tracker using Sementic Analysis


Lande Bhakti Dadabhau , Jaihind College of Engineering; Dhobale Madhuri Pandurang, Jaihind College of Engineering; Nighot Rutuja Dattatray, Jaihind College of Engineering; Wagh Rupali Kailas, Jaihind College of Engineering


Data Mining, Influential Nodes Tracking, Social Network, Greedy Algorithm, Sentiment Analysis


The problem of increasing influence spread has been widely studied in social networks, because of its extreme number of applications in determining critical topics in a social network for information dissemination. In survey, all the method are static in nature, which are designed for social networks with a constant set of links. However, no of forms of social interactions are flexible in nature, with relatively short periods of interaction. Any influence spread may happen only during the period of interaction, and the probability of spread is a function of the corresponding interaction time. In such cases, it may be useful to consider the influential nodes based on the run time interaction patterns. Alternatively, one may wish to find the most likely starting points for a given infection pattern. We will propose methods which can be used both for reduction of information spread, as well as the backward tracing of the source of influence spread. The LDA (Latent Dirichlet Allocation), Sentiment Analysis and greedy algorithms are used. We will present practical results implement the effectiveness of our approach on a number of real data sets.

Other Details

Paper ID: IJSRDV7I30855
Published in: Volume : 7, Issue : 3
Publication Date: 01/06/2019
Page(s): 1624-1625

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