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Twitter Data Analysis
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Author(s): Chitrakala S (Anna University, India)
Copyright: 2018
Pages: 28
Source title:
Modern Technologies for Big Data Classification and Clustering
Source Author(s)/Editor(s): Hari Seetha (Vellore Institute of Technology-Andhra Pradesh, India), M. Narasimha Murty (Indian Institute of Science, India)and B. K. Tripathy (VIT University, India)
DOI: 10.4018/978-1-5225-2805-0.ch005
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Analyzing Social network data using Big Data Tools and techniques promises to provide information that could be of use in recommendation systems, personalized service and many other applications. A few of the analytics that do this include sentiment analysis, trending topic analysis, topic modeling, information diffusion modeling, provenance determination and social influence study. Twitter Data Analysis involves analyzing data specifically obtained from Twitter, both tweets and the topology. There are three major classifications on the type of analysis being performed such as Content based, Network based and Hybrid analysis. Trending Topic Analysis in the context of Content based static data analysis and Influence Maximization in the context of Hybrid analysis on data streams using the power of Big Data Analytics are discussed. A novel solution to Trending Topic analysis to generate topic evolved, conflict-free sequential sub summaries and influence maximization to handle streaming data are explained with experimental results.
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