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Twitter Data Analysis Using Apache Streaming
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Author(s): Lavanya Sendhilvel (Vellore Institute of Technology, India), Kush Diwakar Desai (Vellore Institute of Technology, India), Simran Adake (Vellore Institute of Technology, India), Rachit Bisaria (Vellore Institute of Technology, India)and Hemang Ghanshyambhai Vekariya (Vellore Institute of Technology, India)
Copyright: 2023
Pages: 15
Source title:
Convergence of Big Data Technologies and Computational Intelligent Techniques
Source Author(s)/Editor(s): Govind P. Gupta (National Institute of Technology, Raipur, India)
DOI: 10.4018/978-1-6684-5264-6.ch002
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Real-time data from social network sites like Twitter or Facebook has been a popular source for analytics and researchers in the recent years due to various factors like large amount of data, structured-ness, and popularity. Analyzing data is a very common requirement today, but such requirements become difficult when there is a bulk of data which needs to processed and analyzed in real time. Analyzing large number of tweets from Twitter to get different patterns and extract useful information is a massive challenge. Apache Spark is a platform that can be used to handle big data efficiently, and it offers faster solutions compared to Hadoop. This chapter addresses the issue of real-time analyzing and filtering the tweets as per the user's requirements from among the millions of other streaming tweets and classifies them into various categories. It creates an interactive automatic system that splits data based on important keywords and displays a graphical representation of connected tweets using Apache Spark.
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