Buddy Analytics Based On Social Media Using Hadoop
Mangesh U. Sanap, Prof. V. S. Phad
Big Data, Hadoop, Map Reduce, HDFS, Pig, Sentimental Analysis, Flume.
Social Network Sites play important roles in peoples lives for sharing information. Facebook, Twitter becomes one of the important platforms for interaction. Facebook allows people to have their own accounts to comment, express feelings and convey emo tions via texts as well as emotions. Over the last few years BIG-DATA used in different industries, on a scaling that generated lots of data every day. Big Data is a collection of data that is huge and complex to process using data processing applications, Hadoop is a distributed paradigm used to manipulate the large amount of data. It holds the large amount data and perform the operations like data and result analysis,data analytics. Hadoop is highly scalable computing platform. E-commerce sites and social media sites like Facebook, Twitter, Instagram are receives comments, tweets and customer reviews in millions per day in the range of Zettabyte each year. Lot of research is going on analysis of reviews given by people. We can collect data from the social media site by using online streaming tool Flume. We are using Pig and its queries to give the sentiment data based up on the groups. Pig and Hive approach to compute huge volumes of data since Google implemented its platform on Google Distributed File Systems (GFS) and then Amazon Web Service (AWS) provides its services with Hadoop. so that we use Proposed algorithm with Pig/Hive. This our Analytics project provides a way of analyzing of big data such as social media data using Apache Hadoop which will process and analyze the comments/tweets or reviews on a Hadoop clusters and recommends a suitable users. This study is significant of enabling the stakeholders such as administrators and businessmen to monitor any discussion issue for enhancing their services.
Article Details
Unique Paper ID: 143898

Publication Volume & Issue: Volume 3, Issue 3

Page(s): 208 - 214
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