User Analytics on Twitter Stream Data

Twitter, is one of the largest social media site that receives tweets in millions of data in every day in range of Petabytes per year. Big Data is a pool of information that is outsized and difficult to progression by data processing applications, Hadoop is a disseminated archetype used to handle the huge quantity of documents. It grasps the vast quantity of documents and carry out the procedures like documents analysis, outcome analysis, and records analytics. It is highly scalable computing platform. Productive E-commerce sites, Facebook, Twitter one of the largest social media site receives comments, tweets or customer reviews in millions every day in the range of terabyte or petabytes per day. Ideas and opinions of people are influenced by the opinions of other people. Lot of research is going on analysis of reviews given by people. We can collect the data from the social media site by using BIGDATA eco-system using online streaming tool Flume. This huge amount of raw data can be used for industrial or business. This Analytics paper provides a way of analyzing of big data such as Twitter data using Apache Hadoop which will process and analyze the tweets on a Hadoop clusters. In this paper, we are going to talk how effectively sentiment analysis is done on the data which is collected from the Twitter using Flume. Twitter is an online web application which contains huge amount of data that can be a structured, semi-structured and un-structured data. Twitter is also difficult due to language that is used for comments. So here we are taking sentiment analysis, for this we are using Hive and its queries to give the sentiment data based up on the groups that we have defined in the HQL (Hive Query Language). Here we have categorized this sentiment analysis into 3 groups like comments that are having positive, moderate and negative comments.