Proposed Feedback Analysis Using Big Data Tools
BIG DATA has become the live wore amongst different industries over the last few year on such a pace that has resulted in the accumulation of huge amount of data every day. Big data is something which is applied to data of very huge amount making it almost inefficient to process operations using the traditional databases in a stipulated amount of time. It is considered to have a huge impact in transforming business and operations in various ways. Here the task is to efficiently store the data along with application of effective methodologies for accessing and classifying data within the given time period. The most adapted methodologies is to implement Hadoop which is an open source implementation of MapReduce programming model, enabling processing of data-intensive tasks in parallel on cluster of commodity servers. Its scalability factor is tremendous thus Hadoop enables its users to process parallel voluminous data and store data which was not possible in less scalable methodologies.
Feedback are important for the system enhancement, finding loop holes and as well as for proper work distribution. Feedback is important not only when it highlights weaknesses but also for strengths. If analysis of feedback is done in wrong way then the result of analysis will also be wrong. As a result, the pattern identified will also be incorrect thus making the whole system incorrect as a whole. An unstructured form of data contained within social media posts, blogs, online products, customer support interaction, etc. which shows either opinions or emotions is Sentiment Data. This is used by many organizations and companies to analyze public opinions at a particular moment and tracks them how they tend to change over time. Here we have divided sentiments broadly into positive, negative or neutral contained in the tweets comment. Twitter, which is a social media website, reports to receive about millions of tweets everyday ranging up to several zettabytes. This enormous amount of raw data can be structured into useful information for business and industrial purposed according to the requirement of need. In this paper, we are going to show how sentiment analysis can be done on the data collected from Twitter using the Flume API for data extraction from Twitter. Twitter has a vast collection of unstructured data, semi structured or even structured form for data that we can collect as BIGDATA using online streaming tool Flume. This implementation is further taken to include visualisation of the results into a pictorial representation of twitter users and their tweets.