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Abstract
Big Data is used for a collection of data sets that are large and complex, which is difficult to store and process using available database management tools or traditional data processing applications. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. Big data is typically broken alone by these characteristics: volume, velocity and variety. Volume refers to the amount of data that is getting generated. Velocity point out the speed at which data is getting generated. Variety indicates the different types of data that is getting generated. These characteristics make it an extreme challenge for discovering useful knowledge from the Big Data. Emerging companies needed to find new technologies that would allow them to store, access, and analyze huge amounts of data in near real time so that they could monetize the benefits of owning this much data about participants in their networks. In particular, the innovations Map Reduce, Big Table, and Hadoop proved to be the sparks that led to a new generation of data management. Big Data could be of three types: Structured, Semi-Structured and Unstructured. Twitter is one of the famous social networks worldwide and Twitter tweets and other social media posts are some of the central source of unstructured data. With the help of machine learning algorithms, it is possible to change unstructured data into organized form. In this work, twitter data was taken as the dataset. For training data, supervised machine learning algorithms like Linear Support vector and Naïve Bayes are used. For analyzing the data, Python programming was used. The result shows that Naïve Bayes classifier yielded more classification accuracy than Linear Support vector classifier.