A Paralleled Big Data Algorithm with MapReduce Framework for Mining Twitter Data

Some recent studies have suggested that public opinions expressed in social media may be correlated with various social issues. To find out what actually can be discovered in social media data, we need data mining. Data mining approaches that can handle massive amount of data have recently been referred to as big data algorithms. In this paper, we propose a big data algorithm to handling Twitter data mining. Furthermore, to ensure scalability, MapReduce framework is adopted to parallelize the proposed algorithm. Through the experiments, the potential of the proposed algorithm can be demonstrated. Computationally, the speed of execution can be shown to increase significantly despite increases in data set size. In fact, the acceleration ratio increases as the size of the dataset increases, and as the number of Data Nodes increases.