An improved Artificial Fish Swarm Optimization based Feature Selection in big data Sentimental Analysis Classification

Abstract

Big data is a term that defines immense data volumes. The details can be organized as well as unstructured. Since big data are created from different fields and resources it is too complicated to deal with conventional data processing methods. Large data processing tasks, in particular in the choice of features, play an important role in eliminating redundancies. Several methods for selecting features like greedy cat-swarm optimization are now available. However, the greedy collection of features is more difficult and even with the efficient algorithms can also generate the worst solutions. In order to overcome this problem, we propose an artificial fish swarm optimization feature based on sentimental research classification to improve the functionality of big data selection. Preprocessing data was the first step followed by the selection of features through Improved Artificial Fish Swarm Optimization (IAFSO).