Hybrid optimization for feature selection in opinion mining

Abstract

A sub-discipline of Information Retrieval (IR) is opinion mining and the lexicon of computers is not concerned of the subject of the doc-ument, but about the opinion expressed. It has caused a large impact in the arena of academics and industry as it has a wide area of re-search and the applications are widespread. Feature selection is a vital step in opinion mining, as its individual feature decides the opin-ions expressed by the customers. Feature selection reduces the dimensionality of data by avoiding non-relevant features; it can be con-sidered as a necessary and excellent process for data mining applications. In this study, feature subset is optimized through Particle Swarm Optimization (PSO) algorithm, Cuckoo Search (CS) algorithm and hybridized PSO-CS algorithm. Classification is done through Naïve bayes and K-Nearest Neighbours (KNN) classifiers. Feature extraction has its basis on Term Frequency-Inverse Document Fre-quency (TF-IDF). The accuracy of classification precision is increased by the reduction in size of feature subset and computational com-plexity.