Feature Extraction based Text Classification: A review

Abstract

This article reviews and discusses feature extraction techniques used for text classification as well as natural language processing in biomedical applications.This researchaims to analyze the similarities of techniques used as technology and algorithms that have become more sophisticated to optimize feature extraction. In feature extraction, a specific of words is taken out from text data. After that,  they transform into a feature set to be usable by a classifier. Several algorithms have been identified for classification,including but not limited to SVM, deep neural networks as well as Naïve Bayes algorithms. Next, the natural language is processed and achieves better performance results as indicated by certain metrics like execution time, specificity, accuracy, specificity,and sensitivity.