A Comprehensive Survey on Effective Feature Selection Approaches for Text Sentiment Classification Process

Abstract

Feature Selection (FS) is the selection of certain features by using a criterion assessment application. In the age of technological advancements and innovations, there has been a significant rise in the use of different social networking applications such as Facebook, YouTube, Twitter, and e-commerce platforms such as Amazon, Alibaba, Flipkart, and others. It has increased the use of sentiment analysis in the different real-world applications so that there is a classification of text in different preset polarities such as optimistic, pessimistic, and impartial. The current research aims to find out the selection process involved in choosing the optimal features for classification using sentimental textual data. The study presents the scope of the sentimental analysis using textual data, fundamentals of the classification process, and the significance of the feature selection schemes so that there is an implementation of effective feature selection for text classification and analysis of sentiments.