Open AccessA Review on Feature Selection Techniques for Sentiment Analysis
Publication year - 2022
Publication title -
indian scientific journal of research in engineering and management
Language(s) - English
Resource type - Journals
ISSN - 2582-3930
DOI - 10.55041/ijsrem12024
Subject(s) - feature selection , sentiment analysis , naive bayes classifier , computer science , artificial intelligence , simplicity , classifier (uml) , selection (genetic algorithm) , data mining , machine learning , word lists by frequency , tf–idf , feature (linguistics) , natural language processing , pattern recognition (psychology) , support vector machine , sentence , philosophy , linguistics , epistemology , physics , quantum mechanics , term (time)
Sentiment Analysis is the technique of identifying and categorizing emotions in order to examine how people feel about services such as movies, products, events, and politics. It is a widely researched on topic in text mining. This paper presents a review and evaluation results for some feature selection techniques such as TF- IDF, document frequency, word frequency, sparsity reduction and chi square statistics. To test these feature selection techniques, the study used twitter data on stock market and Naïve Bayes Classifier for classification because of its computational simplicity and effectiveness. The implementation of the study has been done in R.