Feature Selection and Polarity Classification Using Machine Learning Algorithms NB & SVM
6 Pages Posted: 15 Jul 2019 Last revised: 30 Sep 2019
Date Written: May 18, 2019
Sentiment analysis and its classification of social data has become challenging now a days because of unstructured nature of data, slang, misspells and abbreviations used by customers while giving comments or reviews. Using machine learning approach for sentiment analysis helps in finding useful patterns and derive predictions which are important in decision making for improvement of overall products and customer satisfaction. In this paper we use tweets for famous mobile brands like Iphone, Vivo and Red MI. Machine learning algorithm like naïve Bayes and SVM are used to find polarity of tweets like positive, negative or neutral. This helps to find popular brands. Also we compare overall accuracy of these algorithms using measures like precision and recall and f measure.
Keywords: Naïve Bayes, Polarity Detection, Sentiment Analysis, SVM
JEL Classification: Y60
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