Feature Selection and Polarity Classification Using Machine Learning Algorithms NB & SVM

6 Pages Posted: 15 Jul 2019 Last revised: 30 Sep 2019

See all articles by Smita Bhanap

Smita Bhanap

Fergusson College Pune 4 India

Seema Babrekar

Dr. BAMU, Aurangabad

Date Written: May 18, 2019

Abstract

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

Suggested Citation

Bhanap, Smita and Babrekar, Seema, Feature Selection and Polarity Classification Using Machine Learning Algorithms NB & SVM (May 18, 2019). Proceedings of International Conference on Communication and Information Processing (ICCIP) 2019, Available at SSRN: https://ssrn.com/abstract=3419763 or http://dx.doi.org/10.2139/ssrn.3419763

Smita Bhanap (Contact Author)

Fergusson College Pune 4 India ( email )

Seema Babrekar

Dr. BAMU, Aurangabad ( email )

India

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