Improved Accuracy of Naïve Bayes Algorithm and Support Vector Machine Using Particle Swarm Optimization for Menstrual Cup Sentiment Analysis on Twitter

Abstract

Menstrual cup is a menstrual hygiene sanitation tool that replaces disposable sanitary napkins for women that reaps many pros and cons in its use. From this, it is necessary to analyze the public's views regarding the use of menstrual cups, which is called sentiment analysis. Sentiment analysis is a process that aims to determine the polarity of the sentiment of a text. This paper performs a classification of menstrual cup sentiment analysis on Twitter using the Naïve Bayes and the Support Vector Machine  algorithm. Particle Swarm Optimization is applied to improve the accuracy of both classification algorithms. The final result of the accuracy obtained by the Naïve Bayes algorithm is 92.72% and the Support Vector Machine  algorithm is 96.13%. While the accuracy results after Particle Swarm Optimization is applied, for Naïve Bayes it produces an accuracy rate of 95.87%, and Support Vector Machine is 96.68%.