Sentiment Analysis of Digital Television Migration on Twitter Using Naïve Bayes Multinomial Comparison, Support Vector Machines, and Logistic Regression Algorithms

Abstract

The Ministry of Communication and Information Technology (KEMENKOMINFO) has announced to the publics in Indonesia regarding the termination of analog television broadcasts or called analog switch-off, which requires the public to migrate from analog television to digital television. Regarding the process of stopping analog broadcasts this raises pros and cons by the people in Indonesia. Many people give their respective opinions through social media, especially on Twitter. A collection of pros and cons data from the public can be collected and used as research of sentiment analysis. This research will focus on comparing three classification algorithms, which is called Multinomial Naïve Bayes, Support Vector Machines, and Logistic Regression using the same dataset and the same method called Lexicon Based. The results showed that the highest accuracy is Support Vector Machines with the accuracy is 94.00%, Logistic Regression with the accuracy is 90.00%, and Multinomial Naïve Bayes with the accuracy is 88.00%.

Keywords

• sentiment analysis
• multinomial naïve bayes
• support vector machines
• logistic regression
• twitter