Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis

Styawati Styawati; Auliya Rahman Isnain; Nirwana Hendrastuty; Lili Andraini

doi:10.30591/jpit.v6i1.3245

Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis

Styawati Styawati, Auliya Rahman Isnain, Nirwana Hendrastuty, Lili Andraini

Abstract

Twitter is a social media that is widely used by the public. Twitter social media can be used to express opinions or opinions about an object. This shows that there is a huge opportunity for data sources, so they can be used for sentiment analysis. There are many algorithms for performing sentiment analysis, including Support Vector Machine (SVM) and Naive Bayes (NB). Because of the many opinions regarding the performance of the two methods, the researcher is interested in classifying the data using the SVM and NB methods. The data used in this study is data on public opinion regarding the Covid-19 vaccination policy. The first classification process is carried out by the SVM method using various kernels. After getting the highest accuracy result, then the accuracy result is compared with the accuracy value from the NB method classification results.

Keywords

SVM, NB, Twitter, Sentiment Analysis, Covid-19 vaccination

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DOI: https://doi.org/10.30591/jpit.v6i1.3245