SVM and Naïve Bayes Algorithm Comparison for User Sentiment Analysis on Twitter

Abstract

With the emergence of the Peduli Protect application, which is used by the government to monitor the spread of Covid-19 in Indonesia, it turns out to be reaping the pros and cons of public opinion on Twitter. From this phenomenon, a research was conducted by mapping the sentiment analysis of twitter users towards the Peduli Protect application. This study aims to compare two classification algorithms that are included in the supervised learning category. The two algorithms are Support Vector Machine (SVM) and Naïve Bayes. The two algorithms are implemented in analyzing the sentiment analysis of twitter user reviews on the Peduli Protect application. The dataset used in this research is tweets of twitter users with a total of 4,782 tweets. Then, compared to how much accuracy and processing time required of the two algorithms. The stages of the method in this research are: collecting data from user tweets with a crawling technique, preprocessing text, weighting words using the TF-IDF method, classification using the SVM and Naïve Bayes algorithm, k-fols cross validation test, and drawing conclusions. The results showed that the accuracy of the SMV algorithm with the k-fold test method was 86% and the split 8020 technique resulted in an accuracy of 79%. Meanwhile, the Naïve Bayes algorithm produces an accuracy of 85% with k-fold, and an accuracy of 80% with a split 8020. From these results it can be concluded that both algorithms have the same level of accuracy, only different in processing time, where Naïve Bayes algorithm is faster with time required 0.0094 seconds.

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