Sentiment Analysis of Domestic Violence Issues on Twitter Using Multinomial Naïve Bayes and Support Vector Machine

Abstract

Cases of domestic violence (KDRT) always attract numerous public comments on Twitter's social media platform. This research aims to conduct a sentiment analysis classification regarding ongoing cases of KDRT on Twitter. The study employs the Multinomial Naive Bayes and SVM algorithms to test accuracy in classifying tweets. The research methodology includes the following steps: data collection from Twitter, data preprocessing, sentiment analysis, sentiment classification using SVM and Multinomial Naïve Bayes algorithms, and analysis of results from both algorithms. The research findings indicate that the SVM algorithm achieves the highest accuracy rate, reaching 73% at an 80:20 ratio. In comparison, the Multinomial Naïve Bayes algorithm attains an accuracy rate of 70% at the same ratio. Therefore, it can be concluded that the SVM algorithm exhibits better accuracy compared to the Multinomial Naïve Bayes algorithm.