Comparison of Naïve Bayes Classifier and Support Vector Machine for sentiment analysis on civil military relations conflict among Rohingya refugees as recommendation for defense policy making - Putri, Nanda Selviana; Saragih, Hondor; Heikhmakhtiar, Aulia Khamas (Journal of Intelligent Decision Support System (IDSS), 2024)
This research focuses on the evaluating the performance of various sentiment analysis techniques using the Naive Bayes Classifier and Support Vector Machine in identifying civil-military conflicts among Rohingya refugees. The goal is to assist leaders in formulating defense policies. This research uses text data from news sources on Twitter, with a total of 5018 data that have been processed to become clean data, then divided into 1004 test data and 4018 training data to be classified using the Support Vector Machine and Naive Bayes methods. This research analyzes the sentiment and polarity of public opinion related to the issues that occur in this situation. The results of the sentiment analysis from the two methods are then classified using the Support Vector Machine and Naive Bayes methods, and then compared to determine which method is more effective in capturing the complex dynamics of sentiment. The findings of this research indicate that the Support Vector Machine method has a higher accuracy in identifying sentiments related to the civil-military conflict among Rohingya refugees, with an accuracy of 87.95%, compared to the Naive Bayes Classifier with an accuracy of 85.16%. The analysis results in the form of frequently occurring words in the true positive word cloud, namely apology, human, angry, and solidarity, are handed over to experts to be formulated into recommendation sentences and can be used to assist in the formulation of policies for defense decision-makers in more effectively addressing the Rohingya refugee issue.