Sentiment Analysis Against Rohingya Immigrants On Twitter Using The Support Vector Machine Method

Aulia, Lailatul Husna
Sriani, Sriani

JINAV: Journal of Information and Visualization
Vol. 5 No. 1 (2024)

The influx of Rohingya migrants into Indonesia has sparked diverse reactions from the public, ranging from both positive and negative perspectives. These viewpoints have surfaced prominently on Twitter, where netizens express conflicting opinions that often lead to division and discord. This study seeks to examine the sentiment of public opinion regarding Rohingya immigrants on Twitter, employing a Support Vector Machine with RBF kernel implemented in Python as its analytical method. The sentiment resulting from the crawling process on Twitter was 1347 pieces of data. In the analysis, the comparison between training data and test data was 8: 2. The dataset after preprocessing consisted of 1321 data, 1056 of which were training data while 265 were test data. The results of sentiment analysis show that the SVM method can be used to analyze sentiment, the accuracy value obtained is 72%, precision is 100%, recall is 2%, and f1-Score is 3%.