Analyzing the Indonesian sentiment to rohingya refugees using IndoBERT model

This study aims to analyze public sentiments towards Rohingya refugees in
Indonesia using the IndoBERT model. We collected sentiment data from social
media platforms and news articles, followed by preprocessing techniques
including tokenization, cleaning, case folding, stemming, and filtering.
Sentiment labels were assigned using the InSet lexicon, and the IndoBERT
model was trained with these labeled data. Our findings reveal that the
predominant sentiment is negative, with 65% of the sentiments classified as
negative, 20% as neutral, and 15% as positive. The model demonstrated robust
performance with an accuracy of 87%, precision of 85%, recall of 83%, and an F1
score of 84%. This research addresses a gap in sentiment analysis studies related
to refugee issues and provides valuable insights into public perceptions, which
could inform policies and interventions aimed at improving refugee integration
and support systems in Indonesia.