Sentiment Analysis of Rohingya Refugees in Aceh using Support Vector Machine (SVM) and Multinomial Logistic Regression

Gigih Army Buana Baliputra, Slamet Kacung, Budi Santoso

Abstract


The rapid development of information technology affects the massive dissemination of information. Social media is one of them, and it contributes to communication and information technology. Information about Rohingya ethnic refugees in Aceh has spread widely on social media. This research aims to analyze public sentiment regarding ethnic Rohingya refugees in Aceh on X and YouTube, categorized into positive, neutral, and negative. This study aims to develop an application that uses the Support Vector Machine (SVM) and Multinomial Logistic Regression techniques to conduct sentiment analysis on public opinion with positive, neutral, and negative classifications regarding Rohingya refugees in Aceh. The 3683 comments collected through web crawling were categorized into positive, negative, and neutral sentiments. The analysis results show that 2112 data were classified as negative sentiments, 1400 as neutral sentiments, and 171 as positive sentiments. Based on the test results, the SVM and Multinomial Logistic Regression methods have similar accuracy of 83.18%. However the SVM method obtained 74.65% precision and 65.15% recall. Meanwhile, the Multinomial Logistic Regression method obtained 75.28% precision and 66.84% recall.

Keywords


sentiment analysis; natural language processing; machine learning; deep learning; data mining; text mining; public opinion;

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References


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DOI: https://doi.org/10.32520/stmsi.v14i3.5159

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