The performance of Naïve Bayes, support vector machine, and logistic regression on Indonesia immigration sentiment analysis

Priati Assiroj, Asep Kurnia, Sirojul Alam


In recent years various attempts have been made to automatically mine opinions and sentiments from natural language in online networking messages, news, and product review businesses. Sentiment analysis is needed as an effort to improve service performance in the organization. In this paper, we have explored the polarization of positive and negative sentiments using Twitter user reviews. Sentiment analysis is carried out using the Naïve Bayes (NB), support vector machine (SVM), and logistic regression (LR) model then compares the results of these three models. The results of the experiment showed that the accuracy of LR was better than SVM and NB, namely 77%, 76%, and 70%.


Immigration; Logistic regression; Naïve Bayes; Sentiment analysis; Support vector machine

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).