Predicting Rupiah Sentiment Using Social Sentiment Analysis

In recent years, the behavior of foreign currency exchange rates (FOREX) has attracted a lot of attention from policy makers, investors, academics and regulators because due to its rapidly changing prices. FOREX predicting based on sentiment analysis has attracted a wide variety of research in finance and natural language processing. The availability of news, social media networks, and the rapid development of natural language processing methods result in better predictive performance. However, new studies related on Rupiah (Rupiah is legal money circulating in Indonesia) sentiment and its correlation with other FOREX are still rare and interesting to study.The purpose of this study is to (1) assess the accuracy of predicting Rupiah sentiment using social sentiment analysis and (2) to investigate the correlation between Rupiah sentiment and IDR currency exchange rate. The methodological approach used for this study is based on social sentiment analysis on Twitter and Pearson correlation.The result of this study is (1) from the seven models that we compared, the Decision Tree (DT) and Random Forest (RF) algorithm models have the best accuracy to avoid misclassification based on AUC. The accuracy measured by F1-score has values of 92.13% for both DT and RF, and (2) Rupiah sentiment has a strong positive correlation with USD (P-value: 0.017, r-value: 0.421) and SAR (P-value: 0.016, r-value: 0.419) exchange rate. On the other hand, Rupiah sentiment has a low significant negative correlation with AUD (P-value: 0.051, r-value: -0.403) and EUR (P-value: 0.079, r-value: -0.479). Rupiah sentiment also has a low but positive correlation with MYR (P-value: 0.073, r-value: 0.180) and is not significant with SGD (P-value: 0.119, r-value: -0.348) although it has a positive correlation. The strong positive correlation represents that the more positive Rupiah sentiment on Twitter, the stronger IDR exchange rate against another foreign currency, and vice versa.