ANALISIS SENTIMEN PENGGUNA TWITTER TERHADAP INVESTASI KEUANGAN DI INDONESIA MENGGUNAKAN METODE NAIVE BAYES - Andreas Danny Agus Wahyudi; Tinaliah (PT. Hasba Edukasi Mandiri, 2025)
Financial investment is important for Indonesian people to prepare for their future financially. In this digital era, social media such as Twitter have become popular platforms for sharing opinions and views on various topics including financial investments. By leveraging the data available on Twitter, sentiment analysis can be used to understand user views and opinions regarding financial investments in Indonesia. The Naive Bayes method can be used to perform sentiment analysis on Twitter data by utilizing probability theory to classify tweets with positive, negative or neutral views about financial investment in Indonesia. The amount of tweet data is unbalanced, so it is necessary to do SMOTE over-sampling so that the dataset is balanced and do the testing using k-fold validation so that you can see the confision matrix and get the values for accuracy, precision, recall, and f1-score. Based on the sentiments obtained from Twitter social media, it shows that Twitter social media users have positive sentiments towards financial investment in Indonesia with a total number of positive sentiments of 426 data from a total of 1000 tweet data. Unbalanced data affects the classification results, namely an accuracy of 45% with the SMOTE up-sampling method and an accuracy of 89% without using the SMOTE up-sampling method.