Comparison of Opinion Mining Algorithms (Decision Tree and Naïve Bayes) for iOS 16 Opinion Classification on Twitter (Journal of Applied Statistics and Data Science, 2025)
This study analyzes public response to the release of iOS 16 by Apple Inc. on September 13, 2022. The iOS operating system, exclusive to iPhone devices, generated diverse opinions across social media platforms, particularly Twitter. The research aims to compare the effectiveness of Decision Tree and Naïve Bayes algorithms in classifying public sentiment toward iOS 16, considering the simplicity of both algorithms as an advantage in classification method implementation. The research methodology involved data collection using web crawling techniques through Jupyter Notebook, resulting in 14,946 Indonesian-language tweets gathered during the period of September 13-27, 2022. The tweets underwent a series of processing stages, including comprehensive text preprocessing and sentiment analysis using VADER lexicon polarity detection to categorize tweets into positive, negative, and neutral sentiments. The dataset was divided with a 90:10 ratio for training and testing data. The findings demonstrate that the Decision Tree algorithm outperforms Naïve Bayes in classifying opinions about iOS 16. The Decision Tree achieved superior performance metrics with a Recall of 79.39%, Precision of 79.73%, and Accuracy of 80.60%.