Journal of Digital Information Management Ali Sameer Al-Abeej 24 3 2026 https://doi.org/10.6025/jdim/2026/24/3/149-157 https://www.dline.info/fpaper/jdim/v24i3/jdimv24i3_2.pdf Social media contains opinions, ideas, and facts. Artificial intelligence (AI) has brought a mix of societal perspectives to social media events. This study used bilingual tweets (English and Malay). This study uses Malay stop words and number filters in data preprocessing, builds a machine learning classifier using mBERT, support vector machine, and neural network models, and measures performance using recall, precision, Fmeasure, and accuracy for each model. In the experimental results, Malay stop words and number filters caused variability in classifier performance. Malay stop words and number filters are important in preprocessing student sentiment analysis in this study, although they do not significantly affect the change in accuracy. This paper also discusses the KNIME workflow and the experimental results obtained. This study argues that the neural network is the best classifier, and the model's algorithm can be used to perform sentiment analysis on a new dataset.