A Comparative Study of Multi-class Classification Models and Data Pre-Processing Methods for Analyzing Malaysian University Students' Views on Ai Chatbot
Ali Sameer Al-Abeej
Journal of Digital Information Management, Vol. 24, No. 3, 2026.
DOI: https://doi.org/10.6025/jdim/2026/24/3/149-157
Link: https://www.dline.info/fpaper/jdim/v24i3/jdimv24i3_2.pdf
Abstract:
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.