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.
[1] Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R. J. (2011, June). Sentiment analysis of Twitter data. In Proceedings of the Workshop on Language in Social Media (LSM 2011) (p. 30–38). Association for Computational Linguistics.
[2] Ahmad, M., Aftab, S., Ali, I. (2017). Sentiment analysis of tweets using SVM. International Journal of Computer Applications, 177(5), 25–29. https://doi.org/10.5120/ijca2017915758.
[3] Al Sallab, A., Hajj, H., Badaro, G., Baly, R., El-Hajj, W., Shaban, K. (2015, July). Deep learning models for sentiment analysis in Arabic. In Proceedings of the Second Workshop on Arabic Natural Language Processing (p. 9–17). Association for Computational Linguistics.
[4] Al-Saffar, A., Awang, S., Tao, H., Omar, N., Al-Saiagh, W., Albared, M. (2018). Malay sentiment analysis based on combined classification approaches and Senti-lexicon algorithm. PLoS ONE, 13(4), e0194852. https://doi.org/ 10.1371/journal.pone.0194852.
[5] Er, M. J., Liu, F., Wang, N., Zhang, Y., Pratama, M. (2016, July). User-level Twitter sentiment analysis with a hybrid approach. In International Symposium on Neural Networks (pp. 426–433). Springer.
[6] Ersahin, B., Aktas, Ö., Kilinc, D., Ersahin, M. (2019). A hybrid sentiment analysis method for Turkish. Turkish Journal of Electrical Engineering Computer Sciences, 27(3), 1780–1793.
[7] Fadel, I., Cemil, Ö. Z. (2020). A sentiment analysis model for terrorist attacks reviews on Twitter. Sakarya University Journal of Science, 24(6), 1294–1302.
[8] Gabarron, E., Dorronzoro, E., Rivera-Romero, O., Wynn, R. (2019). Diabetes on Twitter: A sentiment analysis. Journal of Diabetes Science and Technology, 13(3), 439–444. https://doi.org/10.1177/1932296818811679.
[9] Abdullah, A. S., Degan, M., Mohammad, A. J. M. (2024). Audit committee accounting background and audit fees: Evidence from Malaysia. Journal of Accounting and Financial Studies (JAFS), 19(68), 341–349.
[10] Ghaderi, R. (2015). Improving the retrieval of related questions in Stack Overflow (Doctoral dissertation, University of California, Irvine).
[11] Govindarajan, M. (2013). Sentiment analysis of movie reviews using hybrid method of Naive Bayes and genetic algorithm. International Journal of Advanced Computer Research, 3(4), 139–145.
[12] Iqbal, F., Hashmi, J. M., Fung, B. C., Batool, R., Khattak, A. M., Aleem, S., Hung, P. C. (2019). A hybrid framework for sentiment analysis using genetic algorithm-based feature reduction. IEEE Access, 7, 14637–14652.
[13] Jain, A. P., Dandannavar, P. (2016). Application of machine learning techniques to sentiment analysis. In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (p. 628–632). IEEE.
[14] Jansen, B. J. (2010). Gen X and Y’s attitudes on using social media platforms for opinion sharing. In CHI ’10 Extended Abstracts on Human Factors in Computing Systems (p. 3853–3858). ACM.
[15] Kamel, M., Selim, S. Z. (1994). The application of induction learning algorithm. Pattern Recognition, 27(3), 421–428.
[16] Medhat, W., Hassan, A., Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.
[17] Nahar, K. M., Jaradat, A., Atoum, M. S., Ibrahim, F. (2020). Sentiment analysis and classification of Arab Jordanian Facebook comments for Jordanian telecom companies using lexicon based approach and machine learning. International Journal on Emerging Technologies, 6(3), 52–71.
[18] Khan, S. I., Abdullah, N. N., Degan, M., Mohammed, A. J., Shexa, N. S. (2023). Consequences of job insecurity on job performance among bank employees: Exploring the work engagement as mediating role. Mitanni Journal of Humanitarian Sciences, 4(2), 528–534. https://doi.org/10.25156/ptjhss.v4n2y2023.p528-534.
[19] Pang, X., Zhou, Y., Wang, P., Lin, W., Chang, V. (2020). An innovative neural network approach for stock market prediction. The Journal of Supercomputing, 76(3), 2098–2118. https://doi.org/10.1007/s11227-019-02754.
[20]Rajaraman, A., Ullman, J. D. (2011). Data mining. In Mining of Massive Datasets (p. 1–17). Cambridge University Press. https://doi.org/10.1017/CBO9781139058452.002.
[21] Ren, Y. W. (2016). A topic enhanced word embedding for Twitter sentiment classification. Information Sciences, 369, 188–198.
[22] Roberts, K. R. (2012). EmpaTweet: Annotating and detecting emotions on Twitter. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC’12) (p. 3806–3813).
[23] Singhal, T. (2020). A review of coronavirus disease 2019 (COVID-19). The Indian Journal of Pediatrics, 87, 281–286. https://doi.org/10.1007/s12098-020-03263-6.
[24] Alfawareh, F. S., Degan, M., Mohammad, A. J. (2025). The influence of FinTech and gender diversity on bank financial stability: Experience from Jordan. Discover Sustainability, 6, Article 511. https://doi.org/10.1007/ s43621-025-01382-8.
[25] Soliman, T. H., Elmasry, M. A., Hedar, A., Doss, M. M. (2014). Sentiment analysis of Arabic slang comments on Facebook. International Journal of Computers Technology, 12(5), 3470–3478.
[26] Soumeur, A., Mokdadi, M., Guessoum, A., Daoud, A. (2018). Sentiment analysis of users on social networks: Overcoming the challenge of the loose usages of the Algerian dialect. Procedia Computer Science, 142, 26–37.
[27] Degan, M., Al-Abeej, A. S., Obaid, A. H. (2025). The effect of artificial intelligence on accounting and finance. International Journal of Scientific Conferences, 26, 764–777.
[28] Verma, B., Thakur, R. S. (2018). Sentiment analysis using lexicon and machine learning-based approaches: A survey. In Proceedings of International Conference on Recent Advancement on Computer and Communication (p. 441–447). Springer.
[29] Xhymshiti, M. (2020). Domain independence of machine learning and lexicon based methods in sentiment analysis (Bachelor’s thesis, University of Twente).
[30] Yin, M., Wortman Vaughan, J., Wallach, H. (2019, May). Understanding the effect of accuracy on trust in machine learning models. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (p. 1–12). ACM.