How to cite this paper
Tuan, T., Loan, D & Kittiphattanabawon, N. (2025). Understanding students’ sentiment from feedback with a new feature selection and semantics networks.International Journal of Data and Network Science, 9(1), 253-266.
Refrences
Abualhaj, M., Al-Zyoud, M., Hiari, M., Alrabanah, Y., Anbar, M., Amer, A., & Al-Allawee, A. (2024). A fine-tuning of decision tree classifier for ransomware detection based on memory data. International Journal of Data and Network Science, 8(2), 733–742.
Ahn, E., & Kang, H. (2018). Introduction to systematic review and meta-analysis. Kja, 71(2), 103–112. https://doi.org/10.4097/kjae.2018.71.2.103
Aizawa, A. (2003). An information-theoretic perspective of tf–idf measures. Information Processing & Management, 39(1), 45–65.
AL-Akhras, M., Alghamdi, S., Omar, H., & Alshareef, H. (2024). A machine learning technique for Android malicious attacks de-tection based on API calls. Decision Science Letters, 13(1), 29–44.
Alamin, Z., Lorosae, T. A., Ramadhan, S., & others. (2024). Improving Performance Sentiment Movie Review Classification Us-ing Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine. Mathematical Modelling of Engineering Problems, 11(2).
Al-Dwish, G. M., & Aljohani, A. N. (2024). Sentiment Analysis of Saudi Arabian University Students by Using Ml Algorithms. Advances and Applications in Discrete Mathematics, 41(5), 393–410.
Aljrees, T. (2024). Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning. Plos One, 19(1), e0295632.
Alsubaie, F., & Aldoukhi, M. (2024). Using machine learning algorithms with improved accuracy to analyze and predict employ-ee attrition. Decision Science Letters, 13(1), 1–18.
Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2015). Predicting learning-related emotions from students’ textual classroom feed-back via Twitter. International Educational Data Mining Society.
Altun, A., Köklü, M., & others. (2022). Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA. International Journal of Industrial Engineering Computations, 13(4), 617–640.
Asghar, M. Z., Khan, A., Zahra, S. R., Ahmad, S., & Kundi, F. M. (2019). Aspect-based opinion mining framework using heuris-tic patterns. Cluster Computing, 22, 7181–7199.
Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.
Batool, A., & Byun, Y.-C. (2024). Towards Improving Breast Cancer Classification using an Adaptive Voting Ensemble Learning Algorithm. IEEE Access.
Bensba, A., Ahmim, N., Zakaria, C., & Bousbia, N. (2022). Analysis of Students’ Emotions in an Online Learning Environment. 2022 International Conference on Advanced Aspects of Software Engineering (ICAASE), 1–7.
Bhardwaj, A., & Srivastava, P. (2021). A machine learning approach to sentiment analysis on web based feedback. Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020, 127–139.
Bing, L. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press.
Carless, D., & Winstone, N. (2023). Teacher feedback literacy and its interplay with student feedback literacy. Teaching in Higher Education, 28(1), 150–163.
Chadaga, K., Prabhu, S., Sampathila, N., Chadaga, R., Umakanth, S., Bhat, D., & GS, S. K. (2024). Explainable artificial intelli-gence approaches for COVID-19 prognosis prediction using clinical markers. Scientific Reports, 14(1), 1783.
Dalal, R., Safhath, I., Piryani, R., Kappara, D. R., & Singh, V. K. (2015). A lexicon pooled machine learning classifier for opinion mining from course feedbacks. Advances in Intelligent Informatics, 419–428.
Dsouza, D. D., Deepika, D. P. N., Machado, E. J., Adesh, N. D., & others. (2019). Sentimental analysis of student feedback using machine learning techniques. International Journal of Recent Technology and Engineering, 8(14), 986–991.
Elbagir, S., & Yang, J. (2019). Twitter sentiment analysis using natural language toolkit and VADER sentiment. Proceedings of the International Multiconference of Engineers and Computer Scientists, 122(16).
Esparza, G. G., de-Luna, A., Zezzatti, A. O., Hernandez, A., Ponce, J., Álvarez, M., Cossio, E., & de Jesus Nava, J. (2018). A sen-timent analysis model to analyze students reviews of teacher performance using support vector machines. Distributed Compu-ting and Artificial Intelligence, 14th International Conference, 157–164.
Giang, N. T. P., Dien, T. T., & Khoa, T. T. M. (2020). Sentiment analysis for university students’ feedback. Advances in Infor-mation and Communication: Proceedings of the 2020 Future of Information and Communication Conference (FICC), Vol-ume 2, 55–66.
Gutiérrez, G., Ponce, J., Ochoa, A., & Álvarez, M. (2018). Analyzing Students Reviews of Teacher Performance Using Support Vector Machines by a Proposed Model. Intelligent Computing Systems: Second International Symposium, ISICS 2018, Meri-da, Mexico, March 21-23, 2018, Proceedings 2, 113–122.
Harish, B. S., Kumar, K., & Darshan, H. K. (2019). Sentiment analysis on IMDb movie reviews using hybrid feature extraction method.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining (SIGKDD), 168–177.
Imran, M., Hina, S., & Baig, M. M. (2022). Analysis of Learner’s Sentiments to Evaluate Sustainability of Online Education Sys-tem during COVID-19 Pandemic. Sustainability, 14(8), 4529.
Jain, R., Bawa, S., & Sharma, S. (2022). Sentiment analysis of COVID-19 tweets by machine learning and deep learning classifi-ers. Advances in Data and Information Sciences: Proceedings of ICDIS 2021, 329–339.
Jayaprakashpondy. (2022). Student Feedback dataset. Accessed: March. 13, 2024. https://www.kaggle.com/datasets/jayaprakashpondy/student-feedback/data
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., & Mikolov, T. (2016). FastText.zip: Compressing text classification models. ArXiv Preprint ArXiv:1612.03651.
Kabir, M. M., Othman, Z. A., & Yaakub, M. R. (2024). Enhanced Lexicon based Hybrid Method for Slang and Punctuation Scoring for Aspect Based Sentiment Analysis. 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 1418–1422.
Kathuria, A., Gupta, A., & Singla, R. K. (2023). AOH-Senti: Aspect-Oriented Hybrid Approach to Sentiment Analysis of Stu-dents’ Feedback. SN Computer Science, 4(2), 152.
Kaur, G., & Sharma, A. (2023). A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of Big Data, 10(1), 5.
Khan, J., Alam, A., & Lee, Y. (2021). Intelligent hybrid feature selection for textual sentiment classification. IEEE Access, 9, 140590–140608.
Li, S., Xie, Z., Chiu, D. K. W., & Ho, K. K. W. (2023). Sentiment analysis and topic modeling regarding online classes on the Reddit Platform: educators versus learners. Applied Sciences, 13(4), 2250.
Lin, Q., Zhu, Y., Zhang, S., Shi, P., Guo, Q., & Niu, Z. (2019). Lexical based automated teaching evaluation via students’ short reviews. Computer Applications in Engineering Education, 27(1), 194–205.
Loan, D. T. T., & Tuan, T. A. (2024). Towards visual sentiment summary to understand customers’ satisfaction. Bulletin of Electri-cal Engineering and Informatics, 13(4), 2774–2783.
Loria, S. (2018). Textblob Documentation. Release 0.15, 2(8), 269.
Louati, A., Louati, H., Kariri, E., Alaskar, F., & Alotaibi, A. (2023). Sentiment analysis of Arabic course reviews of a Saudi uni-versity using support vector machine. Applied Sciences, 13(23), 12539.
Mandouit, L. (2018). Using student feedback to improve teaching. Educational Action Research, 26(5), 755–769.
Melba Rosalind, J., & Suguna, S. (2021). Ensemble Model for Classifying Sentiments of Online Course Reviews using blended Feature Selection methods. Journal of Education: Rabindrabharati University.
Mercha, E. M., & Benbrahim, H. (2023). Machine learning and deep learning for sentiment analysis across languages: A survey. Neurocomputing, 531, 195–216.
Mudgal, P., & Khunteta, A. (2020). Handling double intensifiers in feature-level sentiment analysis based on movie reviews. In-ternational Conference on Artificial Intelligence: Advances and Applications 2019: Proceedings of ICAIAA 2019, 383–392.
Myo, W. W., Wettayaprasit, W., & Aiyarak, P. (2019). Designing classifier for human activity recognition using artificial neural network. 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), 81–85.
Nafea, I. T. (2018). Machine learning in educational technology. Machine Learning-Advanced Techniques and Emerging Applica-tions, 175–183.
Nasim, Z., Rajput, Q., & Haider, S. (2017). Sentiment analysis of student feedback using machine learning and lexicon based ap-proaches. 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), 1–6.
Nasir, A., Makki, S. V. A.-D., & Al-Sabbagh, A. (2024). Pandemia Prediction Using Machine Learning. PRZEGLĄD EL-EKTROTECHNICZNY, 5, 211–214.
Nasrulloh, S., Permanasari, A., & Kusumawardani, S. (2019). A Framework of Educational Feedback System with Statistical Method and Sentiment Analysis. 6th International Conference on Educational Research and Innovation (ICERI 2018), 310–316.
Pacol, C. A., & Palaoag, T. D. (2021). Enhancing sentiment analysis of textual feedback in the student-faculty evaluation using machine learning techniques. European Journal of Engineering Science and Technology, 4(1), 27–34.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., & others. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653–1670.
Pekrun, R., Marsh, H. W., Elliot, A. J., Stockinger, K., Perry, R. P., Vogl, E., Goetz, T., Van Tilburg, W. A. P., Lüdtke, O., & Vispoel, W. P. (2023). A three-dimensional taxonomy of achievement emotions. Journal of Personality and Social Psycholo-gy, 124(1), 145.
Rajesh, P., & Suseendran, G. (2020). Prediction of N-gram language models using sentiment analysis on E-learning reviews. 2020 International Conference on Intelligent Engineering and Management (ICIEM), 510–514.
Rakhmanov, O. (2020). On validity of sentiment analysis scores and development of classification model for student-lecturer comments using weight-based approach and deep learning. Proceedings of the 21st Annual Conference on Information Tech-nology Education, 174–179.
Ramasamy, L. K., Kadry, S., Nam, Y., & Meqdad, M. N. (2021). Performance analysis of sentiments in Twitter dataset using SVM models. International Journal of Electrical and Computer Engineering (IJECE), 11(3), 2275–2284.
Saha, R., Malviya, L., Jadhav, A., & Dangi, R. (2024). Early stage HIV diagnosis using optimized ensemble learning technique. Biomedical Signal Processing and Control, 89, 105787.
Sarmiento Varón, L., González-Puelma, J., Medina-Ortiz, D., Aldridge, J., Alvarez-Saravia, D., Uribe-Paredes, R., & Navarrete, M. A. (2023). The role of machine learning in health policies during the COVID-19 pandemic and in long COVID manage-ment. Frontiers in Public Health, 11, 1140353.
Shahi, T. B., Sitaula, C., & Paudel, N. (2022). A hybrid feature extraction method for Nepali COVID-19-related tweets classifica-tion. Computational Intelligence and Neuroscience, 2022(1), 5681574.
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, 100003.
Shenify, M. (2024). Sentiment analysis of Saudi e-commerce using na\"\ive bayes algorithm and support vector machine. Interna-tional Journal of Data and Network Science, 8(3), 1607–1612.
Sohel, A., Hossain, M. R., Mostofa, Z. B., Hasan, M. U., Das, U. C., & Parvin, S. K. (2023). Sentiment Analysis Based on Online Course Feedback Using Textblob and Machine Learning Techniques. 2023 26th International Conference on Comput-er and Information Technology (ICCIT), 1–6.
Spacy. (2024). spaCy library. In Accessed: March. 13, 2024. https://spacy.io/
Surya, P. P. M., & Subbulakshmi, B. (2019). Sentimental analysis using Naive Bayes classifier. 2019 International Conference on Vision towards Emerging Trends in Communication and Networking (ViTECoN), 1–5.
Tamrakar, M. L., & others. (2021). An Analytical Study Of Feature Extraction Techniques For Student Sentiment Analysis. Turk-ish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 2900–2908.
Tran, T. A., Duangsuwan, J., & Wettayaprasit, W. (2021a). A new approach for extracting and scoring aspect using SentiWordNet. Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 22(3), 1731–1738.
Tran, T. A., Duangsuwan, J., & Wettayaprasit, W. (2021b). A Novel framework for aspect knowledge base generated automatically from social media using pattern rules. Computer Science, 22.
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Aizawa, A. (2003). An information-theoretic perspective of tf–idf measures. Information Processing & Management, 39(1), 45–65.
AL-Akhras, M., Alghamdi, S., Omar, H., & Alshareef, H. (2024). A machine learning technique for Android malicious attacks de-tection based on API calls. Decision Science Letters, 13(1), 29–44.
Alamin, Z., Lorosae, T. A., Ramadhan, S., & others. (2024). Improving Performance Sentiment Movie Review Classification Us-ing Hybrid Feature TFIDF, N-Gram, Information Gain and Support Vector Machine. Mathematical Modelling of Engineering Problems, 11(2).
Al-Dwish, G. M., & Aljohani, A. N. (2024). Sentiment Analysis of Saudi Arabian University Students by Using Ml Algorithms. Advances and Applications in Discrete Mathematics, 41(5), 393–410.
Aljrees, T. (2024). Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning. Plos One, 19(1), e0295632.
Alsubaie, F., & Aldoukhi, M. (2024). Using machine learning algorithms with improved accuracy to analyze and predict employ-ee attrition. Decision Science Letters, 13(1), 1–18.
Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2015). Predicting learning-related emotions from students’ textual classroom feed-back via Twitter. International Educational Data Mining Society.
Altun, A., Köklü, M., & others. (2022). Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA. International Journal of Industrial Engineering Computations, 13(4), 617–640.
Asghar, M. Z., Khan, A., Zahra, S. R., Ahmad, S., & Kundi, F. M. (2019). Aspect-based opinion mining framework using heuris-tic patterns. Cluster Computing, 22, 7181–7199.
Baccianella, S., Esuli, A., & Sebastiani, F. (2010). SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining.
Batool, A., & Byun, Y.-C. (2024). Towards Improving Breast Cancer Classification using an Adaptive Voting Ensemble Learning Algorithm. IEEE Access.
Bensba, A., Ahmim, N., Zakaria, C., & Bousbia, N. (2022). Analysis of Students’ Emotions in an Online Learning Environment. 2022 International Conference on Advanced Aspects of Software Engineering (ICAASE), 1–7.
Bhardwaj, A., & Srivastava, P. (2021). A machine learning approach to sentiment analysis on web based feedback. Applications of Artificial Intelligence and Machine Learning: Select Proceedings of ICAAAIML 2020, 127–139.
Bing, L. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press.
Carless, D., & Winstone, N. (2023). Teacher feedback literacy and its interplay with student feedback literacy. Teaching in Higher Education, 28(1), 150–163.
Chadaga, K., Prabhu, S., Sampathila, N., Chadaga, R., Umakanth, S., Bhat, D., & GS, S. K. (2024). Explainable artificial intelli-gence approaches for COVID-19 prognosis prediction using clinical markers. Scientific Reports, 14(1), 1783.
Dalal, R., Safhath, I., Piryani, R., Kappara, D. R., & Singh, V. K. (2015). A lexicon pooled machine learning classifier for opinion mining from course feedbacks. Advances in Intelligent Informatics, 419–428.
Dsouza, D. D., Deepika, D. P. N., Machado, E. J., Adesh, N. D., & others. (2019). Sentimental analysis of student feedback using machine learning techniques. International Journal of Recent Technology and Engineering, 8(14), 986–991.
Elbagir, S., & Yang, J. (2019). Twitter sentiment analysis using natural language toolkit and VADER sentiment. Proceedings of the International Multiconference of Engineers and Computer Scientists, 122(16).
Esparza, G. G., de-Luna, A., Zezzatti, A. O., Hernandez, A., Ponce, J., Álvarez, M., Cossio, E., & de Jesus Nava, J. (2018). A sen-timent analysis model to analyze students reviews of teacher performance using support vector machines. Distributed Compu-ting and Artificial Intelligence, 14th International Conference, 157–164.
Giang, N. T. P., Dien, T. T., & Khoa, T. T. M. (2020). Sentiment analysis for university students’ feedback. Advances in Infor-mation and Communication: Proceedings of the 2020 Future of Information and Communication Conference (FICC), Vol-ume 2, 55–66.
Gutiérrez, G., Ponce, J., Ochoa, A., & Álvarez, M. (2018). Analyzing Students Reviews of Teacher Performance Using Support Vector Machines by a Proposed Model. Intelligent Computing Systems: Second International Symposium, ISICS 2018, Meri-da, Mexico, March 21-23, 2018, Proceedings 2, 113–122.
Harish, B. S., Kumar, K., & Darshan, H. K. (2019). Sentiment analysis on IMDb movie reviews using hybrid feature extraction method.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining (SIGKDD), 168–177.
Imran, M., Hina, S., & Baig, M. M. (2022). Analysis of Learner’s Sentiments to Evaluate Sustainability of Online Education Sys-tem during COVID-19 Pandemic. Sustainability, 14(8), 4529.
Jain, R., Bawa, S., & Sharma, S. (2022). Sentiment analysis of COVID-19 tweets by machine learning and deep learning classifi-ers. Advances in Data and Information Sciences: Proceedings of ICDIS 2021, 329–339.
Jayaprakashpondy. (2022). Student Feedback dataset. Accessed: March. 13, 2024. https://www.kaggle.com/datasets/jayaprakashpondy/student-feedback/data
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., & Mikolov, T. (2016). FastText.zip: Compressing text classification models. ArXiv Preprint ArXiv:1612.03651.
Kabir, M. M., Othman, Z. A., & Yaakub, M. R. (2024). Enhanced Lexicon based Hybrid Method for Slang and Punctuation Scoring for Aspect Based Sentiment Analysis. 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), 1418–1422.
Kathuria, A., Gupta, A., & Singla, R. K. (2023). AOH-Senti: Aspect-Oriented Hybrid Approach to Sentiment Analysis of Stu-dents’ Feedback. SN Computer Science, 4(2), 152.
Kaur, G., & Sharma, A. (2023). A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. Journal of Big Data, 10(1), 5.
Khan, J., Alam, A., & Lee, Y. (2021). Intelligent hybrid feature selection for textual sentiment classification. IEEE Access, 9, 140590–140608.
Li, S., Xie, Z., Chiu, D. K. W., & Ho, K. K. W. (2023). Sentiment analysis and topic modeling regarding online classes on the Reddit Platform: educators versus learners. Applied Sciences, 13(4), 2250.
Lin, Q., Zhu, Y., Zhang, S., Shi, P., Guo, Q., & Niu, Z. (2019). Lexical based automated teaching evaluation via students’ short reviews. Computer Applications in Engineering Education, 27(1), 194–205.
Loan, D. T. T., & Tuan, T. A. (2024). Towards visual sentiment summary to understand customers’ satisfaction. Bulletin of Electri-cal Engineering and Informatics, 13(4), 2774–2783.
Loria, S. (2018). Textblob Documentation. Release 0.15, 2(8), 269.
Louati, A., Louati, H., Kariri, E., Alaskar, F., & Alotaibi, A. (2023). Sentiment analysis of Arabic course reviews of a Saudi uni-versity using support vector machine. Applied Sciences, 13(23), 12539.
Mandouit, L. (2018). Using student feedback to improve teaching. Educational Action Research, 26(5), 755–769.
Melba Rosalind, J., & Suguna, S. (2021). Ensemble Model for Classifying Sentiments of Online Course Reviews using blended Feature Selection methods. Journal of Education: Rabindrabharati University.
Mercha, E. M., & Benbrahim, H. (2023). Machine learning and deep learning for sentiment analysis across languages: A survey. Neurocomputing, 531, 195–216.
Mudgal, P., & Khunteta, A. (2020). Handling double intensifiers in feature-level sentiment analysis based on movie reviews. In-ternational Conference on Artificial Intelligence: Advances and Applications 2019: Proceedings of ICAIAA 2019, 383–392.
Myo, W. W., Wettayaprasit, W., & Aiyarak, P. (2019). Designing classifier for human activity recognition using artificial neural network. 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), 81–85.
Nafea, I. T. (2018). Machine learning in educational technology. Machine Learning-Advanced Techniques and Emerging Applica-tions, 175–183.
Nasim, Z., Rajput, Q., & Haider, S. (2017). Sentiment analysis of student feedback using machine learning and lexicon based ap-proaches. 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), 1–6.
Nasir, A., Makki, S. V. A.-D., & Al-Sabbagh, A. (2024). Pandemia Prediction Using Machine Learning. PRZEGLĄD EL-EKTROTECHNICZNY, 5, 211–214.
Nasrulloh, S., Permanasari, A., & Kusumawardani, S. (2019). A Framework of Educational Feedback System with Statistical Method and Sentiment Analysis. 6th International Conference on Educational Research and Innovation (ICERI 2018), 310–316.
Pacol, C. A., & Palaoag, T. D. (2021). Enhancing sentiment analysis of textual feedback in the student-faculty evaluation using machine learning techniques. European Journal of Engineering Science and Technology, 4(1), 27–34.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., & others. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825–2830.
Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653–1670.
Pekrun, R., Marsh, H. W., Elliot, A. J., Stockinger, K., Perry, R. P., Vogl, E., Goetz, T., Van Tilburg, W. A. P., Lüdtke, O., & Vispoel, W. P. (2023). A three-dimensional taxonomy of achievement emotions. Journal of Personality and Social Psycholo-gy, 124(1), 145.
Rajesh, P., & Suseendran, G. (2020). Prediction of N-gram language models using sentiment analysis on E-learning reviews. 2020 International Conference on Intelligent Engineering and Management (ICIEM), 510–514.
Rakhmanov, O. (2020). On validity of sentiment analysis scores and development of classification model for student-lecturer comments using weight-based approach and deep learning. Proceedings of the 21st Annual Conference on Information Tech-nology Education, 174–179.
Ramasamy, L. K., Kadry, S., Nam, Y., & Meqdad, M. N. (2021). Performance analysis of sentiments in Twitter dataset using SVM models. International Journal of Electrical and Computer Engineering (IJECE), 11(3), 2275–2284.
Saha, R., Malviya, L., Jadhav, A., & Dangi, R. (2024). Early stage HIV diagnosis using optimized ensemble learning technique. Biomedical Signal Processing and Control, 89, 105787.
Sarmiento Varón, L., González-Puelma, J., Medina-Ortiz, D., Aldridge, J., Alvarez-Saravia, D., Uribe-Paredes, R., & Navarrete, M. A. (2023). The role of machine learning in health policies during the COVID-19 pandemic and in long COVID manage-ment. Frontiers in Public Health, 11, 1140353.
Shahi, T. B., Sitaula, C., & Paudel, N. (2022). A hybrid feature extraction method for Nepali COVID-19-related tweets classifica-tion. Computational Intelligence and Neuroscience, 2022(1), 5681574.
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, 100003.
Shenify, M. (2024). Sentiment analysis of Saudi e-commerce using na\"\ive bayes algorithm and support vector machine. Interna-tional Journal of Data and Network Science, 8(3), 1607–1612.
Sohel, A., Hossain, M. R., Mostofa, Z. B., Hasan, M. U., Das, U. C., & Parvin, S. K. (2023). Sentiment Analysis Based on Online Course Feedback Using Textblob and Machine Learning Techniques. 2023 26th International Conference on Comput-er and Information Technology (ICCIT), 1–6.
Spacy. (2024). spaCy library. In Accessed: March. 13, 2024. https://spacy.io/
Surya, P. P. M., & Subbulakshmi, B. (2019). Sentimental analysis using Naive Bayes classifier. 2019 International Conference on Vision towards Emerging Trends in Communication and Networking (ViTECoN), 1–5.
Tamrakar, M. L., & others. (2021). An Analytical Study Of Feature Extraction Techniques For Student Sentiment Analysis. Turk-ish Journal of Computer and Mathematics Education (TURCOMAT), 12(11), 2900–2908.
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