Home⇛Isabela State University Linker: Journal of Engineering, Computing and Technology⇛vol. 2 no. 2 (2025)

Sentiment Analysis and Word Cloud of Teachers’ Evaluations Using R Programming Language

Catleen Glo M. Feliciano

Discipline: Computer Science

 

Abstract:

Faculty evaluation is essential for understanding students' perceptions and feedback to improve the employment of teaching strategies. With the use of vast-scale textual feedback, in an efficient manner, sentiment analysis was used as a tool for analyzing textual semantics in a structured way that could help facilitate understanding of what students think. Using the datasets of students' feedback from faculty evaluation from A.Y. 2019-2020 to A.Y. 2024-2025 for sentiment analysis using R programming, this study utilized Natural Language Processing (NLP). Data preprocessing, word cloud creation, and sentiment classification using code were employed to systematically extract prevalent themes, classify sentiments, and examine faculty performance. The approach comprises several processes, such as data preprocessing, word cloud generation, and sentiment classification, which are used to classify sentiments that follow an organized topic extraction and present useful insights about teacher performance. In fact, according to the data, students are overwhelmingly positive, with a deep appreciation for teachers who are helpful, efficient, and supportive in their teaching style and approach. The result also reflects how much students value the hard work that their teachers do, such as the top positive word is kind (mabait). Though they are less common, unfavorable opinions do draw attention to the areas in which students struggle, especially when it comes to their academic performance. While there are terminologies that reflect occasional problems in the classroom, where the top negative words are limit and hardship (hirap), it was noted that certain students struggle with their tasks. The results highlight how crucial it is to have a welcoming and interesting learning environment. Teachers may reinforce their strengths and highlight areas for growth by using sentiment analysis to get insightful information about student responses. Finally, by ensuring a well-rounded, efficient, and student-centered teaching approach for students pursuing a Bachelor of Science in Computer Science, this study offers a data-driven method of improving the learning experience.



References:

  1. Akhil, K., Vamsi, T. B., Soujanya, S., Harshitha, K. K. S., Mounisha, M., & Srihitha, N. (2024). Analyzing unstructured data: Natural language processing frameworks, challenges, approaches, and applications. In 2024 IEEE 4th International Conference on ICT in Business, Industry & Government (ICTBIG) (pp. 1–9). IEEE. https://doi.org/10.1109/ICTBIG64922.2024.10911818
  2. Arifin, A., Suryaningsih, S., & Arifudin, O. (2024). The relationship between classroom environment, teacher professional development, and student academic performance in secondary education. International Education Trend Issues, 2(2), 151–159. https://doi.org/10.56442/ieti.v2i2.467
  3. Balahadia, F. F., & Comendador, B. E. V. (2016). Adoption of opinion mining in the faculty performance evaluation system by the students using Naive Bayes algorithm. International Journal of Computer Theory and Engineering, 8(3), 255–259. https://doi.org/10.7763/IJCTE.2016.V8.1054
  4. Balahadia, F. F., Fernando, M. C. G., & Juanatas, I. C. (2016). Teacher’s performance evaluation tool using opinion mining with sentiment analysis. In 2016 IEEE Region 10 Symposium (TENSYMP) (pp. 95–98). IEEE. https://doi.org/10.1109/TENCONSpring.2016.7519384
  5. Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to revisit existing student performance evaluation approaches in the higher education sector in a new era of ChatGPT: A case study. Cogent Education, 10(1), Article 2210461. https://doi.org/10.1080/2331186X.2023.2210461
  6. Chavan, R., Latthe, S., Dhorepati, M., Suryawanshi, A., Sharma, N., & Salge, A. (2024). Sentiment analysis using VADER and word cloud techniques. In AIP Conference Proceedings (Vol. 3217, No. 1, Article 020012). AIP Publishing. https://doi.org/10.1063/5.0234543
  7. Constantinou, C., & Wijnen-Meijer, M. (2022). Student evaluations of teaching and the development of a comprehensive measure of teaching effectiveness for medical schools. BMC Medical Education, 22(1), Article 113. https://doi.org/10.1186/s12909-022-03148-6
  8. Delgado, D. A., & Cabilles, R. (2024). Challenges in the implementation of online teaching and learning in Thailand: Insights for educational policy. Isabela State University Linker: Journal of Education, Social Sciences and Allied Health, 1(2), 54–65. https://doi.org/10.65141/jessah.v1i2.n6
  9. Deshpande, S. B., Tangod, K. K., Srinivasaiah, S. H., Alahmadi, A. A., Alwetaishi, M., Ong Michael, G. K., & Rajendran, S. (2025). Elevating educational insights: Sentiment analysis of faculty feedback using advanced machine learning models. Advances in Continuous and Discrete Models, 2025(1), Article 89. https://doi.org/10.1186/s13662-025-03933-9
  10. Dogra, V., Verma, S., Kavita, Chatterjee, P., Shafi, J., Choi, J., & Ijaz, M. F. (2022). A complete process of text classification system using state-of-the-art NLP models. Computational Intelligence and Neuroscience, 2022, Article 1883698. https://doi.org/10.1155/2022/1883698
  11. Facciolo, F., & Pittenger, A. (2024). A review of performance evaluation paradigms involving practice faculty. American Journal of Pharmaceutical Education, 88(11), Article 101293. https://doi.org/10.1016/j.ajpe.2024.101293
  12. Grimalt-Álvaro, C., & Usart, M. (2024). Sentiment analysis for formative assessment in higher education: A systematic literature review. Journal of Computing in Higher Education, 36(3), 647–682. https://doi.org/10.1007/s12528-023-09370-5
  13. Hasan, M., Ahmed, T., Islam, M. R., & Uddin, M. P. (2024). Leveraging textual information for social media news categorization and sentiment analysis. PLOS ONE, 19(7). https://doi.org/10.1371/journal.pone.0307027
  14. Jim, J. R., Talukder, M. A. R., Malakar, P., Kabir, M. M., Nur, K., & Mridha, M. F. (2024). Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review. Natural Language Processing Journal, 6, Article 100059. https://doi.org/10.1016/j.nlp.2024.100059
  15. Kim, J., Li, X., & Bergin, C. (2024). Characteristics of effective feedback in teacher evaluation. Educational Assessment, Evaluation and Accountability, 36(2), 195–214. https://doi.org/10.1007/s11092-024-09434-9
  16. Lin, F., Li, C., Lim, R. W. Y., & Lee, Y. H. (2025). Empower instructors with actionable insights: Mining and visualizing student written feedback for instructors’ reflection. Computers and Education: Artificial Intelligence, 8, Article 100389. https://doi.org/10.1016/j.caeai.2025.100389
  17. Payandeh, A., Ghazanfarpour, M., Khoshkholgh, R., Malakoti, N., Afiat, M., & Shakeri, F. (2023). Views of students and faculty members on faculty evaluation by students: A systematic review. Medical Education Bulletin, 4(1), 659–671.
  18. Poonputta, A., & Nuangchalerm, P. (2024). A model framework for enhancing twenty-first century competencies in primary school teachers within northeastern Thailand’s sub-area. International Journal of Learning, Teaching and Educational Research, 23(1), 98–113. https://doi.org/10.26803/ijlter.23.1.6
  19. Raees, M., & Fazilat, S. (2024). Lexicon-based sentiment analysis on text polarities with evaluation of classification models. arXiv. https://doi.org/10.48550/arXiv.2409.12840
  20. Rongali, S. K. (2025). Natural language processing (NLP) in artificial intelligence. World Journal of Advanced Research and Reviews, 25(1), 1931–1935. https://doi.org/10.30574/wjarr.2025.25.1.0277
  21. Salgado, K. D., Arboleda, E., Cabardo, M. J., Obejera, C. M., & Dioses, J., Jr. (2024). Prediction model on the relationship of undergraduate grades and licensure examination performance of BS Agriculture and Biosystems Engineering. Isabela State University Linker: Journal of Engineering, Computing and Technology, 1(1), 15-32. https://doi.org/10.65141/ject.v1i1.n2
  22. Shahare, G., Manchalwar, J., Wankhede, K., Meshram, S., Kalbande, K., & Wyawahare, N. (2024). Exploring multidisciplinary data visualization and data analysis with R. In 2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC) (pp. 1–6). IEEE. https://doi.org/10.1109/AKGEC62572.2024.10869196
  23. Sharma, N. A., Ali, A. S., & Kabir, M. A. (2025). A review of sentiment analysis: Tasks, applications, and deep learning techniques. International Journal of Data Science and Analytics, 19, 351–388. https://doi.org/10.1007/s41060-024-00594-x
  24. Skeppstedt, M., Ahltorp, M., Kucher, K., & Lindström, M. (2024). From word clouds to Word Rain: Revisiting the classic word cloud to visualize climate change texts. Information Visualization, 23(3), 217–238. https://doi.org/10.1177/14738716241236188
  25. Sweta, S. (2024). Sentiment analysis and its application in educational data mining. Springer Nature. https://doi.org/10.1007/978-981-97-2474-1
  26. Takaki, P., & Dutra, M. L. (2023). Text mining applied to distance higher education: A systematic literature review. Education and Information Technologies, 29(9), 11241–11265. https://doi.org/10.1007/s10639-023-12235-0.
  27. Yacoub, A. D., Slim, S., & Aboutabl, A. (2024). A survey of sentiment analysis and sarcasm detection: Challenges, techniques, and trends. International Journal of Electrical and Computer Engineering Systems, 15(1), 69–78. https://doi.org/10.32985/ijeces.15.1.7

Tools


Copyright © 2026  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic