Towards a framework on sentiment analysis of educational domain for improving the teaching and learning services

Mat Zin Harnani, Mustapha Norwati, Azmi Murad Masrah Azrifah, Mohd Sharef Nurfadhlina,

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Type :	article
Subject :	L Education (General)
ISSN :	2289-7844
Main Author :	Mat Zin Harnani, Mustapha Norwati, Azmi Murad Masrah Azrifah, Mohd Sharef Nurfadhlina,
Title :	Towards a framework on sentiment analysis of educational domain for improving the teaching and learning services

Place of Production :	Universiti Pendidikan Sultan Idris
Year of Publication :	2017

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Analysing students’ feedback and their expressed emotions toward any subjects could help lecturers to understand their students’ learning behaviour. Several platforms are used by students to express their feelings such as through social networking sites, blogs, discussion forums and the university survey systems. However, the feedbacks typically contain thousands of sentences and are from various sources which makes analysing them a cumbersome and tedious work. In this regard, sentiment analysis (SA) has been proposed to automate the process of mining user feedback into valuable information. This paper discusses the principles of SA, its potential benefits, and its application in the educational field based on the synthesis of previous studies. We suggest that SA can help lecturers to easily understand the needs and problems of their students. In particular, a framework and a performance evaluation method were proposed to help guide the implementation of the SA in the education domain.

References

1. Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2014a). Learning sentiment from student’s feedback for real-time interventions in classrooms. Lecture Notes in Computer Science, 8779, 40–49. Retrieved from http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6984506 2. Altrabsheh, N., Cocea, M., & Fallahkhair, S. (2014b). Sentiment Analysis: Towards a Tool for Analysing Real-Time Students Feedback. In International Conference on Tools with Artificial Intelligence, ICTAI (pp. 419–423). http://doi.org/10.1109/ICTAI.2014.70 3. Bing Liu. (2010). Sentiment Analysis and Subjectivity. In Handbook of Natural Language Processing (pp. 1–38). http://doi.org/10.1145/1772690.1772756 4. Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 54(4), 82–89. 5. Guitart, I., Conesa, J., Villarejo, L., Lapedriza, A., Masip, D., Perez, A., & Planas, E. (2013). Opinion Mining on Educational Resources at the Open University of Catalonia. In 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (pp. 385–390). IEEE. http://doi.org/10.1109/CISIS.2013.70 6. Haddi, E., Liu, X., & Shi, Y. (2013). The Role of Text Pre-processing in Sentiment Analysis. In Procedia Computer Science (Vol. 17, pp. 26–32). Elsevier B.V. http://doi.org/10.1016/j.procs.2013.05.005 7. Li, C., & Ma, J. (2012). Research on online education teacher evaluation model based on opinion mining. In National Conference on Information Technology and Computer Science (CITCS 2012) (pp. 1061–1064). http://doi.org/doi:10.2991/citcs.2012.264 8. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. (B. Liu, Ed.), Morgan & Claypool. Morgan & Claypool Publishers. http://doi.org/10.2200/S00416ED1V01Y201204HLT016 9. Manning, C. D., Raghavan, P., & Schutze, H. (2009). An Introduction to Information Retrieval. Cambridge University Press. http://doi.org/10.1109/LPT.2009.2020494 10. Mishra, N. ., & Jha, C. K. . (2014). An insight into task of opinion mining. In Lecture Notes of the Institute for Computer Sciences (Vol. 117, pp. 185–190). Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-84921384276&partnerID=40&md5=cb39ca050af23697b78393da851e2cc6 11. Murugananthan, V., & ShivaKumar, B. L. (2016). An adaptive educational data mining technique for mining educational data models in elearning systems. Indian Journal of Science and Technology, 9(3), 1–5. http://doi.org/10.17485/ijst/2016/v9i3/86392 12. Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31(1), 527–541. http://doi.org/10.1016/j.chb.2013.05.024 13. Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations & Trends in Information Retrieval, 2(1–2), 1–135. 14. Ravi, K., Ravi, V., Siddeshwar, V., & Mohan, L. (2015). Sentiment Analysis Applied to Educational Sector. In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). http://doi.org/10.1109/ICCIC.2015.7435667 15. Zarra, T., Chiheb, R., Faizi, R., & Afia, A. El. (2016). Using Textual Similarity and Sentiment Analysis in Discussions Forums to Enhance Learning. International Journal of Software Engineering and Its Applications, 10(1), 191–200. 16. Zhang, L., & Liu, B. (2014). Aspect and Entity Extraction for Opinion Mining. In Data Mining and Knowledge Discovery for Big Data (Vol. 1, pp. 1–40). http://doi.org/10.1007/978-3-642-40837-3

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