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

Harnani Mat Zin

QR Code Link :
Type :	Article
Subject :	QA Mathematics
ISSN :	2289-7844
Main Author :	Harnani Mat Zin
Additional Authors :	Norwati Mustapha Masrah Azrifah Azmi Murad Nurfadhlina Mohd Sharef
Title :	Towards a framework on sentiment analysis of educational domain for improving the teaching and learning services
Hits :	6

Place of Production :	Tanjong Malim
Publisher :	Fakulti Komputeran dan META-Teknologi
Year of Publication :	2017
Notes :	Vol. 4 (2017): Journal of ICT in Education (JICTIE)
Corporate Name :	Perpustakaan Tuanku Bainun
PDF Full Text :	Login required to access this item.

Abstract : Perpustakaan Tuanku Bainun

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. Keywords: Opinion, sentiment analysis, sentiment analysis in educational domain.

References

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

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

Bing Liu. (2010). Sentiment Analysis and Subjectivity. In Handbook of Natural Language Processing (pp. 1–38). http://doi.org/10.1145/1772690.1772756

Feldman, R. (2013). Techniques and Applications for Sentiment Analysis. Communications of the ACM, 54(4), 82–89.

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

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

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

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

Manning, C. D., Raghavan, P., & Schutze, H. (2009). An Introduction to Information Retrieval. Cambridge University Press. http://doi.org/10.1109/LPT.2009.2020494

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

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

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

Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations & Trends in Information Retrieval, 2(1–2), 1–135.

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

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.

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

This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials.
You may use the digitized material for private study, scholarship, or research.

Back to search page