A Framework of Educational Feedback System with Statistical Method and Sentiment Analysis
- 10.2991/iceri-18.2019.9How to use a DOI?
- educational feedback system, sentiment analysis, support vector machine, similarity removal
Sentiment analysis has long and widely used in attempt to know public sentiment towards an entity, including in education, specifically in evaluation of learning. The key to success of sentiment analysis is use of right method and data are valid. The Essay for analytical data is sometimes made carelessly but method used to eliminate this data only using stop word removal and no tendency to recognize pattern of user behavior. In addition, sentiment analysis results also require a comparison with other evaluation system. This study makes framework educational feedback system that can generate qualitative data from essay and quantitative data from performance assessment questionnaires. Data obtained from lecturers’ evaluation by students in Educational Higher School of Muhammadiyah Kuningan. Qualitative data processed with sentiment analysis using Support Vector Machine (SVM) algorithm, but first performed similarity removal in preprocessing stage to remove essay that made carelessly. Then quantitative data processed with statistical method. Each output is a score that can be correlated to measure relationship between them. As a result, sentiment analysis with SVM able to produce 91% sentiment accuracy and correlation between performance score with sentiment score is 0,73 which means have a high relationship.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Sofhian Nasrulloh AU - Adhistya Permanasari AU - Sri Kusumawardani PY - 2019/07 DA - 2019/07 TI - A Framework of Educational Feedback System with Statistical Method and Sentiment Analysis BT - Proceedings of the 6th International Conference on Educational Research and Innovation (ICERI 2018) PB - Atlantis Press SP - 310 EP - 316 SN - 2352-5398 UR - https://doi.org/10.2991/iceri-18.2019.9 DO - 10.2991/iceri-18.2019.9 ID - Nasrulloh2019/07 ER -