Sentimental Analysis of Student Feedback using Machine Learning Techniques
Daneena Deeksha Dsouza1, Deepika2, Divya P Nayak3, Elveera Jenisha Machado4, Adesh N. D.5
1Daneena Deeksha Dsouza, Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Udupi (Karnataka), India.
2Deepika, Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Udupi (Karnataka), India.
3Divya P Nayak, Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management Bantakal-Udupi (Karnataka), India.
4Elveera Jenisha Machado, Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Udupi (Karnataka), India.
5Adesh N. D., Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Udupi (Karnataka), India.
Manuscript received on 07 June 2019 | Revised Manuscript received on 30 June 2019 | Manuscript Published on 04 July 2019 | PP: 986-991 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A11810681S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Educational institutions attempt to collect feedback from students to study their sentiment towards courses and facilitates provided by the institution to improve the college environment. In present scenario, grading technique is used for feedback. This grading technique does not reveal the true sentiment of students, but the textual feedback provides a chance to the students to highlight certain aspects. In this paper, a method has been proposed for sentimental analysis of student feedback using machine learning algorithms such as Support Vector Machine, Multinomial Naïve Bayes Classifier, and Random Forest. A comparative analysis is also conducted between these machine learning techniques. The experimental results suggest that Multinomial Naïve Bayes Classifier is more accurate than other methods.
Keywords: Sentimental Analysis, Multinomial Naïve Bayes, Machine Learning.
Scope of the Article: Machine Learning