Sentiment Analysis of Student’s Opinion on Programming Assessment: Evaluation of Naïve Bayes over Support Vector Machines

Authors

  • Mahmood Umar Department of Computer Science, Faculty of Science, Sokoto State University, P.M.B 2134, Birnin Kebbi Road (Near Airport) Sokoto State Nigeria https://orcid.org/0000-0001-8138-2917
  • Nor Bahiah Ahmad School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Anazida Zainal School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/ijic.v10n2.278

Keywords:

Sentiment Analysis, Programming Assessment, Naïve Bayes, Support Vector Machines and Lexicon-Based Approach

Abstract

This study investigates the performance of machine learning algorithms for sentiment analysis of students’ opinions on programming assessment. Previous researches show that Support Vector Machines (SVM) performs the best among all techniques, followed by Naïve Bayes (NB) in sentiment analysis. This study proposes a framework for classifying sentiments, as positive or negative using NB algorithm and Lexicon-based approach on small data set. The performance of NB algorithm was evaluated using SVM. NB and SVM conquer the Lexicon-based approach opinion lexicon technique in terms of accuracy in the specific area for which it is trained. The Lexicon-based technique, on the other hand, avoids difficult steps needed to train the classifier. Data was analyzed from 75 first year undergraduate students in School of Computing, Universiti Teknologi Malaysia taking programming subject. The student’s sentiments were gathered based on their opinions for the zero-score policy for unsuccessful compilation of program during skill-based test. The result of the study reveals that the students tend to have negative sentiments on programming assessment as it gives them scary emotions. The experimental result of applying NB algorithm yields a prediction accuracy of 85% which outperform both the SVM with 70% and Lexicon-based approach with 60% accuracy. The result shows that NB works better than SVM and Lexicon-based approach on small dataset. 

Author Biography

Mahmood Umar, Department of Computer Science, Faculty of Science, Sokoto State University, P.M.B 2134, Birnin Kebbi Road (Near Airport) Sokoto State Nigeria

Mahmood Umar Department of Computer Science, Faculty of Science, Sokoto State University, P.M.B 2134, Birnin Kebbi Road (Near Airport) Sokoto State Nigeria.

Assistant Lecturer

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Published

2020-11-19

Issue

Section

Computer Science