Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 14, Pages: 1-9
Irfan Ali Kandhro1*, Muhammad Ameen Chhajro1, Kamlesh Kumar1, Haque Nawaz Lashari1 and Usman Khan2
1Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan
[email protected], [email protected], [email protected], [email protected]
2Department of Computer Science, PAF-KIET, Karachi, Pakistan; [email protected]
*Author for correspondence
Irfan Ali Kandhro
Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan.
Email: [email protected]
Objectives: The study has been carried out to present the sentiment analysis model for improving the quality of teaching in academic institutions, particularly at universities. The purpose of this study is to explore the different machine learning techniques to identify its importance as well as to raise interest in this research area. In this regard, the student feedback dataset has been collected at the end of the semester Fall-2018 from both Public and Private sector universities of Karachi, through the Google Survey Forms. The dataset contains valuable information about the quality of teaching and learning. Methods/Statistical Analysis: The model used the Multinomial Naive Bayes, Stochastic Gradient Decent, Support Vector Machine, Random Forest and Multilayer Perceptron Classifier. The result was analyzed through the evaluation metrics i.e. Confusion Matrix, Precision, Recall and F-score. Findings: It is found that the performance of MNB and MLP remained effective as compared to other approaches. It is recommended that MNB and MLP should be used in the research context for the classification of the text. It has great significance for future researchers in sentences and text classification. Application/Improvements: The study helps for improving the quality of teaching in education system. And moreover, it will be upgrade by increasing the data samples of neutral comments in dataset.
Keywords: Course Evaluation, Machine Learning, Opinion Mining, Sentiment Analysis, Student Feedback
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