Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 8, Pages: 1-16
Irfan Ali Kandhro1*, Shaukat Wasi2, Kamlesh Kumar1, Malook Rind1 and Muhammad Ameen1
1Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan;
[email protected], [email protected], [email protected], [email protected]
2Department of Computer Science, Mohammad Ali Jinnah University, Karachi, Pakistan;
*Author for correspondence
Irfan Ali Kandhro
Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan.
Email: [email protected]
Objectives: Teacher’s evaluation in education system is quite important to improve the learning experience ininstitutions. For this purpose, sentiment analysis model is developedto identify the student sentiments from the piece of text. Methods/ Statistical Analysis: Long Short-Term Memory Model (LSTM) is used for analyzing the sentiments expressed by students through textual feedback. For this purpose, dataset has been built through student’s feedback and then divided into 70% and 30% for training and testing. The proposed model has been trained using softmax and adam along with drop out values 0.1 and 0.2. Obtained results showed that our model provides 99%, and 90% accuracy over training and validation with 0.2 and 0.5 losses respectively. Findings: It was found that proposed model provides an efficient way for sentiment analysis for teacher’s evaluation. Model used input as word embedding over the LSTM for mapping the words. Andmoreover, the model is collected significant semantic and syntactic information by implementing pre-trained word vector model. Hence, this model has the prospective to overcome several flaws in traditional methods e.g., bag-of-words, n-gram, Naïve Bayes and SVM models where order and information about word is vanished. The experimental results show that the model can achieve state-ofthe-art accuracy on student feedback dataset. 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, Opinion Mining, Sentiment Analysis, Student’s Feedback, LSTM, RNN
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