Sentiment Analysis of Student�s Comment by using Long-Short Term Model

Malook Rind,Shaukat Wasi,Muhammad Ameen,Kamlesh Kumar,Irfan Ali Kandhro

doi:10.17485/ijst/2019/v12i8/141741

Abstract

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, Naive 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

Highlights

Sentiment Analysis (SA) often known as opinion miningis used for analyzing or classifying user intentions from the word, sentences or document
The results showed that combines Lexicon-based and Learn-based techniques (CLL) performed well for books and hotels but achieved lower rate for electronics
The ratio between the correctly predicted samples and the number of predictions including correct and incorrect predictions which are observed by the system is known as the precision, ; the ratio between the prediction which are correctly predicted and the number of predictions including correct and incorrect predictions observed by the model and the number of sentiments which are truly labeled

Summary

Introduction

Sentiment Analysis (SA) often known as opinion miningis used for analyzing or classifying user intentions from the word, sentences or document. The focus in this research work to address data mining problem in education[2] like Schools, Colleges, Universities; the evaluation process is a quite complex and depends upon different factors in order to maintain quality standards[3,4,5,6]. For this purpose, different approaches are being used to collect data i.e., formal and informal methods in order to analyze opinion from students as to improve the learning and way of teaching

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Discussion

Conclusion