Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Abstract

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students' behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.

Comparison of Sentiment Analysis Methods Used to Investigate the Quality of Teaching Aids Based on Virtual Simulators of Embedded Systems

“…The study proposes a supervised aspect-based opinion mining system based on a two-layered LSTM model. Another new method of sentiment analysis of student feedback is proposed in [29]. It proposes an innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRFs).…”