Open Access

ARTICLE

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

by K. Sangeetha^1,*, D. Prabha²

1 Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India
2 Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, 641008, India

* Corresponding Author: K. Sangeetha. Email:

Computer Systems Science and Engineering 2022, 40(2), 735-747. https://doi.org/10.32604/csse.2022.019310

Received 09 April 2021; Accepted 21 May 2021; Issue published 09 September 2021

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.

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