Aspect-Oriented Lexicon-Based Sentiment Analysis of Students’ Feedback

Abstract

The evaluation of feedback collected from students at the end of the year is very essential for every educational institution. It is important to improve the teaching–learning process and the annual appraisal process. The existing approach utilizes a Likert scale questionnaire, which allows students to express their level of agreement or disagreement with given statements or provide a neutral response. Additionally, the feedback form includes open-ended questions where students can provide textual feedback. This study introduces a Lexicon-based approach to automatically analyze the textual feedback concerning different aspects of teaching. Aspect-based Sentiment Analysis (ABSA) of student feedback aims to identify sentiments expressed toward various aspects of teachers, such as their ability to address student doubts and their overall knowledge. This study explores linguistic characteristics found in sentences, including negation, modifiers and contact shifters. To assess the sentiment of a sentence, the SentiWordNet lexicon is utilized to assign scores to individual words. Based on these scores, the sentence is categorized as either positive, negative or neutral. According to the experimental findings, the Aspect-Oriented Lexicon-Based (AOLB) approach demonstrates superior performance compared to other baseline methods when it comes to accurately scoring sentiment. The approach achieved a high accuracy rate of 94% for the student feedback dataset-I, 74% for the student feedback dataset-II, 55% for laptop reviews and 59% for restaurant reviews in the SemEval 2014 dataset-III.