Aspect-based Sentiment Analysis Model for Evaluating Teachers’ Performance from Students’ Feedback

Abstract

Evaluating teachers’ performance is a fundamental
pillar of educational enhancement, guiding the evolution of pedagogical practices and fostering enriched learning environments.
This study pioneers an innovative approach by harnessing sentiment analysis within an aspect-based framework to decipher the
intricate emotional nuances embedded within students’ feedback.
By categorizing sentiments as positive, negative, and neutral, we
delve into the diverse perceptions of teaching aspects, offering
a multifaceted portrait of educators’ contributions. Through
meticulous data collection, preprocessing, and a deep learning
sentiment analysis model, we dissected student comments into
distinct teaching aspects. The subsequent sentiment analysis
unearthed positive, negative, and neutral sentiments. Positive
sentiments highlighted strengths and effective communication,
while negative sentiments illuminated areas for growth. Neutral
sentiments provided contextual equilibrium, forming a holistic
tapestry of teachers’ performance. The proposed model achieved
86% F1 score for classifying sentiments into three classes.