A comprehensive dataset for aspect-based sentiment analysis in evaluating teacher performance

Abstract

Teacher performance evaluation is an essential task
in the field of education. In recent years, aspect-based sentiment
analysis (ABSA) has emerged as a promising technique for evaluating teaching performance by providing a more nuanced analysis
of student evaluations. This article presents a novel approach for
creating a large-scale dataset for ABSA of teacher performance
evaluation. The dataset was constructed by collecting student
feedback from American International University-Bangladesh
and then labeled by undergraduate-level students into three
sentiment classes: positive, negative, and neutral. The dataset
was carefully cleaned and preprocessed to ensure data quality
and consistency. The final dataset contains over 2,000,000 student
feedback instances related to teacher performance, making it
one of the largest datasets for ABSA of teacher performance
evaluation. This dataset can be used to develop and evaluate
ABSA models for teacher performance evaluation, ultimately
leading to better feedback and improvement for educators. The
results of this study demonstrate the usefulness and effectiveness
of ABSA in evaluating teacher performance and highlight the
importance of creating high-quality datasets for this task.