Sentiment analysis and opinion mining on educational data: A survey

TL;DR

This survey reviews how sentiment analysis and opinion mining are applied in education to interpret student feedback, exploring techniques, AI methodologies, applications, challenges, and future directions.

Contribution

It provides a comprehensive overview of sentiment analysis techniques, AI applications, and challenges specific to educational data, highlighting recent advancements and future research directions.

Findings

Sentiment analysis enhances educational decision-making and pedagogy.

Various annotation techniques and AI models are effective in educational sentiment analysis.

Challenges like negation and opinion spam require ongoing research.

Abstract

Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, (1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and aspect level, (2) sentiment annotation techniques including lexicon-based and corpus-based approaches for unsupervised annotations are explored, (3) the role of AI in sentiment analysis with methodologies like machine learning, deep learning, and transformers are discussed, (4) the impact of sentiment analysis on educational procedures to enhance pedagogy, decision-making, and evaluation are presented. Educational institutions have been widely invested to build sentiment analysis tools and process their student feedback to draw their opinions and insights. Applications built on sentiment analysis of student feedback are reviewed in this study. Challenges in sentiment analysis like multi-polarity, polysemous, negation words, and opinion spam detection are explored and their trends in the research space are discussed. The future directions of sentiment analysis in education are discussed.