# Sentiment analysis and opinion mining on educational data: A survey

arXiv: https://arxiv.org/abs/2302.04359 · HTML: https://mlsanity.com/paper/2302.04359/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 103 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets](https://mlsanity.com/paper/1308.6242/) (2013)
- [FedStack: Personalized activity monitoring using stacked federated learning](https://mlsanity.com/paper/2209.13080/) (2022)
- [A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis](https://mlsanity.com/paper/2301.08826/) (2023)
- [Word Sense Disambiguation for 158 Languages using Word Embeddings Only](https://mlsanity.com/paper/2003.06651/) (2020)
- [Learning review representations from user and product level information for spam detection](https://mlsanity.com/paper/1909.04455/) (2019)
- [SETSum: Summarization and Visualization of Student Evaluations of Teaching](https://mlsanity.com/paper/2207.03640/) (2022)

## Built on by (most cited first, 8 of 8)

- [A Large Language Model Approach to Educational Survey Feedback Analysis](https://mlsanity.com/paper/2309.17447/) (2023)
- [BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.07365/) (2024)
- [A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2605.25502/) (2026)
- [An Integrated NPL Approach to Sentiment Analysis in Satisfaction Surveys](https://mlsanity.com/paper/2307.11771/) (2023)
- [Comparative Analysis of Libraries for the Sentimental Analysis](https://mlsanity.com/paper/2307.14311/) (2023)
- [Identification of Books That are Suitable for Middle School Students Using Artificial Neural Networks](https://mlsanity.com/paper/2311.07591/) (2023)
- [What AI Speaks for Your Community: Polling AI Agents for Public Opinion on Data Center Projects](https://mlsanity.com/paper/2511.22037/) (2025)
- [TeachPro: Multi-Label Qualitative Teaching Evaluation via Cross-View Graph Synergy and Semantic Anchored Evidence Encoding](https://mlsanity.com/paper/2601.09246/) (2026)

## Abstract (arXiv)

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.