In the field of education, understanding students' opinions through their comments is crucial, especially in the Vietnamese language, where resources remain limited. Existing educational datasets often lack domain relevance and student slang. To address these gaps, we introduce NEU-ESC, a new Vietnamese dataset for Educational Sentiment Classification and Topic Classification, curated from university forums, which offers more samples, richer class diversity, longer texts, and broader vocabulary. In addition, we explore multitask learning using encoder-only language models (BERT), in which we showed that it achieves performance up to 83.7% and 79.8% accuracy for sentiment and topic classification tasks. We also benchmark our dataset and model with other datasets and models, including Large Language Models, and discuss these benchmarks. The dataset is publicly available at: https://huggingface.co/datasets/hung20gg/NEU-ESC.
@article{arxiv.2506.23524,
title = {NEU-ESC: A Comprehensive Vietnamese dataset for Educational Sentiment analysis and topic Classification toward multitask learning},
author = {Phan Quoc Hung Mai and Quang Hung Nguyen and Phuong Giang Duong and Hong Hanh Nguyen and Nguyen Tuan Long},
journal= {arXiv preprint arXiv:2506.23524},
year = {2025}
}