Datasets:
metadata
license: apache-2.0
task_categories:
- text-classification
language:
- vi
tags:
- sentiment-analysis
- topic-classification
- education
- NLP
NEU-ESC: NEU Dataset for Educational Sentiment Analysis and Topic Classification
Overview
NEU-ESC is a dataset designed for Educational Sentiment Analysis and Topic Classification. Each sentence in the dataset is labeled with two attributes:
- Sentiment Analysis: Indicates the sentiment of the text.
- Topic Classification: Categorizes the content into predefined educational topics.
The dataset is collected from online forums, educational social network fan pages, and groups. It has been preprocessed and cleaned to ensure high-quality textual data.
Dataset Statistics
Sentiment Analysis Labels
| Label | Sentiment | Count | Percentage | Mean Length |
|---|---|---|---|---|
| 0 | Neutral | 22,773 | 69.08% | 23.21 |
| 1 | Positive | 4,148 | 12.58% | 24.29 |
| 2 | Negative | 5,250 | 15.77% | 34.30 |
| 3 | Toxic | 845 | 2.56% | 22.78 |
Topic Classification Labels
| Label | Classification | Count | Percentage | Mean Length |
|---|---|---|---|---|
| 0 | Spam | 405 | 1.23% | 22.71 |
| 1 | News | 902 | 2.74% | 59.55 |
| 2 | Academic | 10,512 | 31.89% | 29.62 |
| 3 | Other | 14,402 | 43.69% | 11.40 |
| 4 | Service | 2,358 | 7.15% | 30.94 |
| 5 | Jobs & Recruitment | 808 | 2.45% | 55.14 |
| 6 | Personal Affairs | 1,478 | 4.48% | 33.17 |
| 7 | Social Affairs | 769 | 2.33% | 67.11 |
| 8 | Help & Share | 670 | 2.03% | 37.03 |
| 9 | Club & Events | 662 | 2.01% | 68.82 |
Dataset Format
Each sample in the dataset contains:
- Text: The input sentence.
- Sentiment: One of the four sentiment classes (Neutral, Positive, Negative, Toxic).
- Classification: One of the ten topic categories.
Usage
The dataset is useful for:
- Training and evaluating sentiment analysis models in the educational domain.
- Building topic classification models for educational discussions.
- Understanding user engagement in online educational communities.
License
This dataset is released under an open-source license for research and educational purposes. Please ensure proper citation when using it in your work.
Citation
If you use NEU-ESC in your research, please cite:
@misc{neu_esc,
author = {Nguyen Quang Hung, Mai Phan Quoc Hung, Nguyen Thi Hong Hanh, Duong Phuong Giang},
title = {NEU-ESC: NEU Dataset for Educational Sentiment Analysis and Topic Classification},
year = {2024},
howpublished = {Hugging Face},
url = {https://huggingface.co/datasets/hung20gg/NEU-ESC}
}
Contact
For any inquiries or issues regarding the dataset, please reach out via Hugging Face discussions or GitHub Issues.