visolex/UIT-VSMEC

Dataset Card for UIT‑VSMEC

1. Dataset Summary

UIT‑VSMEC (Vietnamese Social Media Emotion Corpus) is a benchmark corpus for emotion recognition in Vietnamese social media comments. It consists of 6,927 human‐annotated sentences, each labeled with one of six basic emotions, plus an Other category.

2. Supported Tasks and Leaderboard

• Primary Task: Text classification – emotion recognition
• Metrics: Accuracy, F1‑score

No public leaderboard yet; contributions welcome!

3. Languages

• Vietnamese

4. Data Fields

• Sentence (str): The raw social media comment.
• Emotion (str): Emotion category.
• type (str): Split name.
• index (int): The index of sentence.

5. Emotion Labels

Label	Description
Enjoyment	Joy, happiness, or pleasure.
Sadness	Grief, sorrow, or unhappiness.
Anger	Annoyance or hostility.
Fear	Anxiety or apprehension.
Disgust	Revulsion or aversion.
Surprise	Shock or amazement.
Other	None of the above.

6. Usage

from datasets import load_dataset

ds = load_dataset("visolex/UIT-VSMEC")

train_ds = ds.filter(lambda x: x["type"] == "train")
dev_ds   = ds.filter(lambda x: x["type"] == "dev")
test_ds  = ds.filter(lambda x: x["type"] == "test")

print(train_ds[0])

7. Dataset Creation

1. Source Data: Original splits were downloaded from a Google Drive folder.
2. Merge Process: Combined train/dev/test CSVs into one file; added a type column.
3. Preprocessing: Minimal cleaning—only normalized whitespace; preserved original labels.

8. Source & Links

• Original Google Drive (raw CSVs): https://drive.google.com/drive/folders/1HooABJyrddVGzll7fgkJ6VzkG_XuWfRu

9. Licenses and Citation

License

Please refer to the original dataset license (if unspecified, assume CC BY 4.0).

How to Cite

Original Paper

@InProceedings{10.1007/978-981-15-6168-9_27,
  author    = {Ho, Vong Anh and Nguyen, Duong Huynh-Cong and Nguyen, Danh Hoang and Pham, Linh Thi-Van
               and Nguyen, Duc-Vu and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy},
  title     = {Emotion Recognition for Vietnamese Social Media Text},
  booktitle = {Computational Linguistics},
  year      = {2020},
  publisher = {Springer Singapore},
  pages     = {319--333},
  isbn      = {978-981-15-6168-9}
}