visolex/VLSP2018-ABSA-Restaurant

VLSP2018-ABSA-Restaurant

Dataset Summary

The VLSP 2018 Restaurant corpus targets the same ACSA sub-tasks (ACD & SPC) on 4,751 Vietnamese restaurant reviews. This unified CSV includes:

• 12 aspect–category indicator columns, each with values {0, 1, 2, 3}.
• A type column for train/dev/test.
• A dataset column fixed to VLSP2018-ABSA-Restaurant.

Supported Tasks and Metrics

• Aspect Category Detection
• Sentiment Polarity Classification
• Metrics: Precision, Recall, F1

Languages

• Vietnamese

Dataset Structure

Column	Type	Description
Review	str	The raw restaurant review.
<Aspect#Category> (12 cols)	int	Polarity indicator (0=absent, 1=positive, 2=negative, 3=neutral).
type	str	Split: train / validation / test.
dataset	str	Always VLSP2018-ABSA-Restaurant.

The 12 aspect–category columns:

AMBIENCE#GENERAL, DRINKS#PRICES, …, SERVICE#GENERAL

Usage

from datasets import load_dataset

ds = load_dataset("visolex/vlsp2018-absa-restaurant")

train = ds.filter(lambda ex: ex["type"] == "train")
val   = ds.filter(lambda ex: ex["type"] == "dev")
test  = ds.filter(lambda ex: ex["type"] == "test")

print(train.features)

Source & Links

• GitHub: https://github.com/ds4v/absa-vlsp-2018
• Publication: End-to-end Multi-task Solutions for ACSA on Vietnamese (VLSP 2018) ([github.com][1])

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Citation

@INPROCEEDINGS{9865479,
  author={Dang, Hoang-Quan and Nguyen, Duc-Duy-Anh and Do, Trong-Hop},
  booktitle={2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom)}, 
  title={Multi-task Solution for Aspect Category Sentiment Analysis on Vietnamese Datasets}, 
  year={2022},
  volume={},
  number={},
  pages={404-409},
  keywords={Sentiment analysis;Analytical models;Computational modeling;Multitasking;Task analysis;Cybernetics;Computational intelligence;Aspect-based Sentiment Analysis;PhoBERT;Aspect Category Detection;Sentiment Polarity Classification},
  doi={10.1109/CyberneticsCom55287.2022.9865479}}
}