Datasetpublic

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… See the full description on the dataset page: https://huggingface.co/datasets/visolex/VLSP2018-ABSA-Restaurant.

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Dataset Card

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

python

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])

Citation

bibtex

@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}}
}