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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
_CITATION = """
@INPROCEEDINGS{8629181,
author={Ilmania, Arfinda and Abdurrahman and Cahyawijaya, Samuel and Purwarianti, Ayu},
booktitle={2018 International Conference on Asian Language Processing (IALP)},
title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-Based Sentiment Analysis},
year={2018},
volume={},
number={},
pages={62-67},
doi={10.1109/IALP.2018.8629181
}
"""
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_LOCAL = False
_DATASETNAME = "casa"
_DESCRIPTION = """
CASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms (Ilmania et al., 2018).
The dataset covers six aspects of car quality.
We define the task to be a multi-label classification task,
where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral.
"""
_HOMEPAGE = "https://github.com/IndoNLP/indonlu"
_LICENSE = "CC-BY-SA 4.0"
_URLS = {
"train": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/train_preprocess.csv",
"validation": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/valid_preprocess.csv",
"test": "https://raw.githubusercontent.com/IndoNLP/indonlu/master/dataset/casa_absa-prosa/test_preprocess.csv",
}
_SUPPORTED_TASKS = [Tasks.ASPECT_BASED_SENTIMENT_ANALYSIS]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class CASA(datasets.GeneratorBasedBuilder):
"""CASA is an aspect based sentiment analysis dataset"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name="casa_source",
version=SOURCE_VERSION,
description="CASA source schema",
schema="source",
subset_id="casa",
),
SEACrowdConfig(
name="casa_seacrowd_text_multi",
version=SEACROWD_VERSION,
description="CASA Nusantara schema",
schema="seacrowd_text_multi",
subset_id="casa",
),
]
DEFAULT_CONFIG_NAME = "casa_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"index": datasets.Value("int64"),
"sentence": datasets.Value("string"),
"fuel": datasets.Value("string"),
"machine": datasets.Value("string"),
"others": datasets.Value("string"),
"part": datasets.Value("string"),
"price": datasets.Value("string"),
"service": datasets.Value("string"),
}
)
elif self.config.schema == "seacrowd_text_multi":
features = schemas.text_multi_features(["positive", "neutral", "negative"])
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
train_csv_path = Path(dl_manager.download_and_extract(_URLS["train"]))
validation_csv_path = Path(dl_manager.download_and_extract(_URLS["validation"]))
test_csv_path = Path(dl_manager.download_and_extract(_URLS["test"]))
data_dir = {
"train": train_csv_path,
"validation": validation_csv_path,
"test": test_csv_path,
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir["train"],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": data_dir["test"],
"split": "test",
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": data_dir["validation"],
"split": "dev",
},
),
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_csv(filepath, sep=",", header="infer").reset_index()
if self.config.schema == "source":
for row in df.itertuples():
entry = {"index": row.index, "sentence": row.sentence, "fuel": row.fuel, "machine": row.machine, "others": row.others, "part": row.part, "price": row.price, "service": row.service}
yield row.index, entry
elif self.config.schema == "seacrowd_text_multi":
for row in df.itertuples():
entry = {
"id": str(row.index),
"text": row.sentence,
"labels": [label for label in row[3:]],
}
yield row.index, entry
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