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from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from nusacrowd.utils import schemas
from nusacrowd.utils.configs import NusantaraConfig
from nusacrowd.utils.constants import Tasks
_CITATION = """\
@article{karo2022sentiment,
title={Sentiment Analysis in Karonese Tweet using Machine Learning},
author={Karo, Ichwanul Muslim Karo and Fudzee, Mohd Farhan Md and Kasim, Shahreen and Ramli, Azizul Azhar},
journal={Indonesian Journal of Electrical Engineering and Informatics (IJEEI)},
volume={10},
number={1},
pages={219--231},
year={2022}
}
"""
_LANGUAGES = ["btx"]
_LOCAL = False
_DATASETNAME = "karonese_sentiment"
_DESCRIPTION = """\
Karonese sentiment was crawled from Twitter between 1 January 2021 and 31 October 2021.
The first crawling process used several keywords related to the Karonese, such as
"deleng sinabung, Sinabung mountain", "mejuah-juah, greeting welcome", "Gundaling",
and so on. However, due to the insufficient number of tweets obtained using such
keywords, a second crawling process was done based on several hashtags, such as
#kalakkaro, # #antonyginting, and #lyodra.
"""
_HOMEPAGE = "http://section.iaesonline.com/index.php/IJEEI/article/view/3565"
_LICENSE = "Unknown"
_URLS = {
_DATASETNAME: "https://raw.githubusercontent.com/aliakbars/karonese/main/karonese_sentiment.csv",
}
_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
_SOURCE_VERSION = "1.0.0"
_NUSANTARA_VERSION = "1.0.0"
class KaroneseSentimentDataset(datasets.GeneratorBasedBuilder):
"""Karonese sentiment was crawled from Twitter between 1 January 2021 and 31 October 2021.
The dataset consists of 397 negative, 342 neutral, and 260 positive tweets.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
BUILDER_CONFIGS = [
NusantaraConfig(
name="karonese_sentiment_source",
version=SOURCE_VERSION,
description="Karonese Sentiment source schema",
schema="source",
subset_id="karonese_sentiment",
),
NusantaraConfig(
name="karonese_sentiment_nusantara_text",
version=NUSANTARA_VERSION,
description="Karonese Sentiment Nusantara schema",
schema="nusantara_text",
subset_id="karonese_sentiment",
),
]
DEFAULT_CONFIG_NAME = "sentiment_nathasa_review_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"no": datasets.Value("string"),
"tweet": datasets.Value("string"),
"label": datasets.Value("string"),
}
)
elif self.config.schema == "nusantara_text":
features = schemas.text_features(["negative", "neutral", "positive"])
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
# Dataset does not have predetermined split, putting all as TRAIN
data_dir = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_dir,
},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
df = pd.read_csv(filepath).drop("no", axis=1)
df.columns = ["text", "label"]
if self.config.schema == "source":
for idx, row in df.iterrows():
example = {
"no": str(idx+1),
"tweet": row.text,
"label": row.label,
}
yield idx, example
elif self.config.schema == "nusantara_text":
for idx, row in df.iterrows():
example = {
"id": str(idx+1),
"text": row.text,
"label": row.label,
}
yield idx, example
else:
raise ValueError(f"Invalid config: {self.config.name}")
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