Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
Thai
Size:
10K - 100K
License:
File size: 5,085 Bytes
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"""Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""
import json
import os
import datasets
from datasets.tasks import TextClassification
_CITATION = """\
@software{bact_2019_3457447,
author = {Suriyawongkul, Arthit and
Chuangsuwanich, Ekapol and
Chormai, Pattarawat and
Polpanumas, Charin},
title = {PyThaiNLP/wisesight-sentiment: First release},
month = sep,
year = 2019,
publisher = {Zenodo},
version = {v1.0},
doi = {10.5281/zenodo.3457447},
url = {https://doi.org/10.5281/zenodo.3457447}
}
"""
_DESCRIPTION = """\
Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)
* Released to public domain under Creative Commons Zero v1.0 Universal license.
* Category (Labels): {"pos": 0, "neu": 1, "neg": 2, "q": 3}
* Size: 26,737 messages
* Language: Central Thai
* Style: Informal and conversational. With some news headlines and advertisement.
* Time period: Around 2016 to early 2019. With small amount from other period.
* Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
* Privacy:
* Only messages that made available to the public on the internet (websites, blogs, social network sites).
* For Facebook, this means the public comments (everyone can see) that made on a public page.
* Private/protected messages and messages in groups, chat, and inbox are not included.
* Alternations and modifications:
* Keep in mind that this corpus does not statistically represent anything in the language register.
* Large amount of messages are not in their original form. Personal data are removed or masked.
* Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact.
(Mis)spellings are kept intact.
* Messages longer than 2,000 characters are removed.
* Long non-Thai messages are removed. Duplicated message (exact match) are removed.
* More characteristics of the data can be explore: https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb
"""
class WisesightSentimentConfig(datasets.BuilderConfig):
"""BuilderConfig for WisesightSentiment."""
def __init__(self, **kwargs):
"""BuilderConfig for WisesightSentiment.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(WisesightSentimentConfig, self).__init__(**kwargs)
class WisesightSentiment(datasets.GeneratorBasedBuilder):
"""Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""
_DOWNLOAD_URL = "https://github.com/PyThaiNLP/wisesight-sentiment/raw/master/huggingface/data.zip"
_TRAIN_FILE = "train.jsonl"
_VAL_FILE = "valid.jsonl"
_TEST_FILE = "test.jsonl"
BUILDER_CONFIGS = [
WisesightSentimentConfig(
name="wisesight_sentiment",
version=datasets.Version("1.0.0"),
description="Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)",
),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"texts": datasets.Value("string"),
"category": datasets.features.ClassLabel(names=["pos", "neu", "neg", "q"]),
}
),
supervised_keys=None,
homepage="https://github.com/PyThaiNLP/wisesight-sentiment",
citation=_CITATION,
task_templates=[TextClassification(text_column="texts", label_column="category")],
)
def _split_generators(self, dl_manager):
arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL)
data_dir = os.path.join(arch_path, "data")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FILE)},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(data_dir, self._VAL_FILE)},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FILE)},
),
]
def _generate_examples(self, filepath):
"""Generate WisesightSentiment examples."""
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
texts = data["texts"]
category = data["category"]
yield id_, {"texts": texts, "category": category}
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