Datasets:
wisesight_sentiment

	"""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}