Datasets:

fthbrmnby
/

turkish_product_reviews

	"""Turkish Product Reviews"""


	import os

	import datasets
	from datasets.tasks import TextClassification


	logger = datasets.logging.get_logger(__name__)


	_CITATION = ""

	_DESCRIPTION = """
	Turkish Product Reviews.
	This repository contains 235.165 product reviews collected online. There are 220.284 positive, 14881 negative reviews.
	"""

	_URL = "https://github.com/fthbrmnby/turkish-text-data/raw/master/reviews.tar.gz"
	_FILES_PATHS = ["reviews.pos", "reviews.neg"]

	_HOMEPAGE = "https://github.com/fthbrmnby/turkish-text-data"


	class TurkishProductReviews(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"sentiment": datasets.ClassLabel(names=["negative", "positive"]),
	}
	),
	citation=_CITATION,
	homepage=_HOMEPAGE,
	task_templates=[TextClassification(text_column="sentence", label_column="sentiment")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	archive = dl_manager.download(_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive)}),
	]

	def _generate_examples(self, files):
	"""Generate TurkishProductReviews examples."""
	for file_idx, (path, f) in enumerate(files):
	_, file_extension = os.path.splitext(path)
	label = "negative" if file_extension == ".neg" else "positive"
	for idx, line in enumerate(f):
	line = line.decode("utf-8").strip()
	yield f"{file_idx}_{idx}", {
	"sentence": line,
	"sentiment": label,
	}