Datasets:
muchocine

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	import glob
	import os
	import re
	from xml.dom.minidom import parseString

	import datasets
	from datasets.tasks import TextClassification


	# no BibTeX citation
	_CITATION = ""

	_DESCRIPTION = """\
	The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language,
	each with a shorter summary review, and a rating on a 1-5 scale.
	"""

	_LICENSE = "CC-BY-2.1"

	_URLs = {"default": "http://www.lsi.us.es/~fermin/corpusCine.zip"}


	class Muchocine(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.1.1")

	def _info(self):
	features = datasets.Features(
	{
	"review_body": datasets.Value("string"),
	"review_summary": datasets.Value("string"),
	"star_rating": datasets.ClassLabel(names=[str(i) for i in range(1, 6)]),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage="http://www.lsi.us.es/~fermin/index.php/Datasets",
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[
	TextClassification(text_column="review_body", label_column="star_rating"),
	TextClassification(text_column="review_summary", label_column="star_rating"),
	],
	)

	def _split_generators(self, dl_manager):
	my_urls = _URLs[self.config.name]
	data_dir = dl_manager.download_and_extract(my_urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"filepaths": sorted(glob.glob(os.path.join(data_dir, "corpusCriticasCine", "*.xml"))),
	"split": "train",
	},
	),
	]

	def _generate_examples(self, filepaths, split):
	for filepath in filepaths:
	with open(filepath, encoding="latin-1") as f:
	id = re.search(r"\d+\.xml", filepath)[0][:-4]

	txt = f.read()
	txt = txt.replace("“", '"').replace("”", '"').replace("…", "")
	txt = txt.replace("‘", '"').replace("’", '"').replace("′", "")
	txt = txt.replace("à", "à").replace("–", "-").replace("è", "è")
	txt = txt.replace("ö", "ö").replace("ç", "ç").replace("&", "and")
	try:
	doc = parseString(txt)
	except Exception as e:
	# skip 6 malformed xml files, for example unescaped < and >
	_ = e
	continue

	btxt = ""
	review_bod = doc.getElementsByTagName("body")
	if len(review_bod) > 0:
	for node in review_bod[0].childNodes:
	if node.nodeType == node.TEXT_NODE:
	btxt += node.data + " "

	rtxt = ""
	review_summ = doc.getElementsByTagName("summary")
	if len(review_summ) > 0:
	for node in review_summ[0].childNodes:
	if node.nodeType == node.TEXT_NODE:
	rtxt += node.data + " "

	yield id, {
	"review_body": btxt,
	"review_summary": rtxt,
	"star_rating": doc.documentElement.attributes["rank"].value,
	}