Datasets:
emotone_ar

	# 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.
	"""Dataset of 10065 tweets in Arabic for Emotion detection in Arabic text """


	import csv

	import datasets
	from datasets.tasks import TextClassification


	_CITATION = """\
	@inbook{inbook,
	author = {Al-Khatib, Amr and El-Beltagy, Samhaa},
	year = {2018},
	month = {01},
	pages = {105-114},
	title = {Emotional Tone Detection in Arabic Tweets: 18th International Conference, CICLing 2017, Budapest, Hungary, April 17–23, 2017, Revised Selected Papers, Part II},
	isbn = {978-3-319-77115-1},
	doi = {10.1007/978-3-319-77116-8_8}
	}
	"""


	_DESCRIPTION = """\
	Dataset of 10065 tweets in Arabic for Emotion detection in Arabic text"""


	_HOMEPAGE = "https://github.com/AmrMehasseb/Emotional-Tone"


	_DOWNLOAD_URL = "https://raw.githubusercontent.com/AmrMehasseb/Emotional-Tone/master/Emotional-Tone-Dataset.csv"


	class EmotoneAr(datasets.GeneratorBasedBuilder):
	"""Dataset of 10065 tweets in Arabic for Emotions detection in Arabic text"""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"tweet": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=["none", "anger", "joy", "sadness", "love", "sympathy", "surprise", "fear"]
	),
	}
	),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="tweet", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_DOWNLOAD_URL)
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir})]

	def _generate_examples(self, filepath):
	"""Generate labeled arabic tweets examples for emoptions detection."""
	with open(filepath, encoding="utf-8", mode="r") as csv_file:
	next(csv_file) # skip header
	csv_reader = csv.reader(csv_file, quotechar='"', delimiter=",")

	for id_, row in enumerate(csv_reader):
	_, tweet, label = row
	yield id_, {"tweet": tweet, "label": label}