Datasets:
oclar

	# 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
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	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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	"""Opinion Corpus for Lebanese Arabic Reviews (OCLAR) Data Set"""


	import csv

	import datasets


	_CITATION = """
	@misc{Dua:2019 ,
	author = "Dua, Dheeru and Graff, Casey",
	year = "2017",
	title = "{UCI} Machine Learning Repository",
	url = "http://archive.ics.uci.edu/ml",
	institution = "University of California, Irvine, School of Information and Computer Sciences" }

	@InProceedings{AlOmari2019oclar,
	title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},
	authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},
	year={2019}
	}
	"""

	_DESCRIPTION = """\
	The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato
	website (https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops,
	etc.The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers
	rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about
	451 texts.
	"""

	_HOMEPAGE = "http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#"

	# TODO: Add the licence for the dataset here if you can find it
	_LICENSE = ""

	_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/00499/OCLAR%20-%20Opinion%20Corpus%20for%20Lebanese%20Arabic%20Reviews.csv"


	class Oclar(datasets.GeneratorBasedBuilder):
	"""TOpinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on
	services reviews, including hotels, restaurants, shops, and others.
	"""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"pagename": datasets.Value("string"),
	"review": datasets.Value("string"),
	"rating": datasets.Value("int8"),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

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

	def _generate_examples(self, filepath, split):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
	next(csv_reader, None) # skipping headers
	for id_, row in enumerate(csv_reader):
	pagename, review, rating = row
	rating = int(rating)
	yield id_, {"pagename": pagename, "review": review, "rating": rating}