Datasets:

komari6
/

ajgt_twitter_ar

	# coding=utf-8
	# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# 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.

	# Lint as: python3
	"""Arabic Jordanian General Tweets."""


	import os

	import openpyxl # noqa: requires this pandas optional dependency for reading xlsx files
	import pandas as pd

	import datasets
	from datasets.tasks import TextClassification


	_DESCRIPTION = """\
	Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets \
	annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.
	"""

	_CITATION = """\
	@inproceedings{alomari2017arabic,
	title={Arabic tweets sentimental analysis using machine learning},
	author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
	booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
	pages={602--610},
	year={2017},
	organization={Springer}
	}
	"""

	_URL = "https://raw.githubusercontent.com/komari6/Arabic-twitter-corpus-AJGT/master/"


	class AjgtConfig(datasets.BuilderConfig):
	"""BuilderConfig for Ajgt."""

	def __init__(self, **kwargs):
	"""BuilderConfig for Ajgt.

	Args:
	**kwargs: keyword arguments forwarded to super.
	"""
	super(AjgtConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)


	class AjgtTwitterAr(datasets.GeneratorBasedBuilder):
	"""Ajgt dataset."""

	BUILDER_CONFIGS = [
	AjgtConfig(
	name="plain_text",
	description="Plain text",
	)
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"Negative",
	"Positive",
	]
	),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/komari6/Arabic-twitter-corpus-AJGT",
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	urls_to_download = {
	"train": os.path.join(_URL, "AJGT.xlsx"),
	}
	downloaded_files = dl_manager.download(urls_to_download)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
	]

	def _generate_examples(self, filepath):
	"""Generate examples."""
	with open(filepath, "rb") as f:
	df = pd.read_excel(f, engine="openpyxl")
	for id_, record in df.iterrows():
	tweet, sentiment = record["Feed"], record["Sentiment"]
	yield str(id_), {"text": tweet, "label": sentiment}