Datasets:
tweets_hate_speech_detection

	# 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
	#
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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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	# limitations under the License.

	# Lint as: python3
	"""Detecing which tweets showcase hate or racist remarks."""

	from __future__ import absolute_import, division, print_function

	import csv

	import datasets


	_DESCRIPTION = """\
	The objective of this task is to detect hate speech in tweets. For the sake of simplicity, we say a tweet contains hate speech if it has a racist or sexist sentiment associated with it. So, the task is to classify racist or sexist tweets from other tweets.

	Formally, given a training sample of tweets and labels, where label ‘1’ denotes the tweet is racist/sexist and label ‘0’ denotes the tweet is not racist/sexist, your objective is to predict the labels on the given test dataset.
	"""

	_CITATION = """\
	@InProceedings{Z
	Roshan Sharma:dataset,
	title = {Sentimental Analysis of Tweets for Detecting Hate/Racist Speeches},
	authors={Roshan Sharma},
	year={2018}
	}
	"""

	_TRAIN_DOWNLOAD_URL = (
	"https://raw.githubusercontent.com/sharmaroshan/Twitter-Sentiment-Analysis/master/train_tweet.csv"
	)


	class TweetsHateSpeechDetection(datasets.GeneratorBasedBuilder):
	"""Detecing which tweets showcase hate or racist remarks."""

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"label": datasets.ClassLabel(names=["no-hate-speech", "hate-speech"]),
	"tweet": datasets.Value("string"),
	}
	),
	homepage="https://github.com/sharmaroshan/Twitter-Sentiment-Analysis",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
	]

	def _generate_examples(self, filepath):
	"""Generate Tweet examples."""
	with open(filepath, encoding="utf-8") as csv_file:
	csv_reader = csv.reader(
	csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
	)
	next(csv_reader, None)
	for id_, row in enumerate(csv_reader):
	row = row[1:]
	(label, tweet) = row

	yield id_, {
	"label": int(label),
	"tweet": (tweet),
	}