Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
File size: 3,067 Bytes
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# 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
"""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),
}
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