Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K - 100K
License:
# 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), | |
} | |