Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Source Datasets:
original
ArXiv:
License:
File size: 3,483 Bytes
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# 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
#
# 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.
"""An annotated dataset for hate speech and offensive language detection on tweets."""
import csv
import datasets
_CITATION = """\
@inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {2017},
location = {Montreal, Canada},
pages = {512-515}
}
"""
_DESCRIPTION = """\
An annotated dataset for hate speech and offensive language detection on tweets.
"""
_HOMEPAGE = "https://github.com/t-davidson/hate-speech-and-offensive-language"
_LICENSE = "MIT"
_URL = "https://raw.githubusercontent.com/t-davidson/hate-speech-and-offensive-language/master/data/labeled_data.csv"
_CLASS_MAP = {
"0": "hate speech",
"1": "offensive language",
"2": "neither",
}
class HateSpeechOffensive(datasets.GeneratorBasedBuilder):
"""An annotated dataset for hate speech and offensive language detection on tweets."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"count": datasets.Value("int64"),
"hate_speech_count": datasets.Value("int64"),
"offensive_language_count": datasets.Value("int64"),
"neither_count": datasets.Value("int64"),
"class": datasets.ClassLabel(names=["hate speech", "offensive language", "neither"]),
"tweet": datasets.Value("string"),
}
),
supervised_keys=("tweet", "class"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_file = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_file,
},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
reader = csv.reader(f)
for id_, row in enumerate(reader):
if id_ == 0:
continue
yield id_, {
"count": row[1],
"hate_speech_count": row[2],
"offensive_language_count": row[3],
"neither_count": row[4],
"class": _CLASS_MAP[row[5]],
"tweet": row[6],
}
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