Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
sentiment-analysis
Languages:
Chinese
Size:
1K - 10K
Tags:
stance-detection
License:
File size: 3,347 Bytes
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# Copyright 2022 Mads Kongsbak and Leon Derczynski
#
# 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.
"""NLPCC Shared Task 4, Stance Detection in Chinese Microblogs (Task A)"""
import csv
import json
import os
import datasets
_CITATION = """\
@incollection{xu2016overview,
title={Overview of nlpcc shared task 4: Stance detection in chinese microblogs},
author={Xu, Ruifeng and Zhou, Yu and Wu, Dongyin and Gui, Lin and Du, Jiachen and Xue, Yun},
booktitle={Natural language understanding and intelligent applications},
pages={907--916},
year={2016},
publisher={Springer}
}
"""
_DESCRIPTION = """\
This is a stance prediction dataset in Chinese.
The data is that from a shared task, stance detection in Chinese microblogs, in NLPCC-ICCPOL 2016. It covers Task A, a mandatory supervised task which detects stance towards five targets of interest with given labeled data.
"""
_HOMEPAGE = ""
_LICENSE = "cc-by-4.0"
class NLPCCConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(NLPCCConfig, self).__init__(**kwargs)
class NLPCCStance(datasets.GeneratorBasedBuilder):
"""The NLPCC Shared Task 4 dataset regarding Stance Detection in Chinese Microblogs (Task A)"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
NLPCCConfig(name="task_a", version=VERSION, description="Task A, the supervised learning task."),
]
def _info(self):
features = datasets.Features(
{
"id": datasets.Value("string"),
"target": datasets.Value("string"),
"text": datasets.Value("string"),
"stance": datasets.features.ClassLabel(
names=[
"AGAINST",
"FAVOR",
"NONE",
]
)
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_text = dl_manager.download_and_extract("taska_train.csv")
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_text, "split": "train"}),
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter=",")
guid = 0
for instance in reader:
instance["target"] = instance.pop("target")
instance["text"] = instance.pop("text")
instance["stance"] = instance.pop("stance")
instance['id'] = str(guid)
yield guid, instance
guid += 1 |