Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
Danish
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
other
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
File size: 2,744 Bytes
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# coding=utf-8
# Copyright 2020 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
import datasets
_DESCRIPTION = """\
The dataset consists of 9008 sentences that are labelled with fine-grained polarity in the range from -2 to 2 (negative to postive). The quality of the fine-grained is not cross validated and is therefore subject to uncertainties; however, the simple polarity has been cross validated and therefore is considered to be more correct.
"""
_HOMEPAGE_URL = "https://github.com/steffan267/Sentiment-Analysis-on-Danish-Social-Media"
_URL = (
"https://raw.githubusercontent.com/steffan267/Sentiment-Analysis-on-Danish-Social-Media/master/all_sentences.tsv"
)
_CITATION = "https://github.com/lucaspuvis/SAM/blob/master/Thesis.pdf"
class DanishPoliticalComments(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.9.1")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"sentence": datasets.Value("string"),
"target": datasets.features.ClassLabel(names=["2", "1", "0", "-1", "-2"]),
},
),
supervised_keys=None,
homepage=_HOMEPAGE_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"datapath": path},
)
]
def _generate_examples(self, datapath):
sentence_counter = 0
with open(datapath, encoding="utf-8") as f:
for row in f:
row = row.strip()
target, sentence = row.split("\t")
result = (
sentence_counter,
{
"id": str(sentence_counter),
"sentence": sentence,
"target": target,
},
)
sentence_counter += 1
yield result
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