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:
| # 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 | |