Datasets:
Tasks:
Text Classification
Multilinguality:
multilingual
Size Categories:
100K<n<1M
Language Creators:
crowdsourced
Annotations Creators:
machine-generated
Source Datasets:
original
Tags:
License:
# 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. | |
"""DUTCH SOCIAL: Annotated Covid19 tweets in Dutch language (sentiment, industry codes & province).""" | |
import json | |
import os | |
import datasets | |
# TODO: Add BibTeX citation | |
# Find for instance the citation on arxiv or on the dataset repo/website | |
_CITATION = """\ | |
@data{FK2/MTPTL7_2020, | |
author = {Gupta, Aakash}, | |
publisher = {COVID-19 Data Hub}, | |
title = {{Dutch social media collection}}, | |
year = {2020}, | |
version = {DRAFT VERSION}, | |
doi = {10.5072/FK2/MTPTL7}, | |
url = {https://doi.org/10.5072/FK2/MTPTL7} | |
} | |
""" | |
# TODO: Add description of the dataset here | |
# You can copy an official description | |
_DESCRIPTION = """\ | |
The dataset contains around 271,342 tweets. The tweets are filtered via the official Twitter API to | |
contain tweets in Dutch language or by users who have specified their location information within Netherlands | |
geographical boundaries. Using natural language processing we have classified the tweets for their HISCO codes. | |
If the user has provided their location within Dutch boundaries, we have also classified them to their respective | |
provinces The objective of this dataset is to make research data available publicly in a FAIR (Findable, Accessible, | |
Interoperable, Reusable) way. Twitter's Terms of Service Licensed under Attribution-NonCommercial 4.0 International | |
(CC BY-NC 4.0) (2020-10-27) | |
""" | |
# TODO: Add a link to an official homepage for the dataset here | |
_HOMEPAGE = "http://datasets.coronawhy.org/dataset.xhtml?persistentId=doi:10.5072/FK2/MTPTL7" | |
# TODO: Add the licence for the dataset here if you can find it | |
_LICENSE = "CC BY-NC 4.0" | |
# TODO: Add link to the official dataset URLs here | |
# The HuggingFace dataset library don't host the datasets but only point to the original files | |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
_URLs = {"dutch_social": "https://storage.googleapis.com/corona-tweet/dutch-tweets.zip"} | |
_LANG = ["nl", "en"] | |
# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case | |
class DutchSocial(datasets.GeneratorBasedBuilder): | |
""" | |
Annotated Covid19 tweets in Dutch language. The tweets were filtered for users who had indicated | |
their location within Netherlands or if the tweets were in Dutch language. The purpose of curating | |
these tweets is to measure the economic impact of the Covid19 pandemic | |
""" | |
VERSION = datasets.Version("1.1.0") | |
# This is an example of a dataset with multiple configurations. | |
# If you don't want/need to define several sub-sets in your dataset, | |
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
# If you need to make complex sub-parts in the datasets with configurable options | |
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
# BUILDER_CONFIG_CLASS = MyBuilderConfig | |
# You will be able to load one or the other configurations in the following list with | |
# data = datasets.load_dataset('my_dataset', 'first_domain') | |
# data = datasets.load_dataset('my_dataset', 'second_domain') | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="dutch_social", | |
version=VERSION, | |
description="This part of my dataset provides config for the entire dataset", | |
) | |
# datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), | |
] | |
def _info(self): | |
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
features = datasets.Features( | |
{ | |
"full_text": datasets.Value("string"), | |
"text_translation": datasets.Value("string"), | |
"screen_name": datasets.Value("string"), | |
"description": datasets.Value("string"), | |
"desc_translation": datasets.Value("string"), | |
"location": datasets.Value("string"), | |
"weekofyear": datasets.Value("int64"), | |
"weekday": datasets.Value("int64"), | |
"month": datasets.Value("int64"), | |
"year": datasets.Value("int64"), | |
"day": datasets.Value("int64"), | |
"point_info": datasets.Value("string"), | |
"point": datasets.Value("string"), | |
"latitude": datasets.Value("float64"), | |
"longitude": datasets.Value("float64"), | |
"altitude": datasets.Value("float64"), | |
"province": datasets.Value("string"), | |
"hisco_standard": datasets.Value("string"), | |
"hisco_code": datasets.Value("string"), | |
"industry": datasets.Value("bool_"), | |
"sentiment_pattern": datasets.Value("float64"), | |
"subjective_pattern": datasets.Value("float64"), | |
"label": datasets.ClassLabel(num_classes=3, names=["neg", "neu", "pos"], names_file=None, id=None), | |
} | |
) | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# This defines the different columns of the dataset and their types | |
features=features, # Here we define them above because they are different between the two configurations | |
# If there's a common (input, target) tuple from the features, | |
# specify them here. They'll be used if as_supervised=True in | |
# builder.as_dataset. | |
supervised_keys=None, | |
# Homepage of the dataset for documentation | |
homepage=_HOMEPAGE, | |
# License for the dataset if available | |
license=_LICENSE, | |
# Citation for the dataset | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs | |
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
my_urls = _URLs[self.config.name] | |
data_dir = dl_manager.download_and_extract(my_urls) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "train.jsonl"), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl"), "split": "test"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={ | |
"filepath": os.path.join(data_dir, "dev.jsonl"), | |
"split": "dev", | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split, key=None): | |
"""Yields examples.""" | |
# TODO: This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method. | |
# It is in charge of opening the given file and yielding (key, example) tuples from the dataset | |
# The key is not important, it's more here for legacy reason (legacy from tfds) | |
with open(filepath, encoding="utf-8") as f: | |
for id_, data in enumerate(f): | |
data = json.loads(data) | |
yield id_, { | |
"full_text": "" if not isinstance(data["full_text"], str) else data["full_text"], | |
"text_translation": "" | |
if not isinstance(data["text_translation"], str) | |
else data["text_translation"], | |
"screen_name": "" if not isinstance(data["screen_name"], str) else data["screen_name"], | |
"description": "" if not isinstance(data["description"], str) else data["description"], | |
"desc_translation": "" | |
if not isinstance(data["desc_translation"], str) | |
else data["desc_translation"], | |
"location": "" if not isinstance(data["location"], str) else data["location"], | |
"weekofyear": -1 if data["weekofyear"] is None else data["weekofyear"], | |
"weekday": -1 if data["weekday"] is None else data["weekday"], | |
"month": -1 if data["month"] is None else data["month"], | |
"year": -1 if data["year"] is None else data["year"], | |
"day": -1 if data["day"] is None else data["day"], | |
"point_info": "" if isinstance(data["point_info"], str) else data["point_info"], | |
"point": "" if not isinstance(data["point"], str) else data["point"], | |
"latitude": -1 if data["latitude"] is None else data["latitude"], | |
"longitude": -1 if data["longitude"] is None else data["longitude"], | |
"altitude": -1 if data["altitude"] is None else data["altitude"], | |
"province": "" if not isinstance(data["province"], str) else data["province"], | |
"hisco_standard": "" if not isinstance(data["hisco_standard"], str) else data["hisco_standard"], | |
"hisco_code": "" if not isinstance(data["hisco_code"], str) else data["hisco_code"], | |
"industry": False if not isinstance(data["industry"], bool) else data["industry"], | |
"sentiment_pattern": -100 if data["sentiment_pattern"] is None else data["sentiment_pattern"], | |
"subjective_pattern": -1 if data["subjective_pattern"] is None else data["subjective_pattern"], | |
"label": data["label"], | |
} | |