Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
German
Size:
10K<n<100K
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. | |
"""The “One Million Posts” corpus is an annotated data set consisting of | |
user comments posted to an Austrian newspaper website (in German language).""" | |
from pathlib import Path | |
import pandas as pd | |
import datasets | |
_CITATION = """\ | |
@InProceedings{Schabus2017, | |
Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp}, | |
Title = {One Million Posts: A Data Set of German Online Discussions}, | |
Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)}, | |
Pages = {1241--1244}, | |
Year = {2017}, | |
Address = {Tokyo, Japan}, | |
Doi = {10.1145/3077136.3080711}, | |
Month = aug | |
} | |
""" | |
_DESCRIPTION = """\ | |
The “One Million Posts” corpus is an annotated data set consisting of | |
user comments posted to an Austrian newspaper website (in German language). | |
DER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper’s website, | |
there is a discussion section below each news article where readers engage in | |
online discussions. The data set contains a selection of user posts from the | |
12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and | |
1,000,000 unlabeled posts in the data set. The labeled posts were annotated by | |
professional forum moderators employed by the newspaper. | |
The data set contains the following data for each post: | |
* Post ID | |
* Article ID | |
* Headline (max. 250 characters) | |
* Main Body (max. 750 characters) | |
* User ID (the user names used by the website have been re-mapped to new numeric IDs) | |
* Time stamp | |
* Parent post (replies give rise to tree-like discussion thread structures) | |
* Status (online or deleted by a moderator) | |
* Number of positive votes by other community members | |
* Number of negative votes by other community members | |
For each article, the data set contains the following data: | |
* Article ID | |
* Publishing date | |
* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1) | |
* Title | |
* Body | |
Detailed descriptions of the post selection and annotation procedures are given in the paper. | |
## Annotated Categories | |
Potentially undesirable content: | |
* Sentiment (negative/neutral/positive) | |
An important goal is to detect changes in the prevalent sentiment in a discussion, e.g., | |
the location within the fora and the point in time where a turn from positive/neutral | |
sentiment to negative sentiment takes place. | |
* Off-Topic (yes/no) | |
Posts which digress too far from the topic of the corresponding article. | |
* Inappropriate (yes/no) | |
Swearwords, suggestive and obscene language, insults, threats etc. | |
* Discriminating (yes/no) | |
Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content. | |
Neutral content that requires a reaction: | |
* Feedback (yes/no) | |
Sometimes users ask questions or give feedback to the author of the article or the | |
newspaper in general, which may require a reply/reaction. | |
Potentially desirable content: | |
* Personal Stories (yes/no) | |
In certain fora, users are encouraged to share their personal stories, experiences, | |
anecdotes etc. regarding the respective topic. | |
* Arguments Used (yes/no) | |
It is desirable for users to back their statements with rational argumentation, | |
reasoning and sources. | |
""" | |
_HOMEPAGE = "https://ofai.github.io/million-post-corpus/" | |
_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License" | |
_URLs = { | |
"posts_labeled": "https://github.com/aseifert/million-post-corpus/raw/master/data/posts_labeled.csv.xz", | |
"posts_unlabeled": "https://github.com/aseifert/million-post-corpus/raw/master/data/posts_unlabeled.csv.xz", | |
"articles": "https://github.com/aseifert/million-post-corpus/raw/master/data/articles.csv.xz", | |
} | |
class Omp(datasets.GeneratorBasedBuilder): | |
"""The “One Million Posts” corpus is an annotated data set consisting of user comments | |
posted to an Austrian newspaper website (in German language). Annotated categories include: | |
sentiment (negative/neutral/positive), off-topic (yes/no), inappropriate (yes/no), | |
discriminating (yes/no), feedback (yes/no), personal story (yes/no), arguments used (yes/no).""" | |
VERSION = datasets.Version("1.1.0") | |
BUILDER_CONFIGS = [ | |
datasets.BuilderConfig( | |
name="posts_labeled", | |
version=VERSION, | |
description="This part of the dataset includes labeled posts (11,773 annotated posts)", | |
), | |
datasets.BuilderConfig( | |
name="posts_unlabeled", | |
version=VERSION, | |
description="This part of the dataset includes unlabeled posts (1,000,000)", | |
), | |
datasets.BuilderConfig( | |
name="articles", | |
version=VERSION, | |
description="This part of the dataset includes the articles that the comments were posted to (~12k)", | |
), | |
] | |
DEFAULT_CONFIG_NAME = ( | |
"posts_labeled" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
) | |
def _info(self): | |
if self.config.name == "posts_labeled": | |
features = datasets.Features( | |
{ | |
"ID_Post": datasets.Value("string"), | |
"ID_Parent_Post": datasets.Value("string"), | |
"ID_Article": datasets.Value("string"), | |
"ID_User": datasets.Value("string"), | |
"CreatedAt": datasets.Value("string"), | |
"Status": datasets.Value("string"), | |
"Headline": datasets.Value("string"), | |
"Body": datasets.Value("string"), | |
"PositiveVotes": datasets.Value("int32"), | |
"NegativeVotes": datasets.Value("int32"), | |
"Category": datasets.features.ClassLabel( | |
names=[ | |
"ArgumentsUsed", | |
"Discriminating", | |
"Inappropriate", | |
"OffTopic", | |
"PersonalStories", | |
"PossiblyFeedback", | |
"SentimentNegative", | |
"SentimentNeutral", | |
"SentimentPositive", | |
] | |
), | |
"Value": datasets.Value("int32"), | |
"Fold": datasets.Value("int32"), | |
} | |
) | |
elif self.config.name == "posts_unlabeled": | |
features = datasets.Features( | |
{ | |
"ID_Post": datasets.Value("string"), | |
"ID_Parent_Post": datasets.Value("string"), | |
"ID_Article": datasets.Value("string"), | |
"ID_User": datasets.Value("string"), | |
"CreatedAt": datasets.Value("string"), | |
"Status": datasets.Value("string"), | |
"Headline": datasets.Value("string"), | |
"Body": datasets.Value("string"), | |
"PositiveVotes": datasets.Value("int32"), | |
"NegativeVotes": datasets.Value("int32"), | |
} | |
) | |
elif self.config.name == "articles": | |
features = datasets.Features( | |
{ | |
"ID_Article": datasets.Value("string"), | |
"Path": datasets.Value("string"), | |
"publishingDate": datasets.Value("string"), | |
"Title": datasets.Value("string"), | |
"Body": datasets.Value("string"), | |
} | |
) | |
else: | |
assert False | |
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.""" | |
# 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_path = Path(dl_manager.download_and_extract(my_urls)) | |
if data_path.is_dir(): | |
if self.config.name == "posts_labeled": | |
fname = "posts_labeled.csv.gz" | |
elif self.config.name == "posts_unlabeled": | |
fname = "posts_unlabeled.csv.gz" | |
elif self.config.name == "articles": | |
fname = "articles.csv.gz" | |
else: | |
assert False | |
data_path = data_path / fname | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
# These kwargs will be passed to _generate_examples | |
gen_kwargs={"filepath": str(data_path), "split": "train"}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
"""Yields examples.""" | |
if self.config.name in ["posts_labeled", "posts_unlabeled"]: | |
dtype = {"ID_Post": str, "ID_Parent_Post": str, "ID_Article": str, "ID_User": str} | |
elif self.config.name == "articles": | |
dtype = {"ID_Article": str, "Path": str, "publishingDate": str, "ID_User": str} | |
data = pd.read_csv(filepath, compression=None, dtype=dtype).fillna("") | |
for id_, row in data.iterrows(): | |
yield id_, row.to_dict() | |