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# 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.
"""SentiWS: German-language resource for sentiment analysis, pos-tagging"""
import os
import datasets
_CITATION = """\
@INPROCEEDINGS{remquahey2010,
title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis},
booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)},
author = {Remus, R. and Quasthoff, U. and Heyer, G.},
year = {2010}
}
"""
_DESCRIPTION = """\
SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, and pos-tagging. The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1].
"""
_HOMEPAGE = ""
_LICENSE = "Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License"
_URLs = ["https://pcai056.informatik.uni-leipzig.de/downloads/etc/SentiWS/SentiWS_v2.0.zip"]
class SentiWS(datasets.GeneratorBasedBuilder):
"""SentiWS: German-language resource for sentiment analysis, pos-tagging"""
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="pos-tagging", version=VERSION, description="This covers pos-tagging task"),
datasets.BuilderConfig(
name="sentiment-scoring",
version=VERSION,
description="This covers the sentiment-scoring in [-1, 1] corresponding to (negative, positive) sentiment",
),
]
DEFAULT_CONFIG_NAME = "pos-tagging"
def _info(self):
if (
self.config.name == "pos-tagging"
): # the pos-tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"]
features = datasets.Features(
{
"word": datasets.Value("string"),
"pos-tag": datasets.ClassLabel(names=["NN", "VVINF", "ADJX", "ADV"]),
}
)
else: # This is an example to show how to have different features for "first_domain" and "second_domain"
features = datasets.Features(
{
"word": datasets.Value("string"),
"sentiment-score": datasets.Value("float32"),
}
)
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
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={
"sourcefiles": [
os.path.join(data_dir[0], f)
for f in ["SentiWS_v2.0_Positive.txt", "SentiWS_v2.0_Negative.txt"]
],
"split": "train",
},
),
]
def _generate_examples(self, sourcefiles, split):
"""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)
for file_idx, filepath in enumerate(sourcefiles):
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
word = row.split("|")[0]
if self.config.name == "pos-tagging":
tag = row.split("|")[1].split("\t")[0]
yield f"{file_idx}_{id_}", {"word": word, "pos-tag": tag}
else:
sentiscore = row.split("|")[1].split("\t")[1]
yield f"{file_idx}_{id_}", {"word": word, "sentiment-score": float(sentiscore)}
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