Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Slovenian
Size:
100K - 1M
License:
File size: 4,081 Bytes
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"""SentiNews: Manually sentiment annotated Slovenian news corpus."""
import csv
import datasets
_CITATION = """\
@article{buvcar2018annotated,
title={Annotated news corpora and a lexicon for sentiment analysis in Slovene},
author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez},
journal={Language Resources and Evaluation},
volume={52},
number={3},
pages={895--919},
year={2018},
publisher={Springer}
}
"""
_DESCRIPTION = """\
SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their
sentiment by between 2 and 6 annotators. The news articles contain political, business, economic and financial content
from the Slovenian news portals 24ur, Dnevnik, Finance, Rtvslo, and Žurnal24. The texts were annotated using the
five-level Lickert scale (1 – very negative, 2 – negative, 3 – neutral, 4 – positive, and 5 – very positive) on three
levels of granularity, i.e. on the document, paragraph, and sentence level. The final sentiment is determined using
the following criterion: negative (if average of scores ≤ 2.4); neutral (if average of scores is between 2.4 and 3.6);
positive (average of annotated scores ≥ 3.6).
"""
_HOMEPAGE = "https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene/"
_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
_URLS = {
"document_level": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_document-level.txt",
"paragraph_level": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_paragraph-level.txt",
"sentence_level": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_sentence-level.txt"
}
class Sentinews(datasets.GeneratorBasedBuilder):
"""SentiNews: Manually sentiment annotated Slovenian news corpus. Version 1.0."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="document_level", version=VERSION, description="Dataset annotated at document level."),
datasets.BuilderConfig(name="paragraph_level", version=VERSION, description="Dataset annotated at paragraph level."),
datasets.BuilderConfig(name="sentence_level", version=VERSION, description="Dataset annotated at sentence level."),
]
def _info(self):
_config_features = {
"nid": datasets.Value("uint16"),
"content": datasets.Value("string"),
"sentiment": datasets.Value("string")
}
if self.config.name == "paragraph_level":
_config_features["pid"] = datasets.Value("uint8")
elif self.config.name == "sentence_level":
_config_features["pid"] = datasets.Value("uint8")
_config_features["sid"] = datasets.Value("uint8")
features = datasets.Features(_config_features)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=("content", "sentiment"),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_file = dl_manager.download_and_extract(urls)
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": data_file})]
def _generate_examples(self, data_file):
_keys_to_return = ["nid", "content", "sentiment"]
if self.config.name == "paragraph_level":
_keys_to_return.append("pid")
elif self.config.name == "sentence_level":
_keys_to_return.append("pid")
_keys_to_return.append("sid")
with open(data_file, encoding="utf-8") as f:
data = csv.DictReader(f, delimiter="\t")
for idx, row in enumerate(data):
yield idx, {_k: row[_k] for _k in _keys_to_return}
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