Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
File size: 7,924 Bytes
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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.
"""MultiBooked dataset."""
import os
import xml.etree.ElementTree as ET
from collections import defaultdict
from pathlib import Path
import datasets
_CITATION = """\
@inproceedings{Barnes2018multibooked,
author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
year = {2018},
month = {May},
date = {7-12},
address = {Miyazaki, Japan},
publisher = {European Language Resources Association (ELRA)},
language = {english}
}
"""
_DESCRIPTION = """\
MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.
The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project.
"""
_HOMEPAGE = "http://hdl.handle.net/10230/33928"
_LICENSE = "CC-BY 3.0"
_URL = "https://github.com/jerbarnes/multibooked/archive/master.zip"
class MultiBooked(datasets.GeneratorBasedBuilder):
"""MultiBooked dataset."""
VERSION = datasets.Version("0.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="ca", description="MultiBooked dataset in Catalan language."),
datasets.BuilderConfig(name="eu", description="MultiBooked dataset in Basque language."),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.features.Sequence(
{
"wid": datasets.Value("string"),
"sent": datasets.Value("string"),
"para": datasets.Value("string"),
"word": datasets.Value("string"),
}
),
"terms": datasets.features.Sequence(
{
"tid": datasets.Value("string"),
"lemma": datasets.Value("string"),
"morphofeat": datasets.Value("string"),
"pos": datasets.Value("string"),
"target": datasets.features.Sequence(datasets.Value("string")),
}
),
"opinions": datasets.features.Sequence(
{
"oid": datasets.Value("string"),
"opinion_holder_target": datasets.features.Sequence(datasets.Value("string")),
"opinion_target_target": datasets.features.Sequence(datasets.Value("string")),
"opinion_expression_polarity": datasets.features.ClassLabel(
names=["StrongNegative", "Negative", "Positive", "StrongPositive"]
),
"opinion_expression_target": datasets.features.Sequence(datasets.Value("string")),
}
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"dirpath": os.path.join(data_dir, "multibooked-master", "corpora", self.config.name),
},
),
]
def _generate_examples(self, dirpath):
for id_, filepath in enumerate(sorted(Path(dirpath).iterdir())):
example = defaultdict(lambda: defaultdict(list))
with open(filepath, encoding="utf-8") as f:
for _, elem in ET.iterparse(f):
if elem.tag == "text":
for child in elem:
# sometimes wid is missing in the eu configuration
example["text"]["wid"].append(child.attrib.get("wid", ""))
example["text"]["sent"].append(child.attrib["sent"])
example["text"]["para"].append(child.attrib["para"])
example["text"]["word"].append(child.text)
elif elem.tag == "terms":
for child in elem:
# sometimes tid is missing in the eu configuration
example["terms"]["tid"].append(child.attrib.get("tid", ""))
example["terms"]["lemma"].append(child.attrib["lemma"])
example["terms"]["morphofeat"].append(child.attrib["morphofeat"])
example["terms"]["pos"].append(child.attrib["pos"])
targets = []
for target in child.findall("span/target"):
targets.append(target.attrib["id"])
example["terms"]["target"].append(targets)
elif elem.tag == "opinions":
for child in elem:
example["opinions"]["oid"].append(child.attrib["oid"])
# Opinion holder
opinion_holder = child.find("opinion_holder")
targets = []
for target in opinion_holder.findall("span/target"):
targets.append(target.attrib["id"])
example["opinions"]["opinion_holder_target"].append(targets)
# Opinion target
opinion_target = child.find("opinion_target")
targets = []
for target in opinion_target.findall("span/target"):
targets.append(target.attrib["id"])
example["opinions"]["opinion_target_target"].append(targets)
# Opinion expression
opinion_expression = child.find("opinion_expression")
example["opinions"]["opinion_expression_polarity"].append(
opinion_expression.attrib["polarity"]
)
targets = []
for target in opinion_expression.findall("span/target"):
targets.append(target.attrib["id"])
example["opinions"]["opinion_expression_target"].append(targets)
yield id_, example
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