Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-analysis
Languages:
Yoruba
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
File size: 3,396 Bytes
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# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS"""
import datasets
import json
import pandas as pd
logger = datasets.logging.get_logger(__name__)
_CITATION = """\
@inproceedings{
shode2022yosm,
title={{YOSM}: A {NEW} {YORUBA} {SENTIMENT} {CORPUS} {FOR} {MOVIE} {REVIEWS}},
author={Iyanuoluwa Shode and David Ifeoluwa Adelani and Anna Feldman},
booktitle={3rd Workshop on African Natural Language Processing},
year={2022},
url={https://openreview.net/forum?id=rRzx5qzVIb9}
}
"""
_DESCRIPTION = """\
YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS
- Yoruba
"""
languages=[
'yoruba'
]
_URL = "https://github.com/IyanuSh/YOSM"
_TRAINING_FILE = "train.tsv"
_DEV_FILE = "dev.tsv"
_TEST_FILE = "test.tsv"
class Yosm(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
version=datasets.Version('1.0.0'),
name=lang,
description='YOSM: A NEW YORUBA SENTIMENT CORPUS FOR MOVIE REVIEWS'
) for lang in languages
]
def _info(self):
features = datasets.features({
'yo_review': datasets.Value('string'),
'sentiment': datasets.Value('string')
})
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
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_URL,
# License for the dataset if available
license='',
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
lang = self.config.name
splits = [
datasets.SplitGenerator(
name='train',
gen_kwargs={
'filepath': _TRAINING_FILE,
},
),
datasets.SplitGenerator(
name='dev',
gen_kwargs={
'filepath': _DEV_FILE,
},
),
datasets.SplitGenerator(
name='test',
gen_kwargs={
'filepath': _TEST_FILE,
},
)
]
return splits
def _generate_examples(self, filepath):
yosm_df = pd.read_csv(filepath, sep="\t", lineterminator='\n')
for index, row in yosm_df.iterrows():
yield row["yo_review"], row["sentiment"]
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