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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.
"""NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis"""
_HOMEPAGE = "https://github.com/hausanlp/NaijaSenti"
_DESCRIPTION = """\
NaijaSenti is the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria — Hausa, Igbo, Nigerian-Pidgin, and Yorùbá — consisting of around 30,000 annotated tweets per language, including a significant fraction of code-mixed tweets.
"""
_CITATION = """\
@inproceedings{muhammad-etal-2022-naijasenti,
title = "{N}aija{S}enti: A {N}igerian {T}witter Sentiment Corpus for Multilingual Sentiment Analysis",
author = "Muhammad, Shamsuddeen Hassan and
Adelani, David Ifeoluwa and
Ruder, Sebastian and
Ahmad, Ibrahim Sa{'}id and
Abdulmumin, Idris and
Bello, Bello Shehu and
Choudhury, Monojit and
Emezue, Chris Chinenye and
Abdullahi, Saheed Salahudeen and
Aremu, Anuoluwapo and
Jorge, Al{\'\i}pio and
Brazdil, Pavel",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.63",
pages = "590--602",
}
"""
import textwrap
import pandas as pd
import datasets
LANGUAGES = ['hau', 'ibo', 'yor', 'pcm']
class NaijaSentiConfig(datasets.BuilderConfig):
"""BuilderConfig for NaijaSenti"""
def __init__(
self,
text_features,
label_column,
label_classes,
train_url,
valid_url,
test_url,
citation,
**kwargs,
):
"""BuilderConfig for NaijaSenti.
Args:
text_features: `dict[string]`, map from the name of the feature
dict for each text field to the name of the column in the txt/csv/tsv file
label_column: `string`, name of the column in the txt/csv/tsv file corresponding
to the label
label_classes: `list[string]`, the list of classes if the label is categorical
train_url: `string`, url to train file from
valid_url: `string`, url to valid file from
test_url: `string`, url to test file from
citation: `string`, citation for the data set
**kwargs: keyword arguments forwarded to super.
"""
super(NaijaSentiConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.text_features = text_features
self.label_column = label_column
self.label_classes = label_classes
self.train_url = train_url
self.valid_url = valid_url
self.test_url = test_url
self.citation = citation
class NaijaSenti(datasets.GeneratorBasedBuilder):
"""NaijaSenti benchmark"""
BUILDER_CONFIGS = []
for lang in LANGUAGES:
BUILDER_CONFIGS.append(
NaijaSentiConfig(
name=lang,
description=textwrap.dedent(
f"""{_DESCRIPTION}"""
),
text_features={"tweet": "tweet"},
label_classes=["positive", "neutral", "negative"],
label_column="label",
train_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/train.tsv",
valid_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/dev.tsv",
test_url=f"https://raw.githubusercontent.com/hausanlp/NaijaSenti/main/data/annotated_tweets/{lang}/test.tsv",
citation=textwrap.dedent(
f"""{_CITATION}"""
),
),
)
def _info(self):
features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features}
features["label"] = datasets.features.ClassLabel(names=self.config.label_classes)
return datasets.DatasetInfo(
description=self.config.description,
features=datasets.Features(features),
citation=self.config.citation,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
train_path = dl_manager.download_and_extract(self.config.train_url)
valid_path = dl_manager.download_and_extract(self.config.valid_url)
test_path = dl_manager.download_and_extract(self.config.test_url)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
]
def _generate_examples(self, filepath):
df = pd.read_csv(filepath, sep='\t')
print('-'*100)
print(df.head())
print('-'*100)
for id_, row in df.iterrows():
tweet = row["tweet"]
label = row["label"]
yield id_, {"tweet": tweet, "label": label}
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