Datasets:

HausaNLP
/

NaijaSenti-Twitter

	# 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}