AfriSenti-twitter-sentiment.py · shmuhammad/AfriSenti-twitter-sentiment at main

AfriSenti-twitter-sentiment / AfriSenti-twitter-sentiment.py

Rename AfriSenti.py to AfriSenti-twitter-sentiment.py

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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.
	"""AfriSenti: A Twitter sentiment dataset for 14 African languages"""



	_HOMEPAGE = "https://github.com/afrisenti-semeval/afrisent-semeval-2023"

	_DESCRIPTION = """\
	AfriSenti is the largest sentiment analysis benchmark dataset for under-represented African languages---covering 110,000+ annotated tweets in 14 African languages (Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and yoruba).
	"""


	_CITATION = """\
	@inproceedings{muhammad-etal-2023-semeval,
	title="{S}em{E}val-2023 Task 12: Sentiment Analysis for African Languages ({A}fri{S}enti-{S}em{E}val)",
	author="Muhammad, Shamsuddeen Hassan and
	Yimam, Seid and
	Abdulmumin, Idris and
	Ahmad, Ibrahim Sa'id and
	Ousidhoum, Nedjma, and
	Ayele, Abinew, and
	Adelani, David and
	Ruder, Sebastian and
	Beloucif, Meriem and
	Bello, Shehu Bello and
	Mohammad, Saif M.",
	booktitle="Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
	month=jul,
	year="2023",
	}
	"""


	import csv
	import textwrap
	import pandas as pd

	import datasets

	LANGUAGES = ['amh', 'hau', 'ibo', 'arq', 'ary', 'yor', 'por', 'twi', 'tso', 'tir', 'orm', 'pcm', 'kin', 'swa']

	class AfriSentiConfig(datasets.BuilderConfig):
	"""BuilderConfig for AfriSenti"""

	def __init__(
	self,
	text_features,
	label_column,
	label_classes,
	train_url,
	valid_url,
	test_url,
	citation,
	**kwargs,
	):
	"""BuilderConfig for AfriSenti.

	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(AfriSentiConfig, 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 AfriSenti(datasets.GeneratorBasedBuilder):
	"""AfriSenti benchmark"""

	BUILDER_CONFIGS = []

	for lang in LANGUAGES:
	BUILDER_CONFIGS.append(
	AfriSentiConfig(
	name=lang,
	description=textwrap.dedent(
	f"""\
	{lang} dataset."""
	),
	text_features={"tweet": "tweet"},
	label_classes=["positive", "neutral", "negative"],
	label_column="label",
	train_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/train.tsv",
	valid_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/dev.tsv",
	test_url=f"https://raw.githubusercontent.com/afrisenti-semeval/afrisent-semeval-2023/main/data/{lang}/test.tsv",
	citation=textwrap.dedent(
	f"""\
	{lang} 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}