Datasets:
senti_lex

	# 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.
	"""Sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them"""


	import os

	import datasets


	_CITATION = """\
	@inproceedings{inproceedings,
	author = {Chen, Yanqing and Skiena, Steven},
	year = {2014},
	month = {06},
	pages = {383-389},
	title = {Building Sentiment Lexicons for All Major Languages},
	volume = {2},
	journal = {52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference},
	doi = {10.3115/v1/P14-2063}
	}
	"""

	_DESCRIPTION = """\
	This dataset add sentiment lexicons for 81 languages generated via graph propagation based on a knowledge graph--a graphical representation of real-world entities and the links between them.
	"""

	_HOMEPAGE = "https://sites.google.com/site/datascienceslab/projects/multilingualsentiment"

	_LICENSE = "GNU General Public License v3"

	# Origin data from: "https://www.kaggle.com/rtatman/sentiment-lexicons-for-81-languages"
	_URL = "data.zip"

	LANGS = [
	"af",
	"an",
	"ar",
	"az",
	"be",
	"bg",
	"bn",
	"br",
	"bs",
	"ca",
	"cs",
	"cy",
	"da",
	"de",
	"el",
	"eo",
	"es",
	"et",
	"eu",
	"fa",
	"fi",
	"fo",
	"fr",
	"fy",
	"ga",
	"gd",
	"gl",
	"gu",
	"he",
	"hi",
	"hr",
	"ht",
	"hu",
	"hy",
	"ia",
	"id",
	"io",
	"is",
	"it",
	"ja",
	"ka",
	"km",
	"kn",
	"ko",
	"ku",
	"ky",
	"la",
	"lb",
	"lt",
	"lv",
	"mk",
	"mr",
	"ms",
	"mt",
	"nl",
	"nn",
	"no",
	"pl",
	"pt",
	"rm",
	"ro",
	"ru",
	"sk",
	"sl",
	"sq",
	"sr",
	"sv",
	"sw",
	"ta",
	"te",
	"th",
	"tk",
	"tl",
	"tr",
	"uk",
	"ur",
	"uz",
	"vi",
	"vo",
	"wa",
	"yi",
	"zh",
	"zhw",
	]


	class SentiLex(datasets.GeneratorBasedBuilder):
	"""Sentiment lexicons for 81 different languages"""

	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name=i,
	version=datasets.Version("1.1.0"),
	description=("Lexicon of positive and negative words for the " + i + " language"),
	)
	for i in LANGS
	]

	def _info(self):

	features = datasets.Features(
	{
	"word": datasets.Value("string"),
	"sentiment": datasets.ClassLabel(
	names=[
	"negative",
	"positive",
	]
	),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"data_dir": data_dir,
	},
	),
	]

	def _generate_examples(self, data_dir):
	"""Yields examples."""

	filepaths = [
	os.path.join(data_dir, "sentiment-lexicons", "negative_words_" + self.config.name + ".txt"),
	os.path.join(data_dir, "sentiment-lexicons", "positive_words_" + self.config.name + ".txt"),
	]

	for file_idx, filepath in enumerate(filepaths):

	with open(filepath, encoding="utf-8") as f:

	for id_, line in enumerate(f):

	if "negative" in filepath:
	yield f"{file_idx}_{id_}", {
	"word": line.strip(" \n"),
	"sentiment": "negative",
	}
	elif "positive" in filepath:
	yield f"{file_idx}_{id_}", {
	"word": line.strip(" \n"),
	"sentiment": "positive",
	}