Datasets:

fhamborg
/

news_sentiment_newsmtsc

	# 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.
	"""NewsMTSC Dataset: (Multi-)Target-dependent Sentiment Classification in News Articles Dataset"""


	import csv
	import json
	import os

	import datasets

	_CITATION = """\
	@InProceedings{Hamborg2021b,
	author = {Hamborg, Felix and Donnay, Karsten},
	title = {NewsMTSC: (Multi-)Target-dependent Sentiment Classification in News Articles},
	booktitle = {Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2021)},
	year = {2021},
	month = {Apr.},
	location = {Virtual Event},
	}
	"""
	_DESCRIPTION = """\
	NewsMTSC: A large, manually annotated dataset for target-dependent sentiment classification in English news articles.
	"""
	_HOMEPAGE = "https://github.com/fhamborg/NewsMTSC/"
	_LICENSE = "MIT"
	_URL = "https://raw.githubusercontent.com/fhamborg/NewsMTSC/6b838e00f54423c253806327a0ae24dbffa24c9e/NewsSentiment/experiments/default/datasets/"
	_URLS = {
	"rw": {
	datasets.Split.TRAIN: _URL + "newsmtsc-rw-hf/train.jsonl",
	datasets.Split.VALIDATION: _URL + "newsmtsc-rw-hf/dev.jsonl",
	datasets.Split.TEST: _URL + "newsmtsc-rw-hf/test.jsonl",
	},
	"mt": {
	datasets.Split.TRAIN: _URL + "newsmtsc-mt-hf/train.jsonl",
	datasets.Split.VALIDATION: _URL + "newsmtsc-mt-hf/dev.jsonl",
	datasets.Split.TEST: _URL + "newsmtsc-mt-hf/test.jsonl",
	},
	}


	class AllowNoFurtherMentionsFeatures(datasets.Features):
	def encode_example(self, example):
	return super().encode_example(example)


	class NewsSentimentNewsmtsc(datasets.GeneratorBasedBuilder):
	"""NewsMTSC Dataset: A large, manually annotated dataset for target-dependent sentiment classification in political
	news articles."""

	VERSION = datasets.Version("1.0.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(name="mt", version=VERSION, description="Multiple targets: each sentence contains two or more targets with individually labeled sentiment (in validation and test splits)"),
	datasets.BuilderConfig(name="rw", version=VERSION, description="Real world: distribution of sentiment classes resembles real-world distribution (in validation and test splits)"),
	]

	DEFAULT_CONFIG_NAME = "rw"

	def _info(self):
	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=AllowNoFurtherMentionsFeatures(
	{
	"mention": datasets.Value("string"),
	"polarity": datasets.Value("int32"),
	"from": datasets.Value("int32"),
	"to": datasets.Value("int32"),
	"sentence": datasets.Value("string"),
	"id": datasets.Value("string")
	},
	),
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	urls = _URLS[self.config.name]
	data_dir = dl_manager.download(urls)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir[datasets.Split.TRAIN],
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir[datasets.Split.TEST],
	"split": "test"
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": data_dir[datasets.Split.VALIDATION],
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	for row in f:
	data = json.loads(row)
	#if split == "test":
	# data["polarity"] = None
	yield data["id"], data