Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Size:
10K<n<100K
License:
File size: 5,054 Bytes
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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.
"""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
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