Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1k<10K
ArXiv:
Tags:
License:
File size: 4,282 Bytes
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""" TweetTopic Dataset """
import json
from itertools import chain
import datasets
logger = datasets.logging.get_logger(__name__)
_DESCRIPTION = """[TweetTopic](https://arxiv.org/abs/2209.09824)"""
_VERSION = "1.0.4"
_CITATION = """
@inproceedings{dimosthenis-etal-2022-twitter,
title = "{T}witter {T}opic {C}lassification",
author = "Antypas, Dimosthenis and
Ushio, Asahi and
Camacho-Collados, Jose and
Neves, Leonardo and
Silva, Vitor and
Barbieri, Francesco",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics"
}
"""
_HOME_PAGE = "https://cardiffnlp.github.io"
_LABEL_TYPE = "single"
_NAME = f"tweet_topic_{_LABEL_TYPE}"
_URL = f'https://huggingface.co/datasets/cardiffnlp/{_NAME}/raw/main/dataset'
_URLS = {
f"{str(datasets.Split.TEST)}_2020": [f'{_URL}/split_temporal/test_2020.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TEST)}_2021": [f'{_URL}/split_temporal/test_2021.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TRAIN)}_2020": [f'{_URL}/split_temporal/train_2020.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TRAIN)}_2021": [f'{_URL}/split_temporal/train_2021.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TRAIN)}_all": [f'{_URL}/split_temporal/train_2020.{_LABEL_TYPE}.json', f'{_URL}/split_temporal/train_2021.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.VALIDATION)}_2020": [f'{_URL}/split_temporal/validation_2020.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.VALIDATION)}_2021": [f'{_URL}/split_temporal/validation_2021.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TRAIN)}_random": [f'{_URL}/split_random/train_random.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.VALIDATION)}_random": [f'{_URL}/split_random/validation_random.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TEST)}_coling2022_random": [f'{_URL}/split_coling2022_random/test_random.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TRAIN)}_coling2022_random": [f'{_URL}/split_coling2022_random/train_random.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TEST)}_coling2022": [f'{_URL}/split_coling2022_temporal/test_2021.{_LABEL_TYPE}.json'],
f"{str(datasets.Split.TRAIN)}_coling2022": [f'{_URL}/split_coling2022_temporal/train_2020.{_LABEL_TYPE}.json'],
}
class TweetTopicSingleConfig(datasets.BuilderConfig):
"""BuilderConfig"""
def __init__(self, **kwargs):
"""BuilderConfig.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(TweetTopicSingleConfig, self).__init__(**kwargs)
class TweetTopicSingle(datasets.GeneratorBasedBuilder):
"""Dataset."""
BUILDER_CONFIGS = [
TweetTopicSingleConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION),
]
def _split_generators(self, dl_manager):
downloaded_file = dl_manager.download_and_extract(_URLS)
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[i]}) for i in _URLS.keys()]
def _generate_examples(self, filepaths):
_key = 0
for filepath in filepaths:
logger.info(f"generating examples from = {filepath}")
with open(filepath, encoding="utf-8") as f:
_list = [i for i in f.read().split('\n') if len(i) > 0]
for i in _list:
data = json.loads(i)
yield _key, data
_key += 1
def _info(self):
names = ["arts_&_culture", "business_&_entrepreneurs", "pop_culture", "daily_life", "sports_&_gaming", "science_&_technology"]
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"date": datasets.Value("string"),
"label": datasets.features.ClassLabel(names=names),
"label_name": datasets.Value("string"),
"id": datasets.Value("string")
}
),
supervised_keys=None,
homepage=_HOME_PAGE,
citation=_CITATION,
)
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