Datasets:

philschmid
/

emotion

	import json

	import datasets
	from datasets.tasks import TextClassification


	_CITATION = """\
	@inproceedings{saravia-etal-2018-carer,
	title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
	author = "Saravia, Elvis and
	Liu, Hsien-Chi Toby and
	Huang, Yen-Hao and
	Wu, Junlin and
	Chen, Yi-Shin",
	booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
	month = oct # "-" # nov,
	year = "2018",
	address = "Brussels, Belgium",
	publisher = "Association for Computational Linguistics",
	url = "https://www.aclweb.org/anthology/D18-1404",
	doi = "10.18653/v1/D18-1404",
	pages = "3687--3697",
	abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
	}
	"""

	_DESCRIPTION = """\
	Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.
	"""

	_HOMEPAGE = "https://github.com/dair-ai/emotion_dataset"

	_LICENSE = "The dataset should be used for educational and research purposes only"

	_URLS = {
	"split": {
	"train": "data/train.jsonl.gz",
	"validation": "data/validation.jsonl.gz",
	"test": "data/test.jsonl.gz",
	},
	"unsplit": {
	"train": "data/data.jsonl.gz",
	},
	}


	class Emotion(datasets.GeneratorBasedBuilder):
	VERSION = datasets.Version("1.0.0")
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="split", version=VERSION, description="Dataset split in train, validation and test"
	),
	datasets.BuilderConfig(name="unsplit", version=VERSION, description="Unsplit dataset"),
	]
	DEFAULT_CONFIG_NAME = "split"

	def _info(self):
	class_names = ["sadness", "joy", "love", "anger", "fear", "surprise"]
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{"text": datasets.Value("string"), "label": datasets.ClassLabel(names=class_names)}
	),
	supervised_keys=("text", "label"),
	homepage=_HOMEPAGE,
	citation=_CITATION,
	license=_LICENSE,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	paths = dl_manager.download_and_extract(_URLS[self.config.name])
	if self.config.name == "split":
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": paths["validation"]}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": paths["test"]}),
	]
	else:
	return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": paths["train"]})]

	def _generate_examples(self, filepath):
	"""Generate examples."""
	with open(filepath, encoding="utf-8") as f:
	for idx, line in enumerate(f):
	example = json.loads(line)
	yield idx, example