Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K - 100K
Tags:
emotion-classification
License:
File size: 3,967 Bytes
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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 |