Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
10K<n<100K
Tags:
emotion-classification
License:
File size: 4,189 Bytes
dcb07a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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://huggingface.co/datasets/jeffnyman/emotions"
_LICENSE = "cc-by-sa-4.0"
_URLS = {
"split": {
"train": "data/train.jsonl.gz",
"validation": "data/validation.jsonl.gz",
"test": "data/test.jsonl.gz",
},
"unsplit": {
"train": "data/data.jsonl.gz",
},
}
class Emotions(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):
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):
with open(filepath, encoding="utf-8") as f:
for idx, line in enumerate(f):
example = json.loads(line)
yield idx, example
|