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# coding=utf-8
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import pandas as pd
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
_CITATION = """\
@inproceedings{ho2020emotion,
title={Emotion recognition for vietnamese social media text},
author={Ho, Vong Anh and Nguyen, Duong Huynh-Cong and Nguyen, Danh Hoang and Pham, Linh Thi-Van and Nguyen, Duc-Vu and Nguyen, Kiet Van and Nguyen, Ngan Luu-Thuy},
booktitle={Computational Linguistics: 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019, Hanoi, Vietnam, October 11--13, 2019, Revised Selected Papers 16},
pages={319--333},
year={2020},
organization={Springer}
}
"""
_DATASETNAME = "uit_vsmec"
_DESCRIPTION = """\
This dataset consists of Vietnamese Facebook comments that were manually annotated for sentiment.
There are seven possible emotion labels: enjoyment, sadness, fear, anger, disgust, surprise or other (for comments with no or neutral emotions).
Two rounds of manual annotations were done to train annotators with tagging and editing guidelines.
Annotation was performed until inter-annotator agreement reached at least 80%.
"""
_HOMEPAGE = "https://drive.google.com/drive/folders/1HooABJyrddVGzll7fgkJ6VzkG_XuWfRu"
_LICENSE = Licenses.UNKNOWN.value
_LANGUAGES = ["vie"]
_LOCAL = False
_URLS = {
"train": "https://docs.google.com/spreadsheets/export?id=10VYzfK7JLg-vfmqH0UmKX62z_uaXU-Hp&format=csv",
"valid": "https://docs.google.com/spreadsheets/export?id=1EsSFZ94fj2yTvFKO6EyxM0wBRcG0s1KE&format=csv",
"test": "https://docs.google.com/spreadsheets/export?id=1D16FCKKgJ0T6t2aSA3biWVwvD9fa4G9a&format=csv",
}
_SUPPORTED_TASKS = [Tasks.EMOTION_CLASSIFICATION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class UITVSMECDataset(datasets.GeneratorBasedBuilder):
"""
This is the main class of SEACrowd dataloader for UIT-VSMEC, focusing on emotion/sentiment classification task.
"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=f"{_DATASETNAME}",
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_text",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema="seacrowd_text",
subset_id=f"{_DATASETNAME}",
),
]
LABEL_NAMES = ["Other", "Disgust", "Enjoyment", "Anger", "Surprise", "Sadness", "Fear"]
DEFAULT_CONFIG_NAME = "uit_vsmec_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features({"Emotion": datasets.Value("string"), "Sentence": datasets.Value("string")})
elif self.config.schema == "seacrowd_text":
features = schemas.text_features(self.LABEL_NAMES)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
path_dict = dl_manager.download_and_extract(_URLS)
train_path, valid_path, test_path = path_dict["train"], path_dict["valid"], path_dict["test"]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": train_path,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": test_path,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": valid_path,
},
),
]
def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
df = pd.read_csv(filepath).reset_index()
if self.config.schema == "source":
for row in df.itertuples():
ex = {"Emotion": row.Emotion, "Sentence": row.Sentence}
yield row.index, ex
elif self.config.schema == "seacrowd_text":
for row in df.itertuples():
ex = {"id": str(row.index), "text": row.Sentence, "label": self.LABEL_NAMES.index(row.Emotion)}
yield row.index, ex
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