Datasets:
Tasks:
Text Classification
Languages:
Vietnamese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
"""Vietnamese Students’ Feedback Corpus.""" | |
import datasets | |
_CITATION = """\ | |
@InProceedings{8573337, | |
author={Nguyen, Kiet Van and Nguyen, Vu Duc and Nguyen, Phu X. V. and Truong, Tham T. H. and Nguyen, Ngan Luu-Thuy}, | |
booktitle={2018 10th International Conference on Knowledge and Systems Engineering (KSE)}, | |
title={UIT-VSFC: Vietnamese Students’ Feedback Corpus for Sentiment Analysis}, | |
year={2018}, | |
volume={}, | |
number={}, | |
pages={19-24}, | |
doi={10.1109/KSE.2018.8573337} | |
} | |
""" | |
_DESCRIPTION = """\ | |
Students’ feedback is a vital resource for the interdisciplinary research involving the combining of two different | |
research fields between sentiment analysis and education. | |
Vietnamese Students’ Feedback Corpus (UIT-VSFC) is the resource consists of over 16,000 sentences which are | |
human-annotated with two different tasks: sentiment-based and topic-based classifications. | |
To assess the quality of our corpus, we measure the annotator agreements and classification evaluation on the | |
UIT-VSFC corpus. As a result, we obtained the inter-annotator agreement of sentiments and topics with more than over | |
91% and 71% respectively. In addition, we built the baseline model with the Maximum Entropy classifier and achieved | |
approximately 88% of the sentiment F1-score and over 84% of the topic F1-score. | |
""" | |
_HOMEPAGE = "https://sites.google.com/uit.edu.vn/uit-nlp/datasets-projects#h.p_4Brw8L-cbfTe" | |
# TODO: Add the licence for the dataset here if you can find it | |
# _LICENSE = "" | |
_URLS = { | |
datasets.Split.TRAIN: { | |
"sentences": "https://drive.google.com/uc?id=1nzak5OkrheRV1ltOGCXkT671bmjODLhP&export=download", | |
"sentiments": "https://drive.google.com/uc?id=1ye-gOZIBqXdKOoi_YxvpT6FeRNmViPPv&export=down load", | |
"topics": "https://drive.google.com/uc?id=14MuDtwMnNOcr4z_8KdpxprjbwaQ7lJ_C&export=download", | |
}, | |
datasets.Split.VALIDATION: { | |
"sentences": "https://drive.google.com/uc?id=1sMJSR3oRfPc3fe1gK-V3W5F24tov_517&export=download", | |
"sentiments": "https://drive.google.com/uc?id=1GiY1AOp41dLXIIkgES4422AuDwmbUseL&export=download", | |
"topics": "https://drive.google.com/uc?id=1DwLgDEaFWQe8mOd7EpF-xqMEbDLfdT-W&export=download", | |
}, | |
datasets.Split.TEST: { | |
"sentences": "https://drive.google.com/uc?id=1aNMOeZZbNwSRkjyCWAGtNCMa3YrshR-n&export=download", | |
"sentiments": "https://drive.google.com/uc?id=1vkQS5gI0is4ACU58-AbWusnemw7KZNfO&export=download", | |
"topics": "https://drive.google.com/uc?id=1_ArMpDguVsbUGl-xSMkTF_p5KpZrmpSB&export=download", | |
}, | |
} | |
class VietnameseStudentsFeedback(datasets.GeneratorBasedBuilder): | |
"""Vietnamese Students’ Feedback Corpus.""" | |
VERSION = datasets.Version("1.0.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"sentence": datasets.Value("string"), | |
"sentiment": datasets.ClassLabel(names=["negative", "neutral", "positive"]), | |
"topic": datasets.ClassLabel(names=["lecturer", "training_program", "facility", "others"]), | |
} | |
), | |
homepage=_HOMEPAGE, | |
# license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_dir = dl_manager.download(_URLS) | |
return [ | |
datasets.SplitGenerator( | |
name=split, | |
gen_kwargs={ | |
"sentences_path": data_dir[split]["sentences"], | |
"sentiments_path": data_dir[split]["sentiments"], | |
"topics_path": data_dir[split]["topics"], | |
}, | |
) for split in _URLS | |
] | |
def _generate_examples(self, sentences_path, sentiments_path, topics_path): | |
with open(sentences_path, encoding="utf-8") as sentences, open( | |
sentiments_path, encoding="utf-8" | |
) as sentiments, open(topics_path, encoding="utf-8") as topics: | |
for key, (sentence, sentiment, topic) in enumerate(zip(sentences, sentiments, topics)): | |
yield key, { | |
"sentence": sentence.strip(), | |
"sentiment": int(sentiment.strip()), | |
"topic": int(topic.strip()), | |
} | |