Datasets:

uitnlp
/

vietnamese_students_feedback

	# 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()),
	}