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Upload id_sent_emo_mobile_apps.py with huggingface_hub
Browse files- id_sent_emo_mobile_apps.py +136 -0
id_sent_emo_mobile_apps.py
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# coding=utf-8
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from pathlib import Path
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from typing import Dict, List, Tuple
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import datasets
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import pandas as pd
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from seacrowd.utils import schemas
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from seacrowd.utils.configs import SEACrowdConfig
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from seacrowd.utils.constants import Licenses, Tasks
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_CITATION = """
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@article{riccosan2023,
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author = {Riccosan and Saputra, Karen Etania},
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title = {Multilabel multiclass sentiment and emotion dataset from indonesian mobile application review},
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journal = {Data in Brief},
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volume = {50},
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year = {2023},
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doi = {10.1016/j.dib.2023.109576},
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}
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"""
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_LOCAL = False
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_LANGUAGES = ["ind"]
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_DATASETNAME = "id_sent_emo_mobile_apps"
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_DESCRIPTION = """
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This dataset contains manually annotated public reviews of mobile applications in Indonesia.
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Each review is given a sentiment label (positive, negative, neutral) and
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an emotion label (anger, sadness, fear, happiness, love, neutral).
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"""
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_HOMEPAGE = "https://github.com/Ricco48/Multilabel-Sentiment-and-Emotion-Dataset-from-Indonesian-" "Mobile-Application-Review/tree/CreateCodeForPaper"
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_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
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_URL = (
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"https://github.com/Ricco48/Multilabel-Sentiment-and-Emotion-Dataset-from-Indonesian-Mobile-Application-Review/raw/CreateCodeForPaper/"
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"Multilabel%20Sentiment%20and%20Emotion%20Dataset%20from%20Indonesian%20Mobile%20Application%20Review/Multilabel%20Sentiment%20and%20Emotion"
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"%20Dataset%20from%20Indonesian%20Mobile%20Application%20Review.csv"
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)
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_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS, Tasks.EMOTION_CLASSIFICATION]
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_SOURCE_VERSION = "1.0.0"
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_SEACROWD_VERSION = "2024.06.20"
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class EmoSentIndMobile(datasets.GeneratorBasedBuilder):
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"""Dataset of Indonesian mobile application reviews manually annotated for emotion and sentiment."""
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SUBSETS = ["emotion", "sentiment"]
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EMOTION_CLASS_LABELS = ["Sad", "Anger", "Fear", "Happy", "Love", "Neutral"]
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SENTIMENT_CLASS_LABELS = ["Negative", "Positive", "Neutral"]
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BUILDER_CONFIGS = [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_source",
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version=datasets.Version(_SOURCE_VERSION),
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description=f"{_DATASETNAME} source schema",
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schema="source",
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subset_id=_DATASETNAME
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)
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] + [
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SEACrowdConfig(
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name=f"{_DATASETNAME}_{subset}_seacrowd_text",
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version=datasets.Version(_SEACROWD_VERSION),
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description=f"{_DATASETNAME} SEACrowd schema for {subset} subset",
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schema="seacrowd_text",
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subset_id=f"{_DATASETNAME}_{subset}",
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)
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for subset in SUBSETS
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]
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DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
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def _info(self) -> datasets.DatasetInfo:
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if self.config.schema == "source":
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features = datasets.Features(
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{
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"content": datasets.Value("string"),
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"sentiment": datasets.Value("string"),
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"emotion": datasets.Value("string"),
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}
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)
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elif self.config.schema == "seacrowd_text":
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if "emotion" in self.config.subset_id:
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labels = self.EMOTION_CLASS_LABELS
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elif "sentiment" in self.config.subset_id:
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labels = self.SENTIMENT_CLASS_LABELS
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features = schemas.text_features(label_names=labels)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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"""Returns SplitGenerators."""
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fp = dl_manager.download(_URL)
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={"filepath": fp},
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),
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]
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def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]:
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"""Yields examples as (key, example) tuples."""
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df = pd.read_csv(filepath, sep="\t", index_col=None)
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for index, row in df.iterrows():
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if self.config.schema == "source":
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example = {
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"content": row["content"],
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"sentiment": row["Sentiment"].title(),
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"emotion": row["Emotion"].title(),
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}
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elif self.config.schema == "seacrowd_text":
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if "emotion" in self.config.subset_id:
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label = row["Emotion"]
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elif "sentiment" in self.config.subset_id:
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label = row["Sentiment"]
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example = {"id": str(index), "text": row["content"], "label": label.title()}
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yield index, example
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