from pathlib import Path from typing import Dict, List, Tuple import datasets import pandas as pd from datasets.download.download_manager import DownloadManager from seacrowd.utils import schemas from seacrowd.utils.configs import SEACrowdConfig from seacrowd.utils.constants import Licenses, Tasks _CITATION = """ @article{SUTOYO2022108554, title = {PRDECT-ID: Indonesian product reviews dataset for emotions classification tasks}, journal = {Data in Brief}, volume = {44}, pages = {108554}, year = {2022}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2022.108554}, url = {https://www.sciencedirect.com/science/article/pii/S2352340922007612}, author = {Rhio Sutoyo and Said Achmad and Andry Chowanda and Esther Widhi Andangsari and Sani M. Isa}, keywords = {Natural language processing, Text processing, Text mining, Emotions classification, Sentiment analysis}, abstract = {Recognizing emotions is vital in communication. Emotions convey additional meanings to the communication process. Nowadays, people can communicate their emotions on many platforms; one is the product review. Product reviews in the online platform are an important element that affects customers’ buying decisions. Hence, it is essential to recognize emotions from the product reviews. Emotions recognition from the product reviews can be done automatically using a machine or deep learning algorithm. Dataset can be considered as the fuel to model the recognizer. However, only a limited dataset exists in recognizing emotions from the product reviews, particularly in a local language. This research contributes to the dataset collection of 5400 product reviews in Indonesian. It was carefully curated from various (29) product categories, annotated with five emotions, and verified by an expert in clinical psychology. The dataset supports an innovative process to build automatic emotion classification on product reviews.} } """ _LOCAL = False _LANGUAGES = ["ind"] _DATASETNAME = "prdect_id" _DESCRIPTION = """ PRDECT-ID Dataset is a collection of Indonesian product review data annotated with emotion and sentiment labels. The data were collected from one of the giant e-commerce in Indonesia named Tokopedia. The dataset contains product reviews from 29 product categories on Tokopedia that use the Indonesian language. Each product review is annotated with a single emotion, i.e., love, happiness, anger, fear, or sadness. The group of annotators does the annotation process to provide emotion labels by following the emotions annotation criteria created by an expert in clinical psychology. Other attributes related to the product review are also extracted, such as Location, Price, Overall Rating, Number Sold, Total Review, and Customer Rating, to support further research. """ _HOMEPAGE = "https://data.mendeley.com/datasets/574v66hf2v/1" _LICENSE = Licenses.CC_BY_4_0.value _URL = "https://data.mendeley.com/public-files/datasets/574v66hf2v/files/f258d159-c678-42f1-9634-edf091a0b1f3/file_downloaded" _SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS, Tasks.EMOTION_CLASSIFICATION] _SOURCE_VERSION = "1.0.0" _SEACROWD_VERSION = "2024.06.20" class PrdectIDDataset(datasets.GeneratorBasedBuilder): """PRDECT-ID Dataset""" SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) SEACROWD_SCHEMA_NAME = "text" BUILDER_CONFIGS = [ SEACrowdConfig( name=f"{_DATASETNAME}_emotion_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}_emotion", ), SEACrowdConfig( name=f"{_DATASETNAME}_sentiment_source", version=SOURCE_VERSION, description=f"{_DATASETNAME} source schema", schema="source", subset_id=f"{_DATASETNAME}_sentiment", ), SEACrowdConfig( name=f"{_DATASETNAME}_emotion_seacrowd_{SEACROWD_SCHEMA_NAME}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} SEACrowd schema for emotion classification", schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", subset_id=f"{_DATASETNAME}_emotion", ), SEACrowdConfig( name=f"{_DATASETNAME}_sentiment_seacrowd_{SEACROWD_SCHEMA_NAME}", version=SEACROWD_VERSION, description=f"{_DATASETNAME} SEACrowd schema for sentiment analysis", schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}", subset_id=f"{_DATASETNAME}_sentiment", ), ] DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source" CLASS_LABELS_EMOTION = ["Happy", "Sadness", "Anger", "Love", "Fear"] CLASS_LABELS_SENTIMENT = ["Positive", "Negative"] def _info(self) -> datasets.DatasetInfo: if self.config.schema == "source": features = datasets.Features( { "Category": datasets.Value("string"), "Product Name": datasets.Value("string"), "Location": datasets.Value("string"), "Price": datasets.Value("int32"), "Overall Rating": datasets.Value("float32"), "Number Sold": datasets.Value("int32"), "Total Review": datasets.Value("int32"), "Customer Rating": datasets.Value("int32"), "Customer Review": datasets.Value("string"), "Sentiment": datasets.ClassLabel(names=self.CLASS_LABELS_SENTIMENT), "Emotion": datasets.ClassLabel(names=self.CLASS_LABELS_EMOTION), } ) elif self.config.schema == "seacrowd_text": if self.config.subset_id == f"{_DATASETNAME}_emotion": features = schemas.text_features(label_names=self.CLASS_LABELS_EMOTION) elif self.config.subset_id == f"{_DATASETNAME}_sentiment": features = schemas.text_features(label_names=self.CLASS_LABELS_SENTIMENT) else: raise ValueError(f"Invalid subset: {self.config.subset_id}") else: raise ValueError(f"Schema '{self.config.schema}' is not defined.") return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager: DownloadManager) -> List[datasets.SplitGenerator]: """Returns SplitGenerators.""" data_file = Path(dl_manager.download(_URL)) return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_file})] def _generate_examples(self, filepath: Path) -> Tuple[int, Dict]: """Yield examples as (key, example) tuples""" df = pd.read_csv(filepath, encoding="utf-8") for idx, row in df.iterrows(): if self.config.schema == "source": yield idx, dict(row) elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}": if self.config.subset_id == f"{_DATASETNAME}_emotion": yield idx, {"id": idx, "text": row["Customer Review"], "label": row["Emotion"]} elif self.config.subset_id == f"{_DATASETNAME}_sentiment": yield idx, {"id": idx, "text": row["Customer Review"], "label": row["Sentiment"]} else: raise ValueError(f"Invalid subset: {self.config.subset_id}")