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prdect_id.py

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+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}")