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id_clickbait.py

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+import json
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks
+
+_CITATION = """\
+@article{WILLIAM2020106231,
+title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines",
+journal = "Data in Brief",
+volume = "32",
+pages = "106231",
+year = "2020",
+issn = "2352-3409",
+doi = "https://doi.org/10.1016/j.dib.2020.106231",
+url = "http://www.sciencedirect.com/science/article/pii/S2352340920311252",
+author = "Andika William and Yunita Sari",
+keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification",
+abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas."
+}
+"""
+
+_LOCAL = False
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_DATASETNAME = "id_clickbait"
+_DESCRIPTION = """\
+The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
+publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
+Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii)
+15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline.
+Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated
+sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.
+"""
+_HOMEPAGE = "https://www.sciencedirect.com/science/article/pii/S2352340920311252#!"
+_LICENSE = "Creative Commons Attribution 4.0 International"
+_URLS = {
+    _DATASETNAME: "https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/k42j7x2kpn-1.zip",
+}
+_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS]
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class IdClickbait(datasets.GeneratorBasedBuilder):
+    """The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news, annotated with a label whether each is a clickbait"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    NUSANTARA_VERSION = datasets.Version(_NUSANTARA_VERSION)
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="id_clickbait_source",
+            version=SOURCE_VERSION,
+            description="CLICK-ID source schema",
+            schema="source",
+            subset_id="id_clickbait",
+        ),
+        NusantaraConfig(
+            name="id_clickbait_nusantara_text",
+            version=NUSANTARA_VERSION,
+            description="CLICK-ID Nusantara schema",
+            schema="nusantara_text",
+            subset_id="id_clickbait",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "id_clickbait_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features({"title": datasets.Value("string"), "label": datasets.Value("string"), "label_score": datasets.Value("int8")})
+        elif self.config.schema == "nusantara_text":
+            features = schemas.text_features(["non-clickbait", "clickbait"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        # Dataset does not have predetermined split, putting all as TRAIN
+        urls = _URLS[_DATASETNAME]
+        base_dir = Path(dl_manager.download_and_extract(urls)) / "annotated" / "combined" / "json"
+        data_files = {"train": base_dir / "main.json"}
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_files["train"],
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        # Dataset does not have row id, using python enumeration.
+        data = json.load(open(filepath, "r"))
+
+        if self.config.schema == "source":
+            for row_index, row in enumerate(data):
+                ex = {
+                    "title": row["title"],
+                    "label": row["label"],
+                    "label_score": row["label_score"],
+                }
+                yield row_index, ex
+
+        elif self.config.schema == "nusantara_text":
+            for row_index, row in enumerate(data):
+                ex = {
+                    "id": str(row_index),
+                    "text": row["title"],
+                    "label": row["label"],
+                }
+                yield row_index, ex