political_advertising_loader.py

from pathlib import Path

import datasets
import pandas as pd

_CITATION = """\
@inproceedings{augustyniak-etal-2020-political,
    title = "Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections",
    author = "Augustyniak, Lukasz  and
      Rajda, Krzysztof  and
      Kajdanowicz, Tomasz  and
      Bernaczyk, Micha{\l}",
    booktitle = "Proceedings of the The Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.winlp-1.28",
    pages = "110--114"
}
"""

_DESCRIPTION = "Polish Political Advertising Dataset"

_HOMEPAGE = "https://github.com/laugustyniak/misinformation"

DATA_PATH = Path(".")


class PoliticalAdvertisingConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(PoliticalAdvertisingConfig, self).__init__(**kwargs)


class PoliticalAdvertisingDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    TRAIN_FILE = DATA_PATH / "train.json"
    VAL_FILE = DATA_PATH / "dev.json"
    TEST_FILE = DATA_PATH / "test.json"

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="political-advertising-pl", version=VERSION)
    ]

    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "tokens": datasets.Sequence(datasets.Value("string")),
                "tags": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            "O",
                            "B-DEFENSE_AND_SECURITY",
                            "I-DEFENSE_AND_SECURITY",
                            "B-EDUCATION",
                            "I-EDUCATION",
                            "B-FOREIGN_POLICY",
                            "I-FOREIGN_POLICY",
                            "B-HEALHCARE",
                            "I-HEALHCARE",
                            "B-IMMIGRATION",
                            "I-IMMIGRATION",
                            "B-INFRASTRUCTURE_AND_ENVIROMENT",
                            "I-INFRASTRUCTURE_AND_ENVIROMENT",
                            "B-POLITICAL_AND_LEGAL_SYSTEM",
                            "I-POLITICAL_AND_LEGAL_SYSTEM",
                            "B-SOCIETY",
                            "I-SOCIETY",
                            "B-WELFARE",
                            "I-WELFARE",
                        ]
                    )
                ),
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, gen_kwargs={"filepath": str(self.TRAIN_FILE)}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={"filepath": str(self.TEST_FILE)}
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": str(self.VAL_FILE)},
            ),
        ]

    def _generate_examples(self, filepath: str):
        df = pd.read_json(filepath)
        for row_id, row in df.iterrows():
            yield row_id, {
                "id": row.id,
                "text": row.text,
                "tags": row.tags,
                "url": row.url,
                "tweet_id": row.tweet_id,
            }