per_sent.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" **Person SenTiment, a challenge dataset for author sentiment prediction in the news domain **

PerSenT is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotation for 5.3k documents and 38k paragraphs covering 3.2k unique entities.

"""


import csv

import datasets
from datasets.splits import NamedSplit


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{bastan2020authors,
      title={Author's Sentiment Prediction},
      author={Mohaddeseh Bastan and Mahnaz Koupaee and Youngseo Son and Richard Sicoli and Niranjan Balasubramanian},
      year={2020},
      eprint={2011.06128},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
Person SenTiment (PerSenT) is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotation for 5.3k documents and 38k paragraphs covering 3.2k unique entities.

The dataset consists of sentiment annotations on news articles about people. For each article, annotators judge what the author’s sentiment is towards the main (target) entity of the article. The annotations also include similar judgments on paragraphs within the article.

To split the dataset, entities into 4 mutually exclusive sets. Due to the nature of news collections, some entities tend to dominate the collection. In the collection, there were four entities which were the main entity in nearly 800 articles. To avoid these entities from dominating the train or test splits, we moved them to a separate test collection. We split the remaining into a training, dev, and test sets at random. Thus our collection includes one standard test set consisting of articles drawn at random (Test Standard -- `test_random`), while the other is a test set which contains multiple articles about a small number of popular entities (Test Frequent -- `test_fixed`).
"""

_LICENSE = "Creative Commons Attribution 4.0 International License"

_URLs = {
    "train": "https://raw.githubusercontent.com/MHDBST/PerSenT/main/train.csv",
    "dev": "https://raw.githubusercontent.com/MHDBST/PerSenT/main/dev.csv",
    "test_random": "https://raw.githubusercontent.com/MHDBST/PerSenT/main/random_test.csv",
    "test_fixed": "https://raw.githubusercontent.com/MHDBST/PerSenT/main/fixed_test.csv",
}


class PerSent(datasets.GeneratorBasedBuilder):
    """Person SenTiment (PerSenT) is a crowd-sourced dataset that captures the sentiment of an author towards the main entity in a news article. This dataset contains annotations for 5.3k documents and 38k paragraphs covering 3.2k unique entities."""

    VERSION = datasets.Version("1.1.0")
    LABELS = ["Negative", "Neutral", "Positive"]
    LABEL_COLS = ["TRUE_SENTIMENT"] + ["Paragraph" + str(i) for i in range(16)]

    def _info(self):
        label = datasets.features.ClassLabel(names=self.LABELS)
        feature_dict = {
            "DOCUMENT_INDEX": datasets.Value("int64"),
            "TITLE": datasets.Value("string"),
            "TARGET_ENTITY": datasets.Value("string"),
            "DOCUMENT": datasets.Value("string"),
            "MASKED_DOCUMENT": datasets.Value("string"),
        }
        feature_dict.update({k: label for k in self.LABEL_COLS})

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(feature_dict),
            supervised_keys=None,
            homepage="https://stonybrooknlp.github.io/PerSenT",
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        train_path = dl_manager.download(_URLs["train"])
        dev_path = dl_manager.download(_URLs["dev"])
        test_fixed_path = dl_manager.download(_URLs["test_fixed"])
        test_random_path = dl_manager.download(_URLs["test_random"])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": train_path,
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=NamedSplit("test_random"),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": test_random_path, "split": "test_random"},
            ),
            datasets.SplitGenerator(
                name=NamedSplit("test_fixed"),
                # These kwargs will be passed to _generate_examples
                gen_kwargs={"filepath": test_fixed_path, "split": "test_fixed"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": dev_path,
                    "split": "dev",
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """Yields examples.

        For examples with missing labels (empty strings in the original files), we replace with -1.
        """

        with open(filepath, encoding="utf-8") as f:
            reader = csv.reader(f)

            # Header
            _ = next(reader)

            for id_, row in enumerate(reader):
                doc_idx, title, target, doc, masked_doc, *labels = row

                # Replace missing labels with -1
                labels = [label if label in self.LABELS else -1 for label in labels]

                example = {
                    "DOCUMENT_INDEX": doc_idx,
                    "TITLE": title,
                    "TARGET_ENTITY": target,
                    "DOCUMENT": doc,
                    "MASKED_DOCUMENT": masked_doc,
                }
                example.update(dict(zip(self.LABEL_COLS, labels)))

                yield id_, example