thanakritbright/PTH_DATA

"""Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""

import json import os

import datasets from datasets.tasks import TextClassification

_CITATION = """
@software{bact_2019_3457447, author = {Suriyawongkul, Arthit and Chuangsuwanich, Ekapol and Chormai, Pattarawat and Polpanumas, Charin}, title = {PyThaiNLP/wisesight-sentiment: First release}, month = sep, year = 2019, publisher = {Zenodo}, version = {v1.0}, doi = {10.5281/zenodo.3457447}, url = {https://doi.org/10.5281/zenodo.3457447} } """

_DESCRIPTION = """
Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)

• Released to public domain under Creative Commons Zero v1.0 Universal license.
• Category (Labels): {"pos": 0, "neu": 1, "neg": 2, "q": 3}
• Size: 26,737 messages
• Language: Central Thai
• Style: Informal and conversational. With some news headlines and advertisement.
• Time period: Around 2016 to early 2019. With small amount from other period.
• Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
• Privacy:
  • Only messages that made available to the public on the internet (websites, blogs, social network sites).
  • For Facebook, this means the public comments (everyone can see) that made on a public page.
  • Private/protected messages and messages in groups, chat, and inbox are not included.
• Alternations and modifications:
  • Keep in mind that this corpus does not statistically represent anything in the language register.
  • Large amount of messages are not in their original form. Personal data are removed or masked.
  • Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact. (Mis)spellings are kept intact.
  • Messages longer than 2,000 characters are removed.
  • Long non-Thai messages are removed. Duplicated message (exact match) are removed.
• More characteristics of the data can be explore: https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb """

class WisesightSentimentConfig(datasets.BuilderConfig): """BuilderConfig for WisesightSentiment."""

def __init__(self, **kwargs):
    """BuilderConfig for WisesightSentiment.
    Args:
      **kwargs: keyword arguments forwarded to super.
    """
    super(WisesightSentimentConfig, self).__init__(**kwargs)

class WisesightSentiment(datasets.GeneratorBasedBuilder): """Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""

_DOWNLOAD_URL = "https://github.com/PyThaiNLP/wisesight-sentiment/raw/master/huggingface/data.zip"
_TRAIN_FILE = "train.jsonl"
_VAL_FILE = "valid.jsonl"
_TEST_FILE = "test.jsonl"

BUILDER_CONFIGS = [
    WisesightSentimentConfig(
        name="wisesight_sentiment",
        version=datasets.Version("1.0.0"),
        description="Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)",
    ),
]

def _info(self):
    return datasets.DatasetInfo(
        description=_DESCRIPTION,
        features=datasets.Features(
            {
                "texts": datasets.Value("string"),
                "category": datasets.features.ClassLabel(names=["pos", "neu", "neg", "q"]),
            }
        ),
        supervised_keys=None,
        homepage="https://github.com/PyThaiNLP/wisesight-sentiment",
        citation=_CITATION,
        task_templates=[TextClassification(text_column="texts", label_column="category")],
    )

def _split_generators(self, dl_manager):
    arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL)
    data_dir = os.path.join(arch_path, "data")
    return [
        datasets.SplitGenerator(
            name=datasets.Split.TRAIN,
            gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FILE)},
        ),
        datasets.SplitGenerator(
            name=datasets.Split.VALIDATION,
            gen_kwargs={"filepath": os.path.join(data_dir, self._VAL_FILE)},
        ),
        datasets.SplitGenerator(
            name=datasets.Split.TEST,
            gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FILE)},
        ),
    ]

def _generate_examples(self, filepath):
    """Generate WisesightSentiment examples."""
    with open(filepath, encoding="utf-8") as f:
        for id_, row in enumerate(f):
            data = json.loads(row)
            texts = data["texts"]
            category = data["category"]
            yield id_, {"texts": texts, "category": category}