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  1. wisesight_sentiment.py +0 -119
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- """Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""
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-
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-
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- import json
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- import os
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-
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- import datasets
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- from datasets.tasks import TextClassification
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-
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-
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- _CITATION = """\
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- @software{bact_2019_3457447,
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- author = {Suriyawongkul, Arthit and
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- Chuangsuwanich, Ekapol and
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- Chormai, Pattarawat and
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- Polpanumas, Charin},
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- title = {PyThaiNLP/wisesight-sentiment: First release},
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- month = sep,
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- year = 2019,
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- publisher = {Zenodo},
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- version = {v1.0},
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- doi = {10.5281/zenodo.3457447},
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- url = {https://doi.org/10.5281/zenodo.3457447}
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- }
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- """
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-
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- _DESCRIPTION = """\
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- Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)
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- * Released to public domain under Creative Commons Zero v1.0 Universal license.
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- * Category (Labels): {"pos": 0, "neu": 1, "neg": 2, "q": 3}
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- * Size: 26,737 messages
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- * Language: Central Thai
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- * Style: Informal and conversational. With some news headlines and advertisement.
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- * Time period: Around 2016 to early 2019. With small amount from other period.
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- * Domains: Mixed. Majority are consumer products and services (restaurants, cosmetics, drinks, car, hotels), with some current affairs.
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- * Privacy:
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- * Only messages that made available to the public on the internet (websites, blogs, social network sites).
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- * For Facebook, this means the public comments (everyone can see) that made on a public page.
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- * Private/protected messages and messages in groups, chat, and inbox are not included.
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- * Alternations and modifications:
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- * Keep in mind that this corpus does not statistically represent anything in the language register.
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- * Large amount of messages are not in their original form. Personal data are removed or masked.
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- * Duplicated, leading, and trailing whitespaces are removed. Other punctuations, symbols, and emojis are kept intact.
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- (Mis)spellings are kept intact.
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- * Messages longer than 2,000 characters are removed.
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- * Long non-Thai messages are removed. Duplicated message (exact match) are removed.
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- * More characteristics of the data can be explore: https://github.com/PyThaiNLP/wisesight-sentiment/blob/master/exploration.ipynb
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- """
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-
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-
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- class WisesightSentimentConfig(datasets.BuilderConfig):
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- """BuilderConfig for WisesightSentiment."""
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-
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- def __init__(self, **kwargs):
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- """BuilderConfig for WisesightSentiment.
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-
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- Args:
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- **kwargs: keyword arguments forwarded to super.
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- """
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- super(WisesightSentimentConfig, self).__init__(**kwargs)
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-
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-
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- class WisesightSentiment(datasets.GeneratorBasedBuilder):
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- """Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)"""
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-
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- _DOWNLOAD_URL = "https://github.com/PyThaiNLP/wisesight-sentiment/raw/master/huggingface/data.zip"
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- _TRAIN_FILE = "train.jsonl"
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- _VAL_FILE = "valid.jsonl"
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- _TEST_FILE = "test.jsonl"
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-
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- BUILDER_CONFIGS = [
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- WisesightSentimentConfig(
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- name="wisesight_sentiment",
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- version=datasets.Version("1.0.0"),
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- description="Wisesight Sentiment Corpus: Social media messages in Thai language with sentiment category (positive, neutral, negative, question)",
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- ),
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- ]
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-
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- def _info(self):
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- return datasets.DatasetInfo(
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- description=_DESCRIPTION,
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- features=datasets.Features(
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- {
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- "texts": datasets.Value("string"),
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- "category": datasets.features.ClassLabel(names=["pos", "neu", "neg", "q"]),
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- }
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- ),
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- supervised_keys=None,
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- homepage="https://github.com/PyThaiNLP/wisesight-sentiment",
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- citation=_CITATION,
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- task_templates=[TextClassification(text_column="texts", label_column="category")],
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- )
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-
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- def _split_generators(self, dl_manager):
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- arch_path = dl_manager.download_and_extract(self._DOWNLOAD_URL)
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- data_dir = os.path.join(arch_path, "data")
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- return [
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- datasets.SplitGenerator(
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- name=datasets.Split.TRAIN,
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- gen_kwargs={"filepath": os.path.join(data_dir, self._TRAIN_FILE)},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.VALIDATION,
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- gen_kwargs={"filepath": os.path.join(data_dir, self._VAL_FILE)},
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- ),
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- datasets.SplitGenerator(
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- name=datasets.Split.TEST,
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- gen_kwargs={"filepath": os.path.join(data_dir, self._TEST_FILE)},
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- ),
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- ]
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-
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- def _generate_examples(self, filepath):
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- """Generate WisesightSentiment examples."""
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- with open(filepath, encoding="utf-8") as f:
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- for id_, row in enumerate(f):
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- data = json.loads(row)
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- texts = data["texts"]
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- category = data["category"]
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- yield id_, {"texts": texts, "category": category}