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"""B2W-Reviews01 dataset""" |
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import datasets |
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import pandas as pd |
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_CITATION = """ |
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@inproceedings{real2019b2w, |
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title={B2W-reviews01: an open product reviews corpus}, |
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author={Real, Livy and Oshiro, Marcio and Mafra, Alexandre}, |
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booktitle={STIL-Symposium in Information and Human Language Technology}, |
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year={2019} |
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} |
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""" |
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_DESCRIPTION = """ |
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B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the Americanas.com website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates""" |
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_URLS = { |
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"train": "https://raw.githubusercontent.com/americanas-tech/b2w-reviews01/4639429ec698d7821fc99a0bc665fa213d9fcd5a/B2W-Reviews01.csv" |
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} |
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class Reviews(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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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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dict( |
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submission_date=datasets.Value("string"), |
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reviewer_id=datasets.Value("string"), |
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product_id=datasets.Value("string"), |
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product_name=datasets.Value("string"), |
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product_brand=datasets.Value("string"), |
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site_category_lv1=datasets.Value("string"), |
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site_category_lv2=datasets.Value("string"), |
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review_title=datasets.Value("string"), |
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overall_rating=datasets.Value("int32"), |
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recommend_to_a_friend=datasets.Value("string"), |
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review_text=datasets.Value("string"), |
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reviewer_birth_year=datasets.Value("int32"), |
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reviewer_gender=datasets.Value("string"), |
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reviewer_state=datasets.Value("string"))), |
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supervised_keys=None, |
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homepage="https://github.com/americanas-tech/b2w-reviews01", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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downloaded_files = dl_manager.download(_URLS) |
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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={ |
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"filepath": downloaded_files["train"], |
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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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def process_row(row): |
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row["overall_rating"] = int(row["overall_rating"]) |
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try: |
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row["reviewer_birth_year"] = int(float(row["reviewer_birth_year"])) |
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except ValueError: |
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row["reviewer_birth_year"] = None |
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return row |
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records = pd.read_csv(filepath).to_dict("records") |
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for idx, row in enumerate(records): |
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yield idx, process_row(row) |
