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""" |
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Filipino-Tagalog Product Reviews Sentiment Analysis |
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This is a machine learning dataset that can be used to analyze the sentiment of product reviews in Filipino-Tagalog. |
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The data is scraped from lazada Philippines. |
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""" |
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import os |
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from pathlib import Path |
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from typing import Dict, List, Tuple |
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import json |
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import datasets |
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from seacrowd.utils import schemas |
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from seacrowd.utils.configs import SEACrowdConfig |
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from seacrowd.utils.constants import Tasks, Licenses |
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_CITATION = """@misc{github, |
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author={Eric Echemane}, |
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title={Filipino-Tagalog-Product-Reviews-Sentiment-Analysis}, |
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year={2022}, |
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url={https://github.com/EricEchemane/Filipino-Tagalog-Product-Reviews-Sentiment-Analysis/tree/main}, |
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} |
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""" |
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_DATASETNAME = "lazada_review_filipino" |
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_DESCRIPTION = """Filipino-Tagalog Product Reviews Sentiment Analysis |
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This is a machine learning dataset that can be used to analyze the sentiment of product reviews in Filipino-Tagalog. |
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The dataset contains over 900+ weakly annotated Filipino reviews scraped from the Lazada Philippines platform. |
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Each review is associated with a five star point rating where one is the lowest and five is the highest. |
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""" |
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_HOMEPAGE = "https://github.com/EricEchemane/Filipino-Tagalog-Product-Reviews-Sentiment-Analysis" |
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_LANGUAGES = ['fil', 'tgl'] |
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_LICENSE = Licenses.UNKNOWN.value |
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_LOCAL = False |
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_URLS = { |
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_DATASETNAME: "https://raw.githubusercontent.com/EricEchemane/Filipino-Tagalog-Product-Reviews-Sentiment-Analysis/main/data/reviews.json", |
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} |
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_SUPPORTED_TASKS = [Tasks.SENTIMENT_ANALYSIS] |
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_SOURCE_VERSION = "1.0.0" |
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_SEACROWD_VERSION = "2024.06.20" |
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class LazadaReviewFilipinoDataset(datasets.GeneratorBasedBuilder): |
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"""The dataset contains over 900+ weakly annotated Filipino reviews scraped from the Lazada Philippines platform""" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION) |
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BUILDER_CONFIGS = [ |
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SEACrowdConfig( |
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name="lazada_review_filipino_source", |
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version=SOURCE_VERSION, |
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description="lazada reviews in filipino source schema", |
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schema="source", |
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subset_id="lazada_review_filipino", |
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), |
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SEACrowdConfig( |
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name="lazada_review_filipino_seacrowd_text", |
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version=SEACROWD_VERSION, |
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description="lazada reviews in filipino SEACrowd schema", |
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schema="seacrowd_text", |
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subset_id="lazada_review_filipino", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "lazada_review_filipino_source" |
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def _info(self) -> datasets.DatasetInfo: |
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if self.config.schema == "source": |
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features = datasets.Features({"index": datasets.Value("string"), "review": datasets.Value("string"), |
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"rating": datasets.Value("string")}) |
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elif self.config.schema == "seacrowd_text": |
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features = schemas.text_features(label_names=["1", "2", "3", "4", "5"]) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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"""Returns SplitGenerators.""" |
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urls = _URLS[_DATASETNAME] |
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data_dir = dl_manager.download_and_extract(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": data_dir, |
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"split": "train", |
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}, |
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) |
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] |
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def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]: |
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"""Yields examples as (key, example) tuples.""" |
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with open(filepath, 'r') as file: |
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data = json.load(file) |
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if self.config.schema == "source": |
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for i in range(len(data)): |
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yield i, {"index": str(i), "review": data[i]['review'], "rating": data[i]['rating']} |
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elif self.config.schema == "seacrowd_text": |
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for i in range(len(data)): |
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yield i, {"id": str(i), "text": data[i]['review'], "label": str(data[i]['rating'])} |
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