bpsad.py

# 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.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BPSAD -- Brazilian Portuguese Sentiment Analysis Datasets"""

import csv
import os
import datasets
import sys

from datasets import ClassLabel

csv.field_size_limit(sys.maxsize)


_HOMEPAGE = """\
https://www.kaggle.com/datasets/fredericods/ptbr-sentiment-analysis-datasets"""


_DESCRIPTION = """\
The Brazilian Portuguese Sentiment Analysis Dataset (BPSAD) is composed
by the concatenation of 5 differents sources (Olist, B2W Digital, Buscapé,
UTLC-Apps and UTLC-Movies), each one is composed by evaluation sentences
classified according to the polarity (0: negative; 1: positive) and ratings
(1, 2, 3, 4 and 5 stars)."""


_CITATION = """\
@inproceedings{souza2021sentiment,
    author={
        Souza, Frederico Dias and
        Baptista de Oliveira e Souza Filho, João},
    booktitle={
        2021 IEEE Latin American Conference on
        Computational Intelligence (LA-CCI)}, 
    title={
        Sentiment Analysis on Brazilian Portuguese User Reviews}, 
    year={2021},
    pages={1-6},
    doi={10.1109/LA-CCI48322.2021.9769838}
}
"""


_VERSION = datasets.Version("1.0.0")
_LICENSE = ""

        

class BPSAD(datasets.GeneratorBasedBuilder):
    """BPSAD dataset."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="polarity",
            description="Polarity classification dataset."
        ),
        datasets.BuilderConfig(
            name="rating",
            description="Rating classification dataset."
        ),
    ]

    @property
    def manual_download_instructions(self):
        return (
            "To use this dataset you have to download it manually:\n"
            "   1. Download the `concatenated` file from `{_HOMEPAGE}`.\n"
            "   2. Extract the file inside `[PATH_TO_FILE]`.\n"
            "   3. Load the dataset using the command:\n"
            "       datasets.load_dataset("
            "\"lm4pt/bpsad\", name=..., data_dir=\"[PATH_TO_FILE]\")\n\n"
            "Possible names are: `polarity` and `rating`."
        )

    def _info(self):
        # Note:
        # DEFAULT_CONFIG_NAME is not working and returns the value `default`.
        # Also, it is better to set the config name explicitly.

        if self.config.name not in ['polarity', 'rating']:
            raise ValueError((
                f"`{self.config.name}` is not a valid config name. Possible "
                "values are `polarity` and `rating`. Make sure to pass via "
                "`datasets.load_dataset('lm4pt/bpsad', name=...)`"
            ))

        if self.config.name == "polarity":
            features = datasets.Features({
                "review_text": datasets.Value("string"),
                "polarity": ClassLabel(
                    num_classes=2,
                    names=['negative', 'positive']
                ),
            })
        else:
            features = datasets.Features({
                "review_text": datasets.Value("string"),
                "rating": datasets.Value("int8"),
            })


        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            version=_VERSION,
        )


    def _split_generators(self, dl_manager):
        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

        # validates if dataset folder exists
        if not os.path.exists(data_dir):
            raise FileNotFoundError((
                data_dir + " does not exist. Make sure to pass the "
                "parameter `data_dir` via `datasets.load_dataset`.\n"
                "Manual download instructions:\n" +
                self.manual_download_instructions
            ))

        data_file = os.path.join(data_dir, "concatenated.csv")

        # check if dataset file exists
        if not os.path.exists(data_file):
            raise FileNotFoundError((
                data_file + " does not exist. " +
                self.manual_download_instructions
            ))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_file,
                    "split": "train",
                    'kfold_min': 1,
                    'kfold_max': 8
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": data_file,
                    "split": "dev",
                    'kfold_min': 9,
                    'kfold_max': 9
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": data_file,
                    "split": "test",
                    'kfold_min': 10,
                    'kfold_max': 10
                },
            ),
        ]


    def _generate_examples(self, filepath, split, kfold_min, kfold_max):
        # CSV columns
        # 0 - original_index,
        # 1 - review_text,
        # 2 - review_text_processed,
        # 3 - review_text_tokenized,
        # 4 - polarity,
        # 5 - rating,
        # 6 - kfold_polarity,
        # 7 - kfold_rating

        with open(filepath) as csv_file:
            csv_reader = csv.reader(csv_file, delimiter=',')
            
            # skip header
            _ = next(csv_reader)

            _id = 0
            if self.config.name == 'polarity':
                for row in csv_reader:
                    kfold = int(row[7])
                    if kfold_min <= kfold and kfold <= kfold_max:
                        yield _id, {
                            "review_text": row[2],
                            "polarity": int(float(row[5])),
                        }
                        _id += 1
            else:
                for row in csv_reader:
                    kfold = int(row[8])
                    if kfold_min <= kfold and kfold <= kfold_max:
                        yield _id, {
                            "review_text": row[2],
                            "rating": int(float(row[6])),
                        }
                        _id += 1