twitter-sentiment-analysis.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
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""Twitter Sentiment Analysis Training Corpus (Dataset)"""

import json
import os

import datasets
from datasets import load_dataset


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@InProceedings{thinknook:dataset,
title = {Twitter Sentiment Analysis Training Corpus (Dataset)},
author={Ibrahim Naji},
year={2012}
}
"""

_DESCRIPTION = """\
The Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment.
The dataset is based on data from the following two sources:

University of Michigan Sentiment Analysis competition on Kaggle
Twitter Sentiment Corpus by Niek Sanders

Finally, I randomly selected a subset of them, applied a cleaning process, and divided them between the test and train subsets, keeping a balance between
the number of positive and negative tweets within each of these subsets.
"""


_URL = "https://raw.githubusercontent.com/cblancac/SentimentAnalysisBert/main/data/"
_URLS = {
    "train": _URL + "train_150k.txt",
    "test": _URL + "test_62k.txt",
}

_HOMEPAGE = "https://raw.githubusercontent.com/cblancac/SentimentAnalysisBert/main"


def _define_columns(example):
    text_splited = example["text"].split('\t')
    return {"text": text_splited[1].strip(), "feeling": int(text_splited[0])}


class NewDataset(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "text": datasets.Value("string"),
                "feeling": datasets.Value("int32")
            }
        )
        return datasets.DatasetInfo(
            # This is description will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,
            homepage=_HOMEPAGE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):

        data_dir_files = dl_manager.download_and_extract(_URLS)
        data_dir = '/'.join(data_dir_files["train"].split('/')[:-1])

        data = load_dataset("text", data_files=data_dir_files)
        data = data.map(_define_columns)

        texts_dataset_clean = data["train"].train_test_split(train_size=0.8, seed=42)
        # Rename the default "test" split to "validation"
        texts_dataset_clean["validation"] = texts_dataset_clean.pop("test")
        # Add the "test" set to our `DatasetDict`
        texts_dataset_clean["test"] = data["test"]
        texts_dataset_clean
        
        for split, dataset in texts_dataset_clean.items():
            dataset.to_json(data_dir + "/" + f"twitter-sentiment-analysis-{split}.jsonl")

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "twitter-sentiment-analysis-train.jsonl")}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "twitter-sentiment-analysis-validation.jsonl")}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "twitter-sentiment-analysis-test.jsonl")}),
        ]   


    def _generate_examples(self, filepath):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                yield key, {
                    "text": data["text"],
                    "feeling": data["feeling"],
                }