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# 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.
"""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"],
} |