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from __future__ import absolute_import, division, print_function
import csv
import os
import datasets
_CITATION = """\
@article{go2009twitter,
title={Twitter sentiment classification using distant supervision},
author={Go, Alec and Bhayani, Richa and Huang, Lei},
journal={CS224N project report, Stanford},
volume={1},
number={12},
pages={2009},
year={2009}
}
"""
_DESCRIPTION = """\
Sentiment140 consists of Twitter messages with emoticons, which are used as noisy labels for
sentiment classification. For more detailed information please refer to the paper.
"""
_URL = "http://help.sentiment140.com/home"
_DATA_URL = "http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip"
_TEST_FILE_NAME = "testdata.manual.2009.06.14.csv"
_TRAIN_FILE_NAME = "training.1600000.processed.noemoticon.csv"
class Sentiment140Config(datasets.BuilderConfig):
"""BuilderConfig for Break"""
def __init__(self, data_url, **kwargs):
"""BuilderConfig for BlogAuthorship
Args:
data_url: `string`, url to the dataset (word or raw level)
**kwargs: keyword arguments forwarded to super.
"""
super(Sentiment140Config, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
self.data_url = data_url
class Sentiment140(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.1.0")
BUILDER_CONFIGS = [
Sentiment140Config(
name="sentiment140",
data_url=_DATA_URL,
description="sentiment classification dataset. Twitter messages are classified as either 'positive'=0, 'neutral'=1 or 'negative'=2.",
)
]
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"text": datasets.Value("string"),
"date": datasets.Value("string"),
"user": datasets.Value("string"),
"sentiment": datasets.Value("int32"),
"query": datasets.Value("string"),
}
),
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_URL,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_dir = dl_manager.download_and_extract(_DATA_URL)
test_csv_file = os.path.join(data_dir, _TEST_FILE_NAME)
train_csv_file = os.path.join(data_dir, _TRAIN_FILE_NAME)
if self.config.name == "sentiment140":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={"file_path": train_csv_file},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={"file_path": test_csv_file},
),
]
else:
raise NotImplementedError("{} does not exist".format(self.config.name))
def _generate_examples(self, file_path):
"""Yields examples."""
with open(file_path, encoding="ISO-8859-1") as f:
data = csv.reader(f, delimiter=",", quotechar='"')
for row_id, row in enumerate(data):
sentiment, tweet_id, date, query, user_name, message = row
yield "{}_{}".format(row_id, tweet_id), {
"text": message,
"date": date,
"user": user_name,
"sentiment": int(sentiment),
"query": query,
}
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