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sentiment140 / sentiment140.py

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Update files from the datasets library (from 1.0.0)

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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,
	}