Datasets:
financial_phrasebank

	# coding=utf-8
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	"""Financial Phrase Bank v1.0: Polar sentiment dataset of sentences from
	financial news. The dataset consists of 4840 sentences from English language
	financial news categorised by sentiment. The dataset is divided by agreement
	rate of 5-8 annotators."""


	import os

	import datasets


	_CITATION = """\
	@article{Malo2014GoodDO,
	title={Good debt or bad debt: Detecting semantic orientations in economic texts},
	author={P. Malo and A. Sinha and P. Korhonen and J. Wallenius and P. Takala},
	journal={Journal of the Association for Information Science and Technology},
	year={2014},
	volume={65}
	}
	"""

	_DESCRIPTION = """\
	The key arguments for the low utilization of statistical techniques in
	financial sentiment analysis have been the difficulty of implementation for
	practical applications and the lack of high quality training data for building
	such models. Especially in the case of finance and economic texts, annotated
	collections are a scarce resource and many are reserved for proprietary use
	only. To resolve the missing training data problem, we present a collection of
	∼ 5000 sentences to establish human-annotated standards for benchmarking
	alternative modeling techniques.

	The objective of the phrase level annotation task was to classify each example
	sentence into a positive, negative or neutral category by considering only the
	information explicitly available in the given sentence. Since the study is
	focused only on financial and economic domains, the annotators were asked to
	consider the sentences from the view point of an investor only; i.e. whether
	the news may have positive, negative or neutral influence on the stock price.
	As a result, sentences which have a sentiment that is not relevant from an
	economic or financial perspective are considered neutral.

	This release of the financial phrase bank covers a collection of 4840
	sentences. The selected collection of phrases was annotated by 16 people with
	adequate background knowledge on financial markets. Three of the annotators
	were researchers and the remaining 13 annotators were master’s students at
	Aalto University School of Business with majors primarily in finance,
	accounting, and economics.

	Given the large number of overlapping annotations (5 to 8 annotations per
	sentence), there are several ways to define a majority vote based gold
	standard. To provide an objective comparison, we have formed 4 alternative
	reference datasets based on the strength of majority agreement: all annotators
	agree, >=75% of annotators agree, >=66% of annotators agree and >=50% of
	annotators agree.
	"""

	_HOMEPAGE = "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news"

	_LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License"

	_REPO = "https://huggingface.co/datasets/financial_phrasebank/resolve/main/data"
	_URL = f"{_REPO}/FinancialPhraseBank-v1.0.zip"


	_VERSION = datasets.Version("1.0.0")


	class FinancialPhraseBankConfig(datasets.BuilderConfig):
	"""BuilderConfig for FinancialPhraseBank."""

	def __init__(
	self,
	split,
	**kwargs,
	):
	"""BuilderConfig for Discovery.
	Args:
	filename_bit: `string`, the changing part of the filename.
	"""

	super(FinancialPhraseBankConfig, self).__init__(name=f"sentences_{split}agree", version=_VERSION, **kwargs)

	self.path = os.path.join("FinancialPhraseBank-v1.0", f"Sentences_{split.title()}Agree.txt")


	class FinancialPhrasebank(datasets.GeneratorBasedBuilder):

	BUILDER_CONFIGS = [
	FinancialPhraseBankConfig(
	split="all",
	description="Sentences where all annotators agreed",
	),
	FinancialPhraseBankConfig(split="75", description="Sentences where at least 75% of annotators agreed"),
	FinancialPhraseBankConfig(split="66", description="Sentences where at least 66% of annotators agreed"),
	FinancialPhraseBankConfig(split="50", description="Sentences where at least 50% of annotators agreed"),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"sentence": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"negative",
	"neutral",
	"positive",
	]
	),
	}
	),
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={"filepath": os.path.join(data_dir, self.config.path)},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="iso-8859-1") as f:
	for id_, line in enumerate(f):
	sentence, label = line.rsplit("@", 1)
	yield id_, {"sentence": sentence, "label": label}