Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
finance
License:
# coding=utf-8 | |
# 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. | |
"""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} | |