|
--- |
|
annotations_creators: |
|
- expert-generated |
|
language_creators: |
|
- found |
|
language: |
|
- en |
|
license: |
|
- cc-by-nc-sa-3.0 |
|
multilinguality: |
|
- monolingual |
|
size_categories: |
|
- 1K<n<10K |
|
source_datasets: |
|
- original |
|
task_categories: |
|
- text-classification |
|
task_ids: |
|
- multi-class-classification |
|
- sentiment-classification |
|
pretty_name: FinancialPhrasebank |
|
dataset_info: |
|
- config_name: sentences_allagree |
|
features: |
|
- name: sentence |
|
dtype: string |
|
- name: label |
|
dtype: |
|
class_label: |
|
names: |
|
'0': negative |
|
'1': neutral |
|
'2': positive |
|
splits: |
|
- name: train |
|
num_bytes: 303371 |
|
num_examples: 2264 |
|
download_size: 681890 |
|
dataset_size: 303371 |
|
- config_name: sentences_75agree |
|
features: |
|
- name: sentence |
|
dtype: string |
|
- name: label |
|
dtype: |
|
class_label: |
|
names: |
|
'0': negative |
|
'1': neutral |
|
'2': positive |
|
splits: |
|
- name: train |
|
num_bytes: 472703 |
|
num_examples: 3453 |
|
download_size: 681890 |
|
dataset_size: 472703 |
|
- config_name: sentences_66agree |
|
features: |
|
- name: sentence |
|
dtype: string |
|
- name: label |
|
dtype: |
|
class_label: |
|
names: |
|
'0': negative |
|
'1': neutral |
|
'2': positive |
|
splits: |
|
- name: train |
|
num_bytes: 587152 |
|
num_examples: 4217 |
|
download_size: 681890 |
|
dataset_size: 587152 |
|
- config_name: sentences_50agree |
|
features: |
|
- name: sentence |
|
dtype: string |
|
- name: label |
|
dtype: |
|
class_label: |
|
names: |
|
'0': negative |
|
'1': neutral |
|
'2': positive |
|
splits: |
|
- name: train |
|
num_bytes: 679240 |
|
num_examples: 4846 |
|
download_size: 681890 |
|
dataset_size: 679240 |
|
--- |
|
|
|
# Dataset Card for financial_phrasebank |
|
|
|
## Table of Contents |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [Dataset Creation](#dataset-creation) |
|
- [Curation Rationale](#curation-rationale) |
|
- [Source Data](#source-data) |
|
- [Annotations](#annotations) |
|
- [Personal and Sensitive Information](#personal-and-sensitive-information) |
|
- [Considerations for Using the Data](#considerations-for-using-the-data) |
|
- [Social Impact of Dataset](#social-impact-of-dataset) |
|
- [Discussion of Biases](#discussion-of-biases) |
|
- [Other Known Limitations](#other-known-limitations) |
|
- [Additional Information](#additional-information) |
|
- [Dataset Curators](#dataset-curators) |
|
- [Licensing Information](#licensing-information) |
|
- [Citation Information](#citation-information) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news) [ResearchGate](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10) |
|
- **Repository:** |
|
- **Paper:** [Arxiv](https://arxiv.org/abs/1307.5336) |
|
- **Leaderboard:** [Kaggle](https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news/code) [PapersWithCode](https://paperswithcode.com/sota/sentiment-analysis-on-financial-phrasebank) = |
|
- **Point of Contact:** [Pekka Malo](mailto:pekka.malo@aalto.fi) [Ankur Sinha](mailto:ankur.sinha@aalto.fi) |
|
|
|
### Dataset Summary |
|
|
|
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. |
|
|
|
### Supported Tasks and Leaderboards |
|
|
|
Sentiment Classification |
|
|
|
### Languages |
|
|
|
English |
|
|
|
## Dataset Structure |
|
|
|
### Data Instances |
|
|
|
``` |
|
{ "sentence": "Pharmaceuticals group Orion Corp reported a fall in its third-quarter earnings that were hit by larger expenditures on R&D and marketing .", |
|
"label": "negative" |
|
} |
|
``` |
|
|
|
### Data Fields |
|
|
|
- sentence: a tokenized line from the dataset |
|
- label: a label corresponding to the class as a string: 'positive', 'negative' or 'neutral' |
|
|
|
### Data Splits |
|
There's no train/validation/test split. |
|
|
|
However the dataset is available in four possible configurations depending on the percentage of agreement of annotators: |
|
|
|
`sentences_50agree`; Number of instances with >=50% annotator agreement: 4846 |
|
`sentences_66agree`: Number of instances with >=66% annotator agreement: 4217 |
|
`sentences_75agree`: Number of instances with >=75% annotator agreement: 3453 |
|
`sentences_allagree`: Number of instances with 100% annotator agreement: 2264 |
|
|
|
## Dataset Creation |
|
|
|
### Curation Rationale |
|
|
|
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. |
|
|
|
### Source Data |
|
|
|
#### Initial Data Collection and Normalization |
|
|
|
The corpus used in this paper is made out of English news on all listed |
|
companies in OMX Helsinki. The news has been downloaded from the LexisNexis |
|
database using an automated web scraper. Out of this news database, a random |
|
subset of 10,000 articles was selected to obtain good coverage across small and |
|
large companies, companies in different industries, as well as different news |
|
sources. Following the approach taken by Maks and Vossen (2010), we excluded |
|
all sentences which did not contain any of the lexicon entities. This reduced |
|
the overall sample to 53,400 sentences, where each has at least one or more |
|
recognized lexicon entity. The sentences were then classified according to the |
|
types of entity sequences detected. Finally, a random sample of ∼5000 sentences |
|
was chosen to represent the overall news database. |
|
|
|
#### Who are the source language producers? |
|
|
|
The source data was written by various financial journalists. |
|
|
|
### Annotations |
|
|
|
#### Annotation process |
|
|
|
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. |
|
|
|
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: |
|
|
|
#### Who are the annotators? |
|
|
|
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. |
|
|
|
### Personal and Sensitive Information |
|
|
|
[More Information Needed] |
|
|
|
## Considerations for Using the Data |
|
|
|
### Social Impact of Dataset |
|
|
|
[More Information Needed] |
|
|
|
### Discussion of Biases |
|
|
|
All annotators were from the same institution and so interannotator agreement |
|
should be understood with this taken into account. |
|
|
|
### Other Known Limitations |
|
|
|
[More Information Needed] |
|
|
|
## Additional Information |
|
|
|
### Dataset Curators |
|
|
|
[More Information Needed] |
|
|
|
### Licensing Information |
|
|
|
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/. |
|
|
|
If you are interested in commercial use of the data, please contact the following authors for an appropriate license: |
|
- [Pekka Malo](mailto:pekka.malo@aalto.fi) |
|
- [Ankur Sinha](mailto:ankur.sinha@aalto.fi) |
|
|
|
### Citation Information |
|
|
|
``` |
|
@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} |
|
} |
|
``` |
|
|
|
### Contributions |
|
|
|
Thanks to [@frankier](https://github.com/frankier) for adding this dataset. |
