Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
| 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 | |
| tags: | |
| - finance | |
| # 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. |