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Labelled Market Sentiment Analysis Dataset
Data Science and Analytics
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About
This dataset is designed to advance labelled financial sentiment analysis research. It combines two notable datasets, FiQA and Financial PhraseBank, into a single, easy-to-use CSV file. The primary purpose is to provide financial sentences accompanied by their corresponding sentiment labels, which can be positive, negative, or neutral. This resource is valuable for understanding market and corporate sentiment expressed in textual data.
Columns
The dataset is structured with at least two key columns:
- Sentence: This column contains the textual financial statement or phrase.
- Sentiment Label: This column provides the associated sentiment of the sentence, categorised as 'positive', 'negative', or 'neutral'.
Distribution
The dataset is provided in a CSV file format. It organises financial sentences with their assigned sentiment labels. Specific details regarding the exact number of rows or records are not available in the provided information.
Usage
This dataset is ideal for various applications and use cases, including:
- Developing and testing Natural Language Processing (NLP) models for sentiment detection in financial texts.
- Conducting data science and analytics projects focused on market dynamics and corporate communications.
- Building tools for business intelligence to gauge sentiment from financial news and reports.
- Academic research into the nuances of economic language and its emotional tone.
Coverage
The dataset's regional scope is global. The financial sentences included refer to various companies and market events, with examples from periods such as 2008 and 2010. While a precise time range for all data points is not specified, the content is relevant to corporate financial and market sentiment over several years. There are no specific notes on demographic scope; the focus is on business and financial entities.
License
CCO
Who Can Use It
This dataset is particularly suited for:
- Researchers keen on exploring financial sentiment analysis techniques and models.
- Data Scientists working on machine learning applications for textual data in the finance domain.
- Financial Analysts looking to integrate sentiment indicators into their market assessments.
- Developers creating applications that require understanding the emotional tone of financial statements.
Dataset Name Suggestions
- Financial Sentence Sentiment Corpus
- Global Financial Sentiment Labeled Data
- Market Sentiment Analysis Dataset
- Corporate Financial Text Sentiment
Attributes
Original Data Source:Financial Sentiment Analysis
QUALITY