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Financial News Sentiment Analysis Dataset
Data Science and Analytics
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About
This dataset provides fine-grained financial sentiment analysis on news headlines. It is a human-annotated collection of over 10,700 news headlines, specifically designed to address the challenging task of sentiment extraction in financial news where multiple entities may be present, often with conflicting sentiments. Over 2,800 headlines within the dataset feature multiple entities. The dataset is balanced, containing more than 4,100 positive entities, 3,200 negative entities, and 4,500 neutral entities. It is ideal for furthering research in entity-aware sentiment analysis and can be used for training models for extracting financial named entities.
Columns
- S No.: Represents the serial number for each entry.
- Title: Contains the news headlines.
- Decisions: Provides the sentiment annotations for various financial entities identified within the news headlines.
- Words: Indicates the number of words in each news headline.
Distribution
The dataset is provided as a data file, typically in CSV format. It contains over 10,700 distinct news headlines. The structure includes human-annotated sentiment labels for financial entities, with sentiments balanced across positive, negative, and neutral categories. The dataset includes headlines with varying word counts, and its sentiment annotations are quite diverse, representing 10,686 unique values for entity sentiments.
Usage
This dataset is well-suited for several applications:
- Performing Aspect-based Sentiment Analysis on financial texts.
- Training machine learning models for the extraction of named financial entities.
- Conducting research into fine-grained financial sentiment analysis.
- Validating the effect of news sentiments on aggregate market movements.
- Developing and evaluating learning schemes, including those utilising lexicon-based and pre-trained sentence representations, and various classification approaches.
Coverage
The dataset has a global regional coverage. While the listing date is 08/06/2025, the underlying research for SEntFiN 1.0 was published in 2022. The news headlines themselves do not specify a fixed time range within the provided information, focusing instead on the content and its annotations. No specific demographic scope is outlined, but the content is inherently focused on financial markets and related entities.
License
CC-BY
Who Can Use It
This dataset is beneficial for a range of users and purposes:
- Researchers focusing on financial Natural Language Processing (NLP) and sentiment analysis.
- Data Scientists and Analysts working on text classification, entity extraction, and market behaviour prediction.
- Anyone involved in Business or Finance seeking to understand or model sentiment from news.
- Developers of AI and Machine Learning models requiring human-annotated financial text data.
Dataset Name Suggestions
- SEntFiN 1.0
- Financial News Entity Sentiment Headlines
- Aspect-Based Financial Sentiment Data
- Annotated Financial News for NLP
- Financial News Sentiment Analysis Dataset
Attributes
Original Data Source: Aspect based Sentiment Analysis for Financial News
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