Financial Sentiment Dataset
This dataset contains financial news articles and social media posts labeled with sentiment scores. It is designed to train and evaluate models for sentiment analysis in the context of financial markets.
Dataset Details
- Data Sources: Financial news websites, social media platforms (e.g., Twitter, Reddit)
- Labels: Positive, Negative, Neutral
- Number of Samples: 50,000
- Languages: English
Dataset Structure
The dataset is structured as follows:
- text: The text of the financial news article or social media post.
- label: The sentiment label (Positive, Negative, Neutral).
Example:
| text | label |
|---|---|
| "The market is bullish today." | Positive |
| "The company's earnings report was poor." | Negative |
| "The stock price remained unchanged." | Neutral |
Usage
To use this dataset for training or evaluation, follow the instructions below:
from datasets import load_dataset
# Load the dataset
dataset = load_dataset('your_dataset_name')
# Print the first example
print(dataset['train'][0])
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