Metadata-Version: 2.4
Name: finsenti
Version: 0.1.0
Summary: A package for financial sentiment analysis using news articles.
Home-page: https://github.com/vaibhavjha100/Sentiment-Analysis
Author: Vaibhav Jha
Author-email: vaibhavjha100@gmail.com
License: MIT
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development :: Libraries
Classifier: Intended Audience :: Financial and Insurance Industry
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: marketminer
Requires-Dist: google-genai
Requires-Dist: python-dotenv
Requires-Dist: transformers
Requires-Dist: torch
Requires-Dist: hf_xet
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# FinSenti

FinSenti is a comprehensive financial sentiment analysis library designed to help users analyze and interpret sentiment in financial texts.

It uses financial RoBERTa models to provide accurate sentiment analysis for financial news articles.

## Features
- **Financial Sentiment Analysis**: Analyze sentiment in financial texts using state-of-the-art RoBERTa models.
- **Compound Score Aggregation**: Aggregate sentiment scores to provide a comprehensive view of sentiment over time.

## Installation

```bash
pip install finsenti
```

## Usage

1. Preprocess your financial text data.

```python
import pandas as pd
from finsenti import finsenti_pipeline
# Input DataFrame df with a 'body' column and a list of tickers
df = pd.DataFrame({'body': ["The stock market is bullish today.", 
                             "Economic downturn expected next quarter.",
                            "Google's new product launch boosts investor confidence."]})
tickers = ['AAPL', 'GOOGL']
df = finsenti_pipeline(tickers=tickers, df=df,text_column='body', gemini_api_key='your_api_key', aggregate=True, aggregation_method='mean')
print(df.info())
```

## Dependencies
- pandas
- marketminer
- python-dotenv
- google-genai
- transformers
- torch
- hf_xet
- scipy

## License

MIT License. Free to use and modify.
