finsenti 0.1.0

A package for financial sentiment analysis using news articles.

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

pip install finsenti

Usage

1. Preprocess your financial text data.

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.