Metadata-Version: 2.4
Name: finsense-analyzer
Version: 0.0.2
Summary: A specialized sentiment analyzer for financial text, optimized for market jargon.
Author-email: Your Name <your.email@example.com>
License: MIT License
        
        Copyright (c) 2025 Nilay Jain
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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License-File: LICENSE.txt
Keywords: finance,market,nlp,sentiment,trading
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# FinSense Analyzer: Financial Sentiment Tool

**FinSense** is a lightweight Python package specialized for analyzing sentiment in financial text. It uses a custom, finance-tuned lexicon to overcome the limitations of general-purpose sentiment tools when dealing with market jargon and financial concepts.

## Installation

```bash
pip install finsense-analyzer
from finsense import FinSenseAnalyzer

# 1. Initialize the analyzer
analyzer = FinSenseAnalyzer()

# 2. Define test sentences
text_positive = "The stock rallied strongly today on excellent profits."
text_negative = "Q4 earnings missed estimates, causing large losses."
text_negation = "The team did not see major losses this quarter."

# 3. Analyze and print results
print("--- Analysis Results ---")
print(f"Positive Score: {analyzer.analyze_sentiment(text_positive)['polarity']:.4f}")
print(f"Negative Score: {analyzer.analyze_sentiment(text_negative)['polarity']:.4f}")
print(f"Negation Score: {analyzer.analyze_sentiment(text_negation)['polarity']:.4f}")