"""
SentimentEnhancedTextAnalysis
Generated by Eden via recursive self-improvement
2025-10-31 21:31:05.544982
"""

import numpy as np
from transformers import pipeline

# Initialize the pre-trained sentiment analysis model from Hugging Face
sentiment_analysis = pipeline("sentiment-analysis")

def enhance_sentiment(text):
    """
    Enhances the sentiment analysis by considering context, tone, and specific nuances.
    
    Parameters:
    text (str): The input text for which to perform enhanced sentiment analysis.
    
    Returns:
    dict: A dictionary containing the original sentiment score and the enhanced sentiment score.
    """
    # Analyze the initial sentiment of the provided text
    original_sentiment = sentiment_analysis(text)[0]
    original_score = float(original_sentiment['score'])
    label = original_sentiment['label']
    
    if 'positive' in label.lower():
        positive_multiplier = 1.2
    elif 'negative' in label.lower():
        positive_multiplier = 0.8
    else:
        positive_multiplier = 1
    
    # Custom function to calculate the sentiment score based on emotional words
    def custom_sentiment_score(text):
        emotions = ['happy', 'sad', 'angry', 'excited']
        word_count = len(text.split())
        emotional_word_count = sum([word in text.lower() for word in emotions])
        return (emotional_word_count / word_count) * 0.5 + 0.2
    
    enhanced_score = original_score * positive_multiplier + custom_sentiment_score(text)
    
    # Adjust the score to ensure it remains within a valid range
    enhanced_score = max(min(enhanced_score, 1), -1)
    
    return {
        'original': original_sentiment,
        'enhanced': {'score': enhanced_score, 'label': label}
    }

# Example usage:
text_to_analyze = "I am so happy and excited about the news! However, I'm also a bit sad because of some personal issues."
analysis_result = enhance_sentiment(text_to_analyze)
print(analysis_result)