backend/sentiment_analysis.py

```python
from typing import Dict, Any, List
import feedparser
from .models_registry import model_registry

CRYPTO_NEWS_RSS = [
    "https://cryptopanic.com/news/rss/",
    "https://cointelegraph.com/rss",
    "https://news.bitcoin.com/feed/"
]

async def get_crypto_sentiment(symbol: str = "BTC") -> Dict[str, Any]:
    headlines = await fetch_crypto_headlines(symbol)
    if not headlines:
        return {"score": 0, "confidence": 0, "headlines": []}
    
    sentiment = await model_registry.analyze_sentiment(headlines)
    return normalize_sentiment(sentiment, headlines)

async def fetch_crypto_headlines(symbol: str) -> List[str]:
    headlines = []
    for url in CRYPTO_NEWS_RSS:
        try:
            feed = feedparser.parse(url)
            for entry in feed.entries:
                if symbol.lower() in entry.title.lower() or symbol.lower() in entry.description.lower():
                    headlines.append(entry.title)
                    if len(headlines) >= 10:  # Limit to 10 headlines
                        return headlines
        except Exception:
            continue
    return headlines

def normalize_sentiment(raw_sentiment: Dict[str, Any], headlines: List[str]) -> Dict[str, Any]:
    positive = 0
    negative = 0
    neutral = 0
    
    for item in raw_sentiment.get("sentiment", []):
        label = item.get("label", "").lower()
        if "positive" in label:
            positive += 1
        elif "negative" in label:
            negative += 1
        else:
            neutral += 1
    
    total = len(headlines)
    if total == 0:
        return {"score": 0, "confidence": 0, "headlines": headlines}
    
    score = (positive - negative) / total
    confidence = max(positive, negative, neutral) / total
    
    return {
        "score": score,
        "confidence": confidence,
        "bullish": positive / total,
        "bearish": negative / total,
        "neutral": neutral / total,
        "headlines": headlines
    }
```