Smart News Aggregator – Cut Through the Noise with AI/

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We’re excited to share this comprehensive guide with you. This resource includes best practices, and real-world implementation strategies that we use at slashdev when building apps for clients worldwide.

What’s Inside This Guide:

• Why traditional news feeds fail you – and how AI curation fixes it
• The smart aggregation architecture – RSS parsing, sentiment analysis, and deduplication
• Production-ready Python code – feedparser, NLP models, and intelligent ranking
• Step-by-step deployment – from local testing to automated daily digests
• Key learnings – building context-aware content filters that actually work

Overview:

Your news feed is designed to keep you scrolling, not informed. Every outlet competes for clicks with sensational headlines, duplicate stories get recycled across dozens of sources, and the algorithm shows you what drives engagement – not what matters to you.

Here’s the uncomfortable truth: you’re not consuming news. You’re consuming noise optimized for ad revenue.

The Real Problem

A typical news consumer sees:

• 73% duplicate stories from different outlets saying the same thing
• Clickbait headlines engineered to trigger emotional reactions
• No context about why a story matters or how biased the source is
• Doomscroll design that surfaces negativity because fear keeps you engaged

You know this. You’ve felt it. That exhaustion after 30 minutes of “catching up on news” and realizing you learned nothing useful.

Smart aggregation flips this model. Instead of feeding you everything, it filters intelligently based on relevance, bias detection, sentiment analysis, and topic clustering.

The Solution: AI-Powered Curation

Traditional RSS readers just collect articles. Smart aggregators understand them.

Here’s what we’re building:

1. RSS parsing that pulls from trusted sources (BBC, Reuters, TechCrunch, Hacker News)
2. Sentiment analysis to flag overly negative or sensational headlines
3. Topic clustering to group related stories and eliminate duplicates
4. Bias detection to identify partisan language and one-sided framing
5. Relevance ranking based on your actual interests, not engagement metrics

The result? A personalized dashboard that shows 10 meaningful stories instead of 100 mediocre ones.

How It Actually Works

The architecture is surprisingly straightforward:

Step 1: Collection Feedparser pulls RSS feeds from your configured sources every hour. You’re not scraping websites – you’re using the standardized feeds they already publish.

Step 2: Analysis Each article runs through an NLP pipeline:

• Extract headline and summary text
• Analyze sentiment (positive/negative/neutral scores)
• Detect emotional manipulation keywords
• Generate embeddings for semantic similarity

Step 3: Filtering Articles get scored on multiple dimensions:

• Relevance to your topic preferences (tech, business, science)
• Sentiment balance (not too doom-y, not too promotional)
• Source credibility (you set the trust levels)
• Uniqueness (deduplicated against recent stories)

Step 4: Presentation Your dashboard shows the top-ranked articles with:

• Clean summaries (no clickbait rewrites)
• Source attribution and bias indicators
• Related story clusters
• Reading time estimates

Why This Matters Now

We’re drowning in information but starving for insight. The average person sees 5,000 marketing messages per day. News has become just another marketing channel.

This aggregator isn’t about filtering out perspectives you disagree with—it’s about removing manipulation, redundancy, and noise. You’ll still see multiple viewpoints on important stories. You just won’t see 47 identical takes on the same press release.

Practical Codes

1.RSS Feed Parser with Multi-Source Support

# news_fetcher.py
import feedparser
from datetime import datetime, timedelta
from typing import List, Dict
import hashlib

class NewsSource:
    def __init__(self, name: str, url: str, category: str, credibility: float):
        self.name = name
        self.url = url
        self.category = category
        self.credibility = credibility  # 0.0 to 1.0 score

SOURCES = [
    NewsSource("BBC News", "http://feeds.bbci.co.uk/news/rss.xml", "world", 0.9),
    NewsSource("Reuters", "https://www.reutersagency.com/feed/", "world", 0.95),
    NewsSource("TechCrunch", "https://techcrunch.com/feed/", "tech", 0.75),
    NewsSource("Hacker News", "https://hnrss.org/frontpage", "tech", 0.8),
    NewsSource("MIT Tech Review", "https://www.technologyreview.com/feed/", "tech", 0.85),
]

class Article:
    def __init__(self, title: str, link: str, summary: str, source: NewsSource, 
                 published: datetime):
        self.title = title
        self.link = link
        self.summary = summary
        self.source = source
        self.published = published
        self.content_hash = self._generate_hash()
    
    def _generate_hash(self) -> str:
        """Generate unique hash for deduplication"""
        content = f"{self.title}{self.summary}".lower()
        return hashlib.md5(content.encode()).hexdigest()

class NewsFetcher:
    def __init__(self, sources: List[NewsSource]):
        self.sources = sources
        self.seen_hashes = set()
    
    def fetch_all(self, hours_back: int = 24) -> List[Article]:
        """Fetch articles from all sources"""
        all_articles = []
        cutoff_time = datetime.now() - timedelta(hours=hours_back)
        
        for source in self.sources:
            try:
                articles = self._fetch_from_source(source, cutoff_time)
                all_articles.extend(articles)
                print(f"✓ Fetched {len(articles)} from {source.name}")
            except Exception as e:
                print(f"✗ Error fetching {source.name}: {e}")
        
        return all_articles
    
    def _fetch_from_source(self, source: NewsSource, 
                          cutoff: datetime) -> List[Article]:
        """Fetch and parse articles from single source"""
        feed = feedparser.parse(source.url)
        articles = []
        
        for entry in feed.entries:
            # Parse publish date
            published = datetime(*entry.published_parsed[:6]) \
                       if hasattr(entry, 'published_parsed') else datetime.now()
            
            if published < cutoff:
                continue
            
            # Extract content
            title = entry.get('title', 'No title')
            link = entry.get('link', '')
            summary = entry.get('summary', entry.get('description', ''))
            
            article = Article(title, link, summary, source, published)
            
            # Deduplicate
            if article.content_hash not in self.seen_hashes:
                articles.append(article)
                self.seen_hashes.add(article.content_hash)
        
        return articles

# Usage
if __name__ == "__main__":
    fetcher = NewsFetcher(SOURCES)
    articles = fetcher.fetch_all(hours_back=24)
    print(f"\nTotal unique articles: {len(articles)}")

2. Sentiment Analysis and Bias Detection

# sentiment_analyzer.py
from transformers import pipeline
import re
from typing import Dict, List

class SentimentAnalyzer:
    def __init__(self):
        # Using DistilBERT for efficiency
        self.sentiment_model = pipeline(
            "sentiment-analysis",
            model="distilbert-base-uncased-finetuned-sst-2-english"
        )
        
        # Emotional manipulation keywords
        self.clickbait_patterns = [
            r'\b(shocking|unbelievable|insane|mind-blowing)\b',
            r'\b(you won\'t believe|wait until you see)\b',
            r'\b(destroys|slams|blasts|crushes)\b',
            r'\d+ (things|reasons|ways|secrets)',
            r'this one (trick|weird trick)',
        ]
        
        # Bias indicator words
        self.bias_keywords = {
            'left': ['progressive', 'social justice', 'equity', 'systemic'],
            'right': ['traditional', 'freedom', 'patriot', 'liberty'],
            'sensational': ['unprecedented', 'explosive', 'bombshell', 'crisis']
        }
    
    def analyze(self, article) -> Dict:
        """Comprehensive sentiment and bias analysis"""
        text = f"{article.title}. {article.summary}"
        
        # Sentiment score
        sentiment_result = self.sentiment_model(text[:512])[0]
        sentiment_score = sentiment_result['score'] if sentiment_result['label'] == 'POSITIVE' else -sentiment_result['score']
        
        # Clickbait detection
        clickbait_score = self._detect_clickbait(article.title)
        
        # Bias detection
        bias_indicators = self._detect_bias(text)
        
        # Overall manipulation score
        manipulation_score = (clickbait_score * 0.6) + (abs(sentiment_score) * 0.4)
        
        return {
            'sentiment_score': sentiment_score,  # -1 to 1
            'sentiment_label': 'positive' if sentiment_score > 0.1 else 'negative' if sentiment_score < -0.1 else 'neutral',
            'clickbait_score': clickbait_score,  # 0 to 1
            'bias_indicators': bias_indicators,
            'manipulation_score': manipulation_score,  # 0 to 1
            'is_trustworthy': manipulation_score < 0.5
        }
    
    def _detect_clickbait(self, title: str) -> float:
        """Detect clickbait patterns in headlines"""
        title_lower = title.lower()
        matches = sum(1 for pattern in self.clickbait_patterns 
                     if re.search(pattern, title_lower, re.IGNORECASE))
        return min(matches * 0.3, 1.0)
    
    def _detect_bias(self, text: str) -> Dict[str, int]:
        """Count bias indicator keywords"""
        text_lower = text.lower()
        indicators = {}
        
        for bias_type, keywords in self.bias_keywords.items():
            count = sum(1 for keyword in keywords if keyword in text_lower)
            if count > 0:
                indicators[bias_type] = count
        
        return indicators

# Usage
analyzer = SentimentAnalyzer()
# result = analyzer.analyze(article)

3. Topic Clustering and Deduplication

# topic_clusterer.py
from sentence_transformers import SentenceTransformer
from sklearn.cluster import DBSCAN
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
from typing import List, Dict

class TopicClusterer:
    def __init__(self):
        # Lightweight embedding model
        self.model = SentenceTransformer('all-MiniLM-L6-v2')
    
    def cluster_articles(self, articles: List, similarity_threshold: float = 0.75) -> Dict:
        """Group similar articles into topic clusters"""
        if not articles:
            return {}
        
        # Generate embeddings
        texts = [f"{a.title}. {a.summary}" for a in articles]
        embeddings = self.model.encode(texts)
        
        # Cluster using DBSCAN
        clustering = DBSCAN(
            eps=1-similarity_threshold,
            min_samples=2,
            metric='cosine'
        ).fit(embeddings)
        
        # Organize into clusters
        clusters = {}
        for idx, label in enumerate(clustering.labels_):
            if label == -1:  # Noise/unique articles
                label = f"unique_{idx}"
            
            if label not in clusters:
                clusters[label] = []
            
            clusters[label].append(articles[idx])
        
        return self._rank_clusters(clusters, embeddings, clustering.labels_)
    
    def _rank_clusters(self, clusters: Dict, embeddings: np.ndarray, 
                      labels: np.ndarray) -> Dict:
        """Rank articles within each cluster by quality"""
        ranked_clusters = {}
        
        for cluster_id, cluster_articles in clusters.items():
            if len(cluster_articles) == 1:
                ranked_clusters[cluster_id] = cluster_articles
                continue
            
            # Get embeddings for this cluster
            indices = [i for i, label in enumerate(labels) 
                      if label == cluster_id or f"unique_{i}" == cluster_id]
            cluster_embeddings = embeddings[indices]
            
            # Calculate centroid
            centroid = np.mean(cluster_embeddings, axis=0)
            
            # Rank by similarity to centroid + source credibility
            scores = []
            for i, article in enumerate(cluster_articles):
                similarity = cosine_similarity(
                    [cluster_embeddings[i]], 
                    [centroid]
                )[0][0]
                credibility = article.source.credibility
                score = (similarity * 0.6) + (credibility * 0.4)
                scores.append((score, article))
            
            # Sort by score descending
            ranked_clusters[cluster_id] = [
                article for _, article in sorted(scores, reverse=True)
            ]
        
        return ranked_clusters
    
    def get_best_from_clusters(self, clusters: Dict, max_per_cluster: int = 1) -> List:
        """Extract top articles from each cluster"""
        best_articles = []
        
        for cluster_articles in clusters.values():
            best_articles.extend(cluster_articles[:max_per_cluster])
        
        return best_articles

# Usage
clusterer = TopicClusterer()
# clusters = clusterer.cluster_articles(articles)
# best = clusterer.get_best_from_clusters(clusters)

4. Complete News Aggregator Pipeline

# main.py
from news_fetcher import NewsFetcher, SOURCES
from sentiment_analyzer import SentimentAnalyzer
from topic_clusterer import TopicClusterer
from datetime import datetime
import json

class SmartNewsAggregator:
    def __init__(self, user_preferences: dict = None):
        self.fetcher = NewsFetcher(SOURCES)
        self.analyzer = SentimentAnalyzer()
        self.clusterer = TopicClusterer()
        self.preferences = user_preferences or {
            'categories': ['tech', 'world'],
            'max_negative_sentiment': -0.5,
            'min_credibility': 0.7,
            'max_articles': 15
        }
    
    def generate_daily_digest(self) -> dict:
        """Main pipeline: fetch → analyze → cluster → rank"""
        print("📰 Fetching articles...")
        articles = self.fetcher.fetch_all(hours_back=24)
        
        print("🔍 Analyzing content...")
        analyzed_articles = []
        for article in articles:
            analysis = self.analyzer.analyze(article)
            
            # Filter based on preferences
            if not self._meets_criteria(article, analysis):
                continue
            
            article.analysis = analysis
            analyzed_articles.append(article)
        
        print(f"✓ {len(analyzed_articles)} articles passed filters")
        
        print("🧩 Clustering topics...")
        clusters = self.clusterer.cluster_articles(analyzed_articles)
        best_articles = self.clusterer.get_best_from_clusters(clusters)
        
        # Final ranking
        ranked = self._final_ranking(best_articles)[:self.preferences['max_articles']]
        
        return self._format_digest(ranked)
    
    def _meets_criteria(self, article, analysis: dict) -> bool:
        """Check if article meets user preferences"""
        # Category filter
        if article.source.category not in self.preferences['categories']:
            return False
        
        # Sentiment filter
        if analysis['sentiment_score'] < self.preferences['max_negative_sentiment']:
            return False
        
        # Credibility filter
        if article.source.credibility < self.preferences['min_credibility']:
            return False
        
        # Quality filter
        if not analysis['is_trustworthy']:
            return False
        
        return True
    
    def _final_ranking(self, articles: List) -> List:
        """Final scoring based on multiple factors"""
        scored = []
        
        for article in articles:
            score = (
                article.source.credibility * 0.3 +
                (1 - article.analysis['manipulation_score']) * 0.3 +
                (article.analysis['sentiment_score'] + 1) / 2 * 0.2 +  # Normalize to 0-1
                0.2  # Recency bonus (could be time-based)
            )
            scored.append((score, article))
        
        return [article for _, article in sorted(scored, reverse=True)]
    
    def _format_digest(self, articles: List) -> dict:
        """Format for output/display"""
        return {
            'generated_at': datetime.now().isoformat(),
            'total_articles': len(articles),
            'articles': [
                {
                    'title': a.title,
                    'source': a.source.name,
                    'link': a.link,
                    'summary': a.summary[:200] + '...',
                    'published': a.published.isoformat(),
                    'sentiment': a.analysis['sentiment_label'],
                    'credibility': a.source.credibility,
                    'quality_score': round(a.analysis['manipulation_score'], 2)
                }
                for a in articles
            ]
        }

# Run the aggregator
if __name__ == "__main__":
    preferences = {
        'categories': ['tech', 'world'],
        'max_negative_sentiment': -0.3,
        'min_credibility': 0.75,
        'max_articles': 10
    }
    
    aggregator = SmartNewsAggregator(preferences)
    digest = aggregator.generate_daily_digest()
    
    print(f"\n📊 Your Smart News Digest ({digest['total_articles']} articles)")
    print("=" * 60)
    
    for i, article in enumerate(digest['articles'], 1):
        print(f"\n{i}. {article['title']}")
        print(f"   Source: {article['source']} | Sentiment: {article['sentiment']}")
        print(f"   {article['link']}")
    
    # Save to JSON
    with open('news_digest.json', 'w') as f:
        json.dump(digest, f, indent=2)
    
    print("\n✓ Digest saved to news_digest.json")

How to Run:

Initial Setup (One Time)

1. Install Dependencies

pip install feedparser transformers sentence-transformers scikit-learn torch

2. Download Required Models (runs automatically on first use)

python -c "from transformers import pipeline; pipeline('sentiment-analysis')"
python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')"

Running the Aggregator

Basic Usage:

python main.py

With Custom Preferences:

python
>>> from main import SmartNewsAggregator
>>> prefs = {'categories': ['tech'], 'max_articles': 5}
>>> aggregator = SmartNewsAggregator(prefs)
>>> digest = aggregator.generate_daily_digest()

Automated Daily Digest (cron job):

# Add to crontab (run daily at 7 AM)
0 7 * * * cd /path/to/project && python main.py

Testing Individual Components

Test RSS Fetching:

python news_fetcher.py

Test Sentiment Analysis:

python
>>> from sentiment_analyzer import SentimentAnalyzer
>>> from news_fetcher import Article, NewsSource
>>> analyzer = SentimentAnalyzer()
>>> # Create test article and analyze

Test Clustering:

python topic_clusterer.py

Output Format

The aggregator generates news_digest.json:

{
  "generated_at": "2024-12-11T09:00:00",
  "total_articles": 10,
  "articles": [...]
}

You can build a simple web dashboard or email digest using this JSON output.

Key Concepts

You’ve now built a production-ready news aggregator using four core AI techniques: RSS feed parsing with multi-source support for standardized content collection, transformer-based sentiment analysis for detecting emotional manipulation and clickbait, semantic embeddings with DBSCAN clustering for intelligent deduplication and topic grouping, and multi-factor ranking algorithms that balance credibility, quality, and relevance. Implementing this system teaches you practical NLP pipeline design, content filtering strategies, and how to build context-aware recommendation systems – giving you skills that apply to any content curation or information filtering challenge. The key is not just filtering content, but understanding how to evaluate information quality algorithmically and building systems that prioritize signal over noise.

Would you like to focus on the first detailed section, “Why traditional news feeds fail you?”

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