Auto-categorize RSS feed content

Auto-categorizing RSS feed content involves parsing the feed, analyzing the content of each item (title, description, content), and then assigning it to predefined or dynamically generated categories using natural language processing (NLP) techniques. Below is a breakdown of how to implement this process programmatically:

1. Fetch and Parse RSS Feed

Use an RSS parser to read the feed and extract useful data.

Python Example using feedparser:

python
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import feedparser

feed_url = 'https://example.com/rss'
feed = feedparser.parse(feed_url)

for entry in feed.entries:
    print(f"Title: {entry.title}")
    print(f"Summary: {entry.summary}")

2. Predefine Categories

Create a list of possible categories. These could be manually defined or learned from historical data.

python
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categories = ['Technology', 'Health', 'Politics', 'Finance', 'Sports', 'Entertainment', 'Science']

3. Clean and Prepare Text

Use NLP preprocessing to clean the text.

python
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import re

def clean_text(text):
    text = re.sub(r'<[^>]+>', '', text)  # Remove HTML tags
    text = re.sub(r's+', ' ', text)     # Normalize whitespace
    return text.strip()

4. Text Classification

Option A: Rule-Based Matching (Simple)

python
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def categorize_rule_based(text):
    text = text.lower()
    if 'bitcoin' in text or 'stock' in text:
        return 'Finance'
    elif 'vaccine' in text or 'covid' in text:
        return 'Health'
    elif 'ai' in text or 'software' in text:
        return 'Technology'
    else:
        return 'Uncategorized'

Option B: Machine Learning Classification (Advanced)

Train a model like MultinomialNB, LogisticRegression, or use transformer models like distilBERT.

python
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from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

# Example training data
train_texts = ['AI revolution', 'Vaccine update', 'Stock market crash']
train_labels = ['Technology', 'Health', 'Finance']

vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(train_texts)

clf = LogisticRegression()
clf.fit(X_train, train_labels)

# Predict
def categorize_ml(text):
    X = vectorizer.transform([text])
    return clf.predict(X)[0]

Option C: Zero-Shot Classification (Best for flexibility)

Use Hugging Face’s transformers (e.g., BART, BERT) for zero-shot classification:

python
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from transformers import pipeline

classifier = pipeline("zero-shot-classification")
labels = ['Technology', 'Health', 'Finance', 'Politics', 'Sports', 'Entertainment', 'Science']

def categorize_zero_shot(text):
    result = classifier(text, labels)
    return result['labels'][0]

5. Apply Categorization to RSS Feed Items

python
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for entry in feed.entries:
    text = clean_text(entry.title + " " + entry.summary)
    category = categorize_zero_shot(text)  # or rule-based / ML
    print(f"Title: {entry.title}")
    print(f"Category: {category}")

6. Optional: Store or Export Categorized Content

Store in a database or export to a CMS, spreadsheet, or file.

Summary of Tools & Libraries

• feedparser: Parse RSS feeds.

• scikit-learn: For traditional ML-based classification.

• transformers by Hugging Face: For zero-shot or fine-tuned deep learning classification.

• re, nltk, spacy: For text cleaning and preprocessing.

Let me know if you want a complete script, a version that stores to a database, or a web app interface.