Wikipedia Analysis

URL → scrape → tokenize → top frequent words with visuals.

ROLE
Concept
STATUS
Published

OVERVIEW

URL → scrape → tokenize → top frequent words with visuals.

ARRIVED AS

Quickly summarize long articles.

This project emerged from a need to quickly understand the key themes and topics within lengthy Wikipedia articles during research sessions. Rather than reading thousands of words, the goal was to extract meaningful patterns through text analysis, identifying the most frequently used terms to reveal what the article emphasizes. The system needed to handle Wikipedia's complex HTML structure, process large volumes of text efficiently, and present insights visually for rapid comprehension.

WHAT I BUILT

  1. 01BeautifulSoup to scrape; tokenization + stopword removal.
  2. 02Frequency counts → charts; simple Flask UI.

WHAT CHANGED

  • Minutes to insights; handy for research & notes.

Data flow

click a stage

User submits Wikipedia article URL through the web interface. URL validation ensures it's a proper Wikipedia domain to prevent scraping arbitrary websites.

COMPONENT

No component mapped to this stage.

Decisions, with the cost of each.

A decision without its trade-off is marketing. Each row says what was chosen, why, and what it gave up.

Stateless Processing

Chose not to persist analysis results in a database. Each request is processed fresh, eliminating maintenance overhead and allowing the tool to remain lightweight and deployable anywhere. This tradeoff favors simplicity over features like historical analysis.

BeautifulSoup over API

Wikipedia offers an official API, but BeautifulSoup on raw HTML provided more control over content extraction, specifically the ability to exclude specific sections like references and external links that would skew frequency analysis with non-article terms.

Extended Stopword List

Standard NLTK stopwords weren't sufficient for Wikipedia content. Added domain-specific stopwords like 'wikipedia', 'cite', 'reference', 'retrieved', 'archive' to filter out meta-content that appears frequently but doesn't relate to article topics.

Top-N Frequency Display

Limited visualization to top 20-30 terms rather than showing all unique words. This decision focuses attention on the most significant themes while keeping charts readable. Long-tail words (appearing 1-2 times) add noise without insight.

The part that mattered.

The numbers behind the work, and the code that produced them.

Web Scraping with BeautifulSoup
def scrape_wikipedia(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.content, 'html.parser')

    # Extract main content, exclude references and nav
    content_div = soup.find('div', {'id': 'mw-content-text'})

    # Remove unwanted sections
    for unwanted in content_div.find_all(['table', 'sup', 'div'], class_=['infobox', 'reference', 'navbox']):
        unwanted.decompose()

    return content_div.get_text()
Text Processing Pipeline
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

def process_text(text):
    # Tokenize and normalize
    tokens = word_tokenize(text.lower())

    # Extended stopword list
    stop_words = set(stopwords.words('english'))
    wiki_stops = {'wikipedia', 'cite', 'reference', 'retrieved', 'archive', 'edit', 'page'}
    all_stops = stop_words.union(wiki_stops)

    # Filter and count
    filtered = [t for t in tokens if t.isalpha() and t not in all_stops]
    freq_dist = FreqDist(filtered)

    return freq_dist.most_common(25)
Flask API Endpoint
@app.route('/analyze', methods=['POST'])
def analyze():
    url = request.json.get('url')

    if not is_valid_wikipedia_url(url):
        return jsonify({'error': 'Invalid Wikipedia URL'}), 400

    try:
        text = scrape_wikipedia(url)
        top_words = process_text(text)

        return jsonify({
            'success': True,
            'data': {
                'words': [w[0] for w in top_words],
                'frequencies': [w[1] for w in top_words]
            }
        })
    except Exception as e:
        return jsonify({'error': str(e)}), 500

✓ LEARNED

  1. Generic NLP techniques require customization for specific domains. Wikipedia articles contain structural elements (citations, infoboxes, navigation) that must be filtered out. Building domain-aware stopword lists improved result quality dramatically, reducing noise from metadata terms that would otherwise dominate frequency counts.

  2. Web scraping is brittle. Wikipedia's HTML structure can change, breaking CSS selectors. The scraper needed defensive programming, checking if elements exist before accessing them, handling various page types (disambiguation, redirect, standard), and gracefully degrading when structure doesn't match expectations.

  3. Perfect linguistic analysis wasn't the goal, quick insights were. Chose simple tokenization over advanced NLP (stemming, lemmatization, named entity recognition) because speed and simplicity outweighed marginal accuracy gains. Sometimes 80% accuracy in milliseconds beats 95% accuracy in seconds.

  4. Raw frequency lists are hard to interpret. Visual charts transformed data into immediate understanding, users could glance at a bar chart and instantly grasp article themes. The visualization layer turned out to be as important as the analysis itself for delivering value.

  5. Processing individual Wikipedia articles works well, but the architecture doesn't scale to bulk analysis of hundreds of articles. If revisiting this project, I'd add caching, batch processing capabilities, and potentially move to Wikipedia dumps for large-scale corpus analysis rather than real-time scraping.