advanced-slack-mining

name	advanced-slack-mining
description	Mine Slack data for embedded URLs, mentioned products, competitive intelligence, and cross-referencing with external data sources.

Advanced Slack Data Mining

Overview

This skill covers sophisticated techniques for extracting data from Slack messages including embedded URLs, product mentions, competitive intelligence, and correlating with external data sources.

URL Extraction from Messages

Basic URL Pattern Matching

import re

def extract_all_urls(text):
    """Extract all URLs from text using regex"""
    url_pattern = r'https?://[^\s\)\"\']+|<(https?://[^\|>]+)[^\>]*>'

    matches = re.findall(url_pattern, text)
    urls = []

    for match in matches:
        if isinstance(match, tuple):
            urls.append(match[1] if match[1] else match[0])
        else:
            urls.append(match)

    return urls

# Slack-formatted URLs are often like: <https://example.com|Text>
def extract_slack_formatted_urls(text):
    """Extract URLs from Slack format"""
    pattern = r'<(https?://[^\|>]+)\|([^>]+)>'
    matches = re.findall(pattern, text)
    return [url for url, _ in matches]

Categorizing URLs by Type

def categorize_urls(urls):
    """Categorize URLs by domain/type"""
    categories = {
        'demo': [],
        'documentation': [],
        'blog': [],
        'github': [],
        'slack_internal': [],
        'other': []
    }

    for url in urls:
        url_lower = url.lower()

        if 'demo' in url_lower:
            categories['demo'].append(url)
        elif 'docs' in url_lower or 'documentation' in url_lower:
            categories['documentation'].append(url)
        elif 'blog' in url_lower or 'medium' in url_lower or 'towards' in url_lower:
            categories['blog'].append(url)
        elif 'github' in url_lower:
            categories['github'].append(url)
        elif 'slack.com/archives' in url_lower:
            categories['slack_internal'].append(url)
        else:
            categories['other'].append(url)

    return categories

Competitive Product Intelligence

Identifying Competitor Mentions

def extract_competitor_mentions(slack_data, competitors_dict):
    """
    competitors_dict: {
        'CompetitorName': {
            'keywords': ['keyword1', 'keyword2'],
            'discussers': set(),
            'topics': [],
            'quotes': []
        }
    }
    """
    results = {name: dict(comp) for name, comp in competitors_dict.items()}

    for msg in slack_data:
        msg_text = msg.get('Message', {}).get('User', {}).get('text', '')
        msg_user = msg.get('Message', {}).get('User', {}).get('userId', '')
        timestamp = msg.get('Message', {}).get('User', {}).get('timestamp', '')

        for competitor, info in results.items():
            keywords = info['keywords']

            if any(kw.lower() in msg_text.lower() for kw in keywords):
                # Record the discusser
                info['discussers'].add(msg_user)

                # Extract topics
                if 'strength' in msg_text.lower() or 'advantage' in msg_text.lower():
                    info['topics'].append({'topic': 'strengths', 'timestamp': timestamp})

                if 'weakness'  msg_text.lower()    msg_text.lower():
                    info[].append({: , : timestamp})

                   msg_text.lower():
                    info[].append({: , : timestamp})

                
                 (msg_text) < :
                    info[].append(msg_text)

     results

Finding Product Insights in Context

def extract_product_insights(slack_data, product_names):
    """Find detailed insights about specific products"""
    insights = {product: {'discussed_in': [], 'contexts': []} for product in product_names}

    for i, msg in enumerate(slack_data):
        msg_text = msg.get('Message', {}).get('User', {}).get('text', '')
        msg_user = msg.get('Message', {}).get('User', {}).get('userId', '')

        for product in product_names:
            if product.lower() in msg_text.lower():
                insights[product]['discussed_in'].append({
                    'message_index': i,
                    'user': msg_user,
                    'text': msg_text[:200]
                })

                # Extract context - what was being discussed
                if 'demo' in msg_text.lower():
                    insights[product]['contexts'].append('demo')
                if 'api' in msg_text.lower():
                    insights[product]['contexts'].append('api')
                if 'integration' in msg_text.lower():
                    insights[product]['contexts'].append('integration')

     insights

Cross-Referencing Slack with External Data

Linking Slack Mentions to Document References

def correlate_slack_mentions_with_documents(slack_data, documents):
    """Find Slack discussion of documents and extract reviewer info"""
    correlations = {}

    # Extract document identifiers
    doc_map = {doc.get('id'): doc for doc in documents}

    for msg in slack_data:
        msg_text = msg.get('Message', {}).get('User', {}).get('text', '')
        msg_user = msg.get('Message', {}).get('User', {}).get('userId', '')

        # Check if any document is mentioned
        for doc_id in doc_map.keys():
            if doc_id in msg_text:
                if doc_id not in correlations:
                    correlations[doc_id] = {
                        'document': doc_map[doc_id],
                        'slack_discussions': []
                    }

                correlations[doc_id]['slack_discussions'].append({
                    'user': msg_user,
                    'message': msg_text[:150],
                    'thread_replies': len(msg.get('ThreadReplies', []))
                })

    return correlations

Linking Slack URLs to URL Database

def match_slack_urls_to_database(slack_data, url_database):
    """Find which URLs are mentioned in Slack and by whom"""
    url_mentions = {}

    for msg in slack_data:
        msg_text = msg.get('Message', {}).get('User', {}).get('text', '')
        msg_user = msg.get('Message', {}).get('User', {}).get('userId', '')
        timestamp = msg.get('Message', {}).get('User', {}).get('timestamp', '')

        # Extract URLs from this message
        urls_found = extract_all_urls(msg_text)

        for url in urls_found:
            # Normalize URL
            url_clean = url.rstrip('/')

            # Check if it's in our database
            for db_url_obj in url_database:
                db_url = db_url_obj.get('link', '').rstrip('/')
                if db_url == url_clean or db_url in url_clean or url_clean in db_url:
                    if db_url not in url_mentions:
                        url_mentions[db_url] = {
                            'mentioned_by': set(),
                            'mentions': []
                        }

                    url_mentions[db_url]['mentioned_by'].add(msg_user)
                    url_mentions[db_url]['mentions'].append({
                        : msg_user,
                        : timestamp,
                        : msg_text[:]
                    })

     url_mentions

Advanced Patterns

Finding Discussion Chains

def trace_discussion_chain(slack_data, starting_msg_idx):
    """Trace a discussion thread and participants"""
    chain = {
        'main_message': slack_data[starting_msg_idx],
        'thread_participants': set(),
        'topics': [],
        'evolution': []
    }

    main_msg = slack_data[starting_msg_idx]
    main_user = main_msg.get('Message', {}).get('User', {}).get('userId', '')

    for reply in main_msg.get('ThreadReplies', []):
        reply_user = reply.get('userId', '')
        if reply_user and reply_user != main_user:
            chain['thread_participants'].add(reply_user)
            chain['evolution'].append({
                'user': reply_user,
                'text': reply.get('text', '')[:150]
            })

    return chain

Sentiment Analysis for Review Comments

def analyze_review_sentiment(thread_replies):
    """Analyze feedback tone from thread replies"""
    positive_words = ['good', 'great', 'excellent', 'agree', 'looks good', 'nice']
    negative_words = ['issue', 'problem', 'concern', 'need', 'should']

    sentiment = {'positive': [], 'negative': [], 'neutral': []}

    for reply in thread_replies:
        text = reply.get('text', '').lower()

        has_positive = any(word in text for word in positive_words)
        has_negative = any(word in text for word in negative_words)

        if has_positive and not has_negative:
            sentiment['positive'].append(reply)
        elif has_negative and not has_positive:
            sentiment['negative'].append(reply)
        else:
            sentiment['neutral'].append(reply)

    return sentiment

Complete Workflow Example

def comprehensive_slack_analysis(slack_data, external_data):
    """Complete analysis of Slack for intelligence gathering"""

    analysis = {
        'urls_extracted': [],
        'competitors_discussed': {},
        'documents_mentioned': {},
        'key_contributors': set()
    }

    # 1. Extract all URLs
    all_urls = set()
    for msg in slack_data:
        text = msg.get('Message', {}).get('User', {}).get('text', '')
        urls = extract_all_urls(text)
        all_urls.update(urls)

    analysis['urls_extracted'] = list(all_urls)

    # 2. Find competitor discussions
    competitors = {
        'PitchPerfect': {'keywords': ['pitchperfect', 'pitch perfect']},
        'ConvoSuggest': {'keywords': ['convosuggest', 'convo suggest']},
        'SalesMate': {'keywords': ['salesmate', 'sales mate']},
    }

    analysis['competitors_discussed'] = extract_competitor_mentions(slack_data, competitors)

    # 3. Link documents to discussions
    analysis['documents_mentioned'] = correlate_slack_mentions_with_documents(
        slack_data,
        external_data.get('documents', [])
    )

    # 4. Extract all participants
    for msg in slack_data:
        user = msg.get('Message', {}).get('User', {}).get(, )
         user:
            analysis[].add(user)

     analysis

Performance Considerations

• Regex compilation - Compile patterns once if used repeatedly
• Set operations - Use sets for deduplication to avoid O(n²) comparisons
• Early filtering - Filter data before detailed analysis to reduce processing
• Caching - Cache extracted URLs and compiled patterns
• Batch processing - Process messages in batches for large datasets