🔬 Technical Showcase

from selenium import webdriver from selenium.webdriver.common.by import By import time, json # Comprehensive multi-source data extraction def scrape_security_reviews(): driver = webdriver.Chrome() sources = { 'reddit': { 'r/antivirus': 'reddit.com/r/antivirus/search?q=mcafee', 'r/cybersecurity': 'reddit.com/r/cybersecurity/search?q=security' }, 'app_stores': { 'apple_store': 'apps.apple.com/reviews', 'google_play': 'play.google.com/store/reviews' }, 'retail_platforms': { 'amazon': 'amazon.com/product-reviews', 'rediff_shopping': 'shopping.rediff.com/reviews' } } extracted_data = [] for platform_type, urls in sources.items(): for platform, url in urls.items(): driver.get(f"https://{url}") reviews = driver.find_elements(By.CLASS_NAME, "review-content") for review in reviews: extracted_data.append({ 'platform_type': platform_type, 'platform': platform, 'text': review.text, 'timestamp': time.time(), 'sentiment': None # To be analyzed by AI }) return extracted_data

import requests import openai # Market data APIs and AI processing def enrich_with_market_data(reviews): enriched_data = [] for review in reviews: # Sentiment analysis via OpenAI response = openai.ChatCompletion.create( model="gpt-4", messages=[{ "role": "system", "content": "Analyze this security software review. Categorize the main issue: Performance, Privacy, Usability, or Support. Rate sentiment 1-10." }, { "role": "user", "content": review['text'] }] ) ai_analysis = response.choices[0].message.content # Add market context enriched_data.append({ **review, 'ai_category': extract_category(ai_analysis), 'sentiment_score': extract_sentiment(ai_analysis), 'competitor_mentions': find_competitors(review['text']), 'issue_severity': calculate_severity(review['text']) }) return enriched_data

# Advanced AI analysis for business insights def generate_strategic_insights(enriched_data): # Group by issue categories issues = categorize_by_problem(enriched_data) insights = {} for category, data in issues.items(): prompt = f""" Analyze {len(data)} user complaints about {category} issues in security software. Provide: 1. Root cause analysis 2. Business impact assessment 3. Competitive advantage opportunities 4. Specific solution recommendations User feedback patterns: {summarize_patterns(data)} """ ai_insight = openai.ChatCompletion.create( model="gpt-4", messages=[{"role": "user", "content": prompt}] ) insights[category] = { 'analysis': ai_insight.choices[0].message.content, 'affected_users': len(data), 'severity_score': calculate_avg_severity(data), 'market_opportunity': estimate_revenue_impact(data) } return insights