Yelp Business Scraper

Extract reviews, ratings, hours, contact info, and sentiment themes from Yelp listings. Local market research and B2B lead generation at scale.

You're researching a local market. You want to know: which businesses are thriving? What are customers complaining about? Who are the real players vs struggling operators? You manually check 30 Yelp listings. 3 hours of your time. Partial data. No analysis.

You're a B2B sales team selling to restaurants. You want to find restaurants with bad customer service (your ICP: pain points = staffing problems). You manually search Yelp. You find 5 candidates. You cold call. 1 shows interest.

What if you could scrape 500 restaurants' Yelp data, automatically score them for your pain points, and get contact info? Then your sales team could focus on prospects who actually need your solution.

This extracts everything from Yelp: reviews, ratings, hours, phone, address, sentiment themes. Then scores businesses by opportunity.

⚡ What You Get

yelp-business-data/
├── business_name: "Tony's Italian Kitchen"
├── yelp_url: "yelp.com/biz/tonys-italian-kitchen"
├── location: "742 Oak Street, Portland, OR 97214"
├── basic_metrics/
│   ├── overall_rating: 4.1,
│   ├── review_count: 287,
│   ├── rating_trend: "stable (flat 6 months)",
│   ├── category: "Italian Restaurant",
│   └── price_range: "$$" (moderate)
├── contact_info/
│   ├── phone: "(503) 555-0147",
│   ├── website: "tonysitalian.com",
│   ├── hours: "Tue-Sun 5PM-10PM, Closed Mon"
│   └── owner_name: "Tony Marinello"
├── review_sentiment_analysis/
│   ├── total_reviews_analyzed: 287,
│   ├── sentiment_breakdown: {"positive": 74%, "neutral": 18%, "negative": 8%},
│   ├── top_praise_themes: [
│   │   {"theme": "Food quality", "frequency": 0.42, "sample_quote": "Best pasta in Portland"},
│   │   {"theme": "Ambiance", "frequency": 0.24, "sample_quote": "Romantic, perfect for date night"},
│   │   {"theme": "Friendly staff", "frequency": 0.18, "sample_quote": "Waiters made us feel at home"}
│   │ ]
│   └── top_complaint_themes: [
│   │   {"theme": "Long wait times", "frequency": 0.35, "sample_quote": "45 min wait even with reservation"},
│   │   {"theme": "Slow service", "frequency": 0.28, "sample_quote": "20 minutes to get water. Understaffed."},
│   │   {"theme": "Limited menu variety", "frequency": 0.12, "sample_quote": "Only 3 vegetarian options"}
│   │ ]
├── opportunity_scoring/
│   ├── pain_point_relevance: {
│   │   ├── understaffing_signals: 0.83,  👈 This restaurant is understaffed. Perfect for HR software pitch.
│   │   ├── customer_service_issues: 0.71,
│   │   ├── operational_inefficiency: 0.65
│   │ }
│   ├── business_health: "Stable but operationally challenged",
│   ├── budget_capacity: "Medium ($$$ establishment)",
│   └── sales_readiness_score: 0.78
├── recent_reviews/
│   ├── review_1: {"date": "2025-02-20", "rating": 5, "text": "Food was amazing! Wait was too long though."},
│   ├── review_2: {"date": "2025-02-18", "rating": 3, "text": "Service was slow. Waited 20 min for appetizers."},
│   └── review_3: {"date": "2025-02-15", "rating": 5, "text": "Best meal we've had all year!"}
└── sales_opportunity: "WARM LEAD. 4.1★ rating but clear understaffing signals (83% match). Long wait times + slow service complaints. Perfect for restaurant management/HR software pitch. Owner contact: (503) 555-0147"

👈 That 0.83 understaffing score is your warm lead. This restaurant has a real problem, customers are complaining about it, and they probably have budget to fix it. This is who your sales team should call.

🎯 Use Cases

• B2B Sales Prospecting selling to restaurants — scrape Yelp to find restaurants with service problems, staff issues, or operational pain. Call the ones with high problem scores.
• Local Market Researchers analyzing competitive landscapes — extract review data to understand which businesses are thriving, what customers value, and gaps in the market
• Franchise Scouts evaluating locations — analyze local restaurants' performance, customer sentiment, and business viability before opening a competitor
• Real Estate Teams analyzing neighborhoods — use restaurant health/sentiment to gauge neighborhood economic health and foot traffic potential
• Reputation Monitoring teams protecting brands — track reviews, sentiment shifts, and complaint themes for your own business + competitors

📊 Sample Output

{
  "business_id": "tonys_italian_kitchen_pdx",
  "business_name": "Tony's Italian Kitchen",
  "yelp_url": "yelp.com/biz/tonys-italian-kitchen",
  "scrape_date": "2025-02-26",
  "basic_information": {
    "location": {
      "address": "742 Oak Street",
      "city": "Portland",
      "state": "OR",
      "zip": "97214",
      "coordinates": {"latitude": 45.5234, "longitude": -122.6762}
    },
    "contact_information": {
      "phone": "(503) 555-0147",
      "website": "tonysitalian.com",
      "hours_of_operation": {
        "monday": "closed",
        "tuesday": "5:00 PM - 10:00 PM",
        "wednesday": "5:00 PM - 10:00 PM",
        "thursday": "5:00 PM - 10:00 PM",
        "friday": "5:00 PM - 10:30 PM",
        "saturday": "5:00 PM - 10:30 PM",
        "sunday": "5:00 PM - 9:00 PM"
      }
    },
    "owner_information": {
      "owner_name": "Tony Marinello",
      "owner_contact": "owner@tonysitalian.com"
    }
  },
  "rating_metrics": {
    "overall_rating": 4.1,
    "total_reviews": 287,
    "rating_distribution": {
      "five_star": 0.42,
      "four_star": 0.24,
      "three_star": 0.18,
      "two_star": 0.10,
      "one_star": 0.06
    },
    "rating_trend": {
      "trend": "stable",
      "change_6_months": 0.0,
      "change_30_days": -0.1
    }
  },
  "business_information": {
    "category": "Italian Restaurant",
    "subcategories": ["Italian", "Dinner", "Wine Bar"],
    "price_range": 2,
    "price_range_description": "$$ (moderate)",
    "reservation_available": true,
    "outdoor_seating": true,
    "parking": "street parking available"
  },
  "review_sentiment_analysis": {
    "total_reviews_analyzed": 287,
    "sentiment_distribution": {
      "positive_percent": 0.74,
      "neutral_percent": 0.18,
      "negative_percent": 0.08
    },
    "top_positive_themes": [
      {
        "rank": 1,
        "theme": "Food quality",
        "frequency": 0.42,
        "confidence": 0.94,
        "sample_quotes": [
          "Best pasta in Portland. Authentic flavors.",
          "Fresh ingredients. Homemade pasta is incredible.",
          "Consistently excellent food."
        ]
      },
      {
        "rank": 2,
        "theme": "Ambiance / Atmosphere",
        "frequency": 0.24,
        "confidence": 0.89,
        "sample_quotes": [
          "Romantic ambiance. Perfect for date nights.",
          "Cozy Italian atmosphere."
        ]
      },
      {
        "rank": 3,
        "theme": "Friendly staff",
        "frequency": 0.18,
        "confidence": 0.85,
        "sample_quotes": [
          "Waiters made us feel at home."
        ]
      }
    ],
    "top_negative_themes": [
      {
        "rank": 1,
        "theme": "Long wait times",
        "frequency": 0.35,
        "confidence": 0.91,
        "trend": "increasing",
        "sample_quotes": [
          "45 minute wait even with reservation",
          "Long wait even for a small group",
          "Arrived at 5:30, seated at 6:15"
        ]
      },
      {
        "rank": 2,
        "theme": "Slow service",
        "frequency": 0.28,
        "confidence": 0.88,
        "trend": "increasing",
        "sample_quotes": [
          "20 minutes to get water. Understaffed.",
          "Server disappeared for long stretches.",
          "Food came out slowly despite quiet restaurant."
        ]
      },
      {
        "rank": 3,
        "theme": "Limited menu variety",
        "frequency": 0.12,
        "confidence": 0.79,
        "sample_quotes": [
          "Only 3 vegetarian options for a modern restaurant."
        ]
      }
    ]
  },
  "pain_point_relevance_scoring": {
    "understaffing_indicators": {
      "score": 0.83,
      "confidence": 0.88,
      "signals": [
        "Long wait times (35% of complaints)",
        "Slow service (28% of complaints)",
        "Server availability issues mentioned in 12 reviews"
      ]
    },
    "customer_service_issues": {
      "score": 0.71,
      "confidence": 0.85,
      "signals": [
        "Service speed complaints (28%)",
        "Some staff friendliness issues mentioned"
      ]
    },
    "operational_efficiency": {
      "score": 0.65,
      "confidence": 0.80,
      "signals": [
        "Wait times despite moderate traffic",
        "Service gaps suggest process inefficiencies"
      ]
    }
  },
  "recent_reviews": [
    {
      "review_date": "2025-02-20",
      "rating": 5,
      "author": "Maria K.",
      "review_text": "Food was amazing! Wait was too long though. Reservation system needs improvement.",
      "sentiment": "positive_with_concern"
    },
    {
      "review_date": "2025-02-18",
      "rating": 3,
      "author": "James D.",
      "review_text": "Service was slow. Waited 20 min for appetizers even though restaurant wasn't busy. Seems understaffed.",
      "sentiment": "negative"
    },
    {
      "review_date": "2025-02-15",
      "rating": 5,
      "author": "Sarah L.",
      "review_text": "Best meal we've had all year! The pasta was fresh and the sauce was incredible. Only minor complaint: long wait.",
      "sentiment": "positive_with_minor_concern"
    }
  ],
  "business_health_assessment": {
    "overall_health": "Stable but operationally challenged",
    "health_score": 0.72,
    "strengths": ["Product quality", "Brand loyalty", "Ambiance appeal"],
    "weaknesses": ["Operational efficiency", "Staff capacity", "Wait time management"],
    "opportunities": [
      "Restaurant management software",
      "Staff scheduling optimization",
      "Reservation system upgrade"
    ]
  },
  "budget_capacity_assessment": {
    "estimated_annual_revenue": "$1,200,000",
    "estimated_profit_margin": 0.15,
    "estimated_annual_profit": "$180,000",
    "budget_capacity": "Medium",
    "likely_budget_available": "$15,000 - $40,000/year for operational improvements"
  },
  "sales_opportunity": {
    "opportunity_rating": "WARM",
    "sales_readiness_score": 0.78,
    "key_sales_angles": [
      "Staff scheduling/management solution (0.83 understaffing score)",
      "Reservation system optimization (wait time complaints)",
      "Service workflow automation (server efficiency improvement)"
    ],
    "optimal_contact": {
      "contact_name": "Tony Marinello",
      "contact_type": "Owner",
      "phone": "(503) 555-0147",
      "email": "owner@tonysitalian.com"
    },
    "outreach_timing": "Immediate — pain points are active and growing",
    "pitch_direction": "Operational efficiency to free up staff capacity",
    "competitive_insights": "Restaurant is thriving on food quality. Can overcome operational issues with right tools."
  }
}

Field Guide:

• overall_rating: 1-5 scale. ≥4.0 = popular. <3.0 = struggling.
• sentiment_distribution: Percentage of positive/neutral/negative reviews.
• pain_point_relevance_scoring: Your solution's fit. ≥0.75 = warm lead.

🔗 Integrations & Automation

Webhook Integration: Scrape 500 restaurants. Filter for understaffing signals >0.80. Webhook pushes warm leads to your Salesforce every morning.

Email Digests: Weekly list of "Top 20 warm leads in your territory" + contact info + key pain points to mention in cold call.

MCP Compatible: Ask your sales AI: "Find me restaurants with staffing problems in Portland." Agent queries this tool, filters results, returns 30 warm leads with contact info.

API: Integrate into your CRM. Scrape 1,000 businesses in your vertical. Auto-score for sales readiness. Identify top opportunities.

Explore integrations →

🔌 Works Great With

• Contact Email Finder — Get detailed owner/manager emails for leads identified through Yelp scraping
• Google Maps Business Scraper — Cross-reference Yelp data with Google Maps reviews for validation
• Regional Lead Scanner — Combine Yelp data with location-based lead gen for comprehensive local prospecting
• Market Research Automator — Analyze local market health using aggregated Yelp data across multiple businesses
• Competitive Intelligence — Deep dive into specific competitors identified through Yelp

💰 Cost & Performance

Typical run: Scrape 500 local businesses' Yelp data (reviews, ratings, sentiment, pain points) in 18 minutes for ~$5.60. That's 1.12¢ per business.

Compare to:

• Manually checking 500 Yelp listings: 25 hours = $1,250-$1,875 in labor
• Hiring a researcher to compile local market data: $3,000-$5,000
• Sales team manually researching prospects: Hours of lost productivity per day

We do it for $5.60. One run pays for itself in the first warm lead that converts.

🛡️ Built Right

• Full Yelp Extraction: Reviews, ratings, hours, photos, menu, pricing, contact info. Everything accessible.
• Sentiment AI: GPT-powered analysis of review text. Detects themes, not just keywords. Understands context ("wait was long" vs "we waited happily in bar").
• Pain Point Scoring: ML models identify your ICP signals. Detects understaffing, service issues, product problems, and more.
• Review Filtering: Latest reviews prioritized. Trends tracked month-over-month. Detects improving vs deteriorating businesses.
• Contact Extraction: Pulls owner info, manager profiles, phone numbers. Cleans and verifies contact data.

---

Fresh data. Zero guesswork. Be the first to know.

📧 Email alerts · 🔗 Webhook triggers · 🤖 MCP compatible · 📡 API access

Built by Creator Fusion — OSINT tools that actually work.