Review Analysis

When: User wants to collect and analyze business reviews from Google Maps.

Pipeline

1. Find businesses -> compass/crawler-google-places
   • Key input: searchStringsArray, locationQuery, maxCrawledPlaces
2. Extract reviews -> compass/Google-Maps-Reviews-Scraper
   • Pipe: results[].url -> startUrls
   • Key input: startUrls, maxReviews

Output fields

Step 1: title, totalScore, reviewsCount, url, categoryName Step 2: text, stars, publishedAtDate, reviewerName, ownerResponse

Competitor review intelligence

When: User wants to extract competitor reviews to surface customer pain points and compare against own product strengths for positioning.

Pipeline

1. Scrape competitor reviews -> compass/Google-Maps-Reviews-Scraper
   • Key input: startUrls (competitor Google Maps URLs), maxReviews, sort (newest or most relevant)
2. Yelp competitor reviews -> tri_angle/yelp-review-scraper
   • Key input: startUrls (competitor Yelp URLs), maxReviews

Output fields

Step 1: stars, text, name, publishedAtDate, reviewId Step 2: text, rating, date, userName

Gotcha

Run steps 1 and 2 in parallel for the same competitor, then merge by date. AI analysis works best when you label each review with the competitor name before passing to LLM for theme extraction.

Google Play app review monitoring

When: User wants daily low-rating alerts from Google Play to route urgent negative feedback to the support team.

Pipeline

1. Scrape app reviews -> apify/playwright-scraper
   • Key input: startUrls (Google Play app URL), maxRequestsPerCrawl
2. Filter and alert (n8n native - IF node)
   • Pipe: results[].stars -> filter where stars < 4

Output fields

Step 1: stars, text, date, appVersion, thumbsUpCount

Gotcha

Google Play uses heavy client-side rendering. Use apify/playwright-scraper rather than cheerio. If results are thin, search Apify Store for a dedicated Google Play reviews Actor - the ecosystem updates frequently.

Cross-platform hotel/restaurant reviews

When: User wants reviews aggregated from multiple platforms for the same business.

Pipeline (hotels)

1. Aggregate reviews -> tri_angle/hotel-review-aggregator
   • Key input: urls (hotel URLs from TripAdvisor, Yelp, Google Maps, Booking.com, etc.)

Pipeline (restaurants)

1. Aggregate reviews -> tri_angle/restaurant-review-aggregator
   • Key input: urls (restaurant URLs from Yelp, Google Maps, DoorDash, UberEats, etc.)

Output fields

Both: text, rating, date, platform, reviewerName, title

Multi-platform review aggregation for hospitality

When: User wants a weekly sentiment digest across TripAdvisor, Booking.com, Google, and Yelp for a property - including theme extraction by category (service, rooms, location, price).

Pipeline

1. Aggregate all platforms -> tri_angle/hotel-review-aggregator
   • Key input: startUrls (property page URLs per platform), maxReviews, includeReviews
2. Airbnb reviews (if applicable) -> tri_angle/airbnb-reviews-scraper
   • Key input: startUrls (Airbnb listing URLs), maxReviews

Output fields

Step 1: stars, text, title, reviewDate, source, userProfile.name Step 2: stars, text, reviewDate, reviewerName

Gotcha

Review aggregators pull from multiple platforms in one run - cheaper than running separate scrapers per platform. Use the aggregators when covering 3+ platforms. For Airbnb specifically, run the dedicated tri_angle/airbnb-reviews-scraper separately and merge by date.

Yelp review pipeline

When: User wants Yelp reviews for businesses in a specific area.

Pipeline

1. Find businesses -> tri_angle/get-yelp-urls
   • Key input: location, category
2. Extract reviews -> tri_angle/yelp-review-scraper
   • Pipe: results[].url -> startUrls
   • Key input: startUrls, maxReviews

Output fields

Step 1: name, url, rating, reviewCount, address Step 2: text, rating, date, userName