ReviewMine - AI-Powered Review Sentiment and Concern Extraction

Acceptance Criteria

Scraping: done when Google Reviews scraper returns 10+ reviews per product with no manual intervention. Extraction: done when Claude endpoint returns sentiment and objections for 10 sample reviews with > 85% accuracy (spot-checked). Weekly Digest: done when Slack bot delivers digest every Monday with top 5 themes ranked by frequency. Dashboard: done when sentiment chart loads in under 2 seconds and reflects extracted data.

How to build it, step by step

1. Run 'npx create-next-app@latest reviewmine --typescript --app' and 'npm install @supabase/supabase-js @anthropic-ai/sdk stripe playwright cheerio node-cron p-limit @slack/web-api zod'. 2. In Supabase dashboard create tables: users (id, email, slack_webhook_url, stripe_customer_id), products (id, user_id, name, google_url, trustpilot_url, capterra_url), reviews (id, product_id, platform, author, rating, text, review_date, raw_html_hash for dedup), extractions (id, review_id, sentiment enum('positive','neutral','negative'), confidence float, objections jsonb, feature_requests jsonb, competitor_mentions jsonb), digests (id, user_id, product_id, week_start date, top_themes jsonb, slack_delivered_at). Enable RLS and add policies so users can only read their own rows. 3. Build the Google Reviews scraper in /lib/scrapers/google.ts: use Playwright to navigate to maps.google.com search for the product name + 'reviews', wait for selector 'div[data-review-id]', scroll to load 20+ reviews, parse text/rating/author from DOM. Hash raw text for dedup before insert. Add retry logic with 3 attempts and 2s delay. 4. Build /lib/scrapers/trustpilot.ts: Trustpilot renders server-side so Cheerio suffices for basic scraping — fetch 'https://www.trustpilot.com/review/{domain}', parse '.review-content__text' and '.star-rating' selectors, handle pagination via '?page=N' query param up to page 3. 5. Build /lib/scrapers/capterra.ts: Capterra requires Playwright (JS-rendered). Navigate to the product URL, wait for '.review-content', extract text and rating. Use p-limit(2) to cap concurrent Playwright instances to avoid memory issues on Vercel. 6. Create /app/api/extract/route.ts: accept POST with {reviews: [{id, text}]}. Build a single Claude API call using tool_use with tool name 'extract_review_insights' and input_schema defining sentiment (enum), confidence (number 0-1), objections (string[]), feature_requests (string[]), competitor_mentions (string[]). Batch up to 15 reviews per call using a numbered list in the user message. Parse tool_use response and map results back to review IDs. Validate output with Zod before writing to extractions table. 7. Create /lib/jobs/weekly-digest.ts: query all products with reviews extracted in past 7 days, aggregate objections and feature_requests by counting frequency across extractions jsonb arrays using Postgres jsonb_array_elements, rank top 5 themes, compute sentiment distribution, write to digests table. Use node-cron schedule '0 8 * * 1' (Mondays 8am UTC). Deploy as a Vercel Cron Job in vercel.json with 'crons': [{'path': '/api/cron/digest', 'schedule': '0 8 * * 1'}]. 8. Create /app/api/slack/install/route.ts using @slack/web-api: store incoming_webhook URL per user in Supabase users table. In weekly-digest job, call WebClient.chat.postMessage with blocks formatted as: header block (week date), section blocks for sentiment bar (positive X% / negative Y%), then bullet list of top 5 themes with frequency counts. 9. Build dashboard at /app/dashboard using server components: fetch products and last 4 digests from Supabase, render sentiment trend as a simple recharts BarChart (npm install recharts), render top themes as a sortable table with objection text and frequency. Use v0.dev to scaffold the component layout then customize. 10. Add Stripe billing: create two products in Stripe dashboard ($79/month Starter, $199/month Pro), add /app/api/stripe/checkout/route.ts using stripe.checkout.sessions.create with price IDs, add /app/api/stripe/webhook/route.ts to update users.stripe_tier on checkout.session.completed. Gate scraping to tier limits (3 products vs unlimited) by checking count of active products per user before allowing new product creation.