apify-tripadvisor-hotel-reviewslisted

Build a fresh hotel reviews dataset from Tripadvisor with the Apify Tripadvisor API Actor (johnvc/tripadvisor-api). Chain search to reviews and get sentiment-ready rows per hotel with the full review text, title, rating, reviewDate, tripDate, tripType (business, couples, family, solo), language, helpful votes and authorName. Feed guest feedback analysis, competitor benchmarking, review monitoring on a schedule, or an LLM sentiment pipeline that needs current hotel reviews instead of a stale dump. Use when the user wants hotel reviews data, guest review analysis, a hotel reviews dataset or export, or review monitoring for a set of properties. Pay per review, MCP-ready for Claude and other AI agents.
johnisanerd/claude-skill-tripadvisor-hotel-reviews · ★ 0 · AI & Automation · score 63

Install: claude install-skill johnisanerd/claude-skill-tripadvisor-hotel-reviews

# Hotel Reviews Dataset Builder for Sentiment and Guest Insight A current hotel reviews dataset for any market, built in two calls per hotel and ready for sentiment analysis, benchmarking, or scheduled monitoring. ## When to use this skill - The user wants "hotel reviews" for one property or a competitive set as data, not screenshots. - They are doing guest sentiment analysis and need full review text with dates and trip context. - They want to benchmark a hotel against neighbors on rating trend and complaint themes. - They want a weekly or monthly review pull for the same properties on a schedule. Not for: hotel prices (Google Hotels, see `references/actor-index.md`), restaurant-first markets (the same Actor covers them, but the workflow below is tuned for hotels), or generic Tripadvisor extraction (use the companion apify-tripadvisor-scraper skill). ## What you get Review rows are the product; search rows are the lookup step. Sentiment-relevant fields per review: - `snippet` holds the full review text; `title` is the reviewer's headline - `rating` (1 to 5), `reviewDate`, `tripDate`, `tripType` (business, couples, family, friends, solo) - `language` for routing to the right sentiment model, `votes` for weighting by helpfulness - `authorName`, `authorUsername`, `authorContributions` for reviewer credibility signals - `placeId` ties every review back to the hotel it belongs to ## Prerequisites - Apify account (sign up at https://apify.com?fpr=9n7kx3&fp_sid=skillrepo).