TripAdvisor Reputation Event Monitor

Monitors TripAdvisor hotels, restaurants, and attractions and emits TYPED EVENTS — not review dumps. The actor re-polls your watchlist on each scheduled run, diffs against persisted state in Apify KV store, and pushes only what changed: new reviews, rating drops, rank shifts, unanswered complaints, and velocity spikes.

Built for hotel GMs, restaurant groups, and reputation-management SaaS platforms that need actionable signals routed to Slack, n8n, or their own webhook — not a spreadsheet to manually diff every morning.

What Makes This Different from Review Scrapers

Most TripAdvisor actors on the Apify Store are snapshot scrapers — they dump all reviews on every run and leave the diffing to you. You pay for 5,000 reviews when you only wanted to know about the 3 new ones since yesterday, and you still have to wire your own logic to detect a rating drop or find unanswered complaints.

This actor does that diffing for you, inside the actor, using a stateful Apify KV store. You get a clean typed-event stream:

Event	What it means
new_review	A review ID not seen on the previous run
complaint	New review with rating ≤ your threshold (default 2★)
rating_crossed	Overall property rating crossed a watched threshold (e.g. fell below 4.0)
rank_moved	Destination ranking shifted by ≥ N positions
review_velocity_spike	More than N new reviews in one run — possible viral moment or review-bomb
owner_response_gap	A negative review has had no management response for ≥ N days
review_count_milestone	Total review count hit a round number (100, 500, 1k, ...)

Route each event type differently: send complaint and owner_response_gap to a GM's Slack, send rating_crossed to a PagerDuty alert, archive everything else to a spreadsheet.

How It Works

1. Warm-GET bootstrap — for each property, fetches the TripAdvisor page to establish a session and extract rating, review count, and destination rank from the HTML.
2. GraphQL poll — fetches the 20 most recent reviews via TripAdvisor's internal getReviewListPageForLocation GraphQL endpoint.
3. KV-state diff — loads the previous run's state from a named Apify KV store and computes the delta.
4. Event emission — pushes only the typed events that passed your filters to the dataset, and charges one PPE event per emission.
5. State update — writes the updated state (seen review IDs, last rating, last rank, unanswered negatives) back to KV for the next run.

First run: bootstraps baseline only. No events emitted. This is by design — you need a "before" snapshot to produce a "what changed" diff.

Input

{
  "propertyUrls": [
    "https://www.tripadvisor.com/Hotel_Review-g60763-d93589-Reviews-The_Plaza-New_York_City_New_York.html",
    "https://www.tripadvisor.com/Restaurant_Review-g60763-d456789-Reviews-Le_Bernardin-New_York.html"
  ],
  "kvStoreName": "tripadvisor-reputation-state",
  "negativeThreshold": 2,
  "ratingCrossedThresholds": [4.0, 3.5],
  "rankMovedMinDelta": 3,
  "velocitySpikeMinDelta": 5,
  "ownerResponseGapDays": 3,
  "proxyConfiguration": { "useApifyProxy": true, "proxyGroups": ["RESIDENTIAL"] }
}

All thresholds are configurable. Use eventTypes to filter which event types reach the dataset (useful when chaining actors in a workflow — e.g. route only complaint to a notification actor).

Output Schema

Every dataset record is a flat event dict:

Field	Type	Description
event_type	string	One of the 7 typed events above
severity	string	info or warning
property_id	number	TripAdvisor location ID
property_name	string	Property display name
property_url	string	TripAdvisor URL
property_rating	number	Current overall rating
property_review_count	number	Current total reviews
property_rank	number	Current destination rank
summary	string	Human-readable one-line summary of the event
current_value	string	Current value (e.g. new rating, new rank)
previous_value	string	Previous value (e.g. old rating, old rank)
review_id	number	Review ID (for review events)
review_rating	number	Star rating 1-5 (for review events)
review_title	string	Review title (for review events)
review_text	string	Review text, truncated to 500 chars (for review events)
review_date	string	ISO-8601 review creation date
review_username	string	TripAdvisor username
mgmt_responded	boolean	Whether management has responded
days_without_response	number	Days since review posted without response (for gap events)
emitted_at	string	ISO-8601 timestamp of event detection

Proxy Requirement

Residential proxies are required. TripAdvisor blocks datacenter IP ranges. This actor is designed to run on the Apify platform where Apify residential proxies are available via proxyConfiguration. The buyer's Apify account pays for proxy compute — the actor author does not bear proxy costs.

Set proxyConfiguration: { "useApifyProxy": true, "proxyGroups": ["RESIDENTIAL"] } in your input. The actor defaults to RESIDENTIAL if no proxy configuration is provided but will log a warning.

Scheduling

Run daily or every 12 hours via Apify Scheduler. The KV store persists between runs — as long as you use the same kvStoreName, the actor knows what it already saw.

Example: monitor 50 competitor hotels at 6am every morning, route complaint and rating_crossed events to your CRM via a webhook actor.

Supported Property Types

• Hotels and accommodation (Hotel_Review-*)
• Restaurants (Restaurant_Review-*)
• Attractions and experiences (Attraction_Review-*)

Each property type uses the same underlying GraphQL endpoint. Airline reviews use a different endpoint structure and are not supported in this version.

Integrations

Built for hotel GMs, restaurant groups, and reputation-SaaS platforms routing actionable review events into their alert pipelines — the JSON/dataset output drops into the tools you already run, no glue code:

• n8n / Make / Zapier — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: n8n, Make, Zapier.
• Webhooks — fire your own endpoint the moment a run finishes, to push results straight into your pipeline (docs).
• MCP server — expose this actor as a tool to Claude, Cursor, or any MCP client so an AI agent can pull this data mid-conversation (guide).
• API & SDKs — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs.

See all Apify integrations.

Legal Notes

This actor monitors only publicly visible TripAdvisor pages. No account login is used, no personal data beyond what TripAdvisor publicly displays is collected. Monitor your own properties and publicly visible competitor data. US-based buyers should be aware that public competitor monitoring is generally lawful under US law; EU buyers should consult local data regulations on public data collection.

MCP Support

This actor is callable via the Apify MCP server for AI agent workflows:

Tool: apify/tripadvisor-reputation-monitor
Input: { "propertyUrls": [...], "eventTypes": ["complaint", "rating_crossed"] }

Use it in n8n, Make, or any MCP-compatible AI agent to receive structured reputation events without building your own scraper or diff logic.

Pricing

Pay per event ($0.005/event). A property monitored daily that generates 3 new reviews and 1 rating tick costs roughly $0.02/day — orders of magnitude cheaper than a $300/month reputation SaaS seat.

The first run is a free baseline bootstrap that emits 0 events.