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.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.