/apify-brand-reputation-monitoring

name: apify-brand-reputation-monitoring
description: "Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors."
risk: critical
source: community
date_added: "2026-03-01"

Brand Reputation Monitoring

Scrape reviews, ratings, and brand mentions from multiple platforms using Apify Actors.

When to Use

• You need to monitor reviews, ratings, or brand mentions across social, travel, or map platforms.
• The task is to select and run an Apify Actor for brand sentiment or reputation tracking.
• You need exported monitoring results and a summary of reputation signals.

Prerequisites

Verify prerequisites before any paid run.

• `.env` file with `APIFY_TOKEN`
• Node.js 20.6+ (for native `--env-file` support)
• An approved Apify connector or Console access to inspect the chosen Actor and its schema; the bundled runner uses REST directly.
• Keep `.env` untracked and private; configure the token locally without pasting it into chat or command arguments.

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Determine data source (select Actor)
- [ ] Step 2: Fetch Actor schema via an approved connector or Console
- [ ] Step 3: Ask user preferences (format, filename)
- [ ] Step 4: Run the monitoring script
- [ ] Step 5: Summarize results

Step 1: Determine Data Source

Select the appropriate Actor based on user needs:

| User Need | Actor ID | Best For | |-----------|----------|----------| | Google Maps reviews | `compass/crawler-google-places` | Business reviews, ratings | | Google Maps review export | `compass/Google-Maps-Reviews-Scraper` | Dedicated review scraping | | Booking.com hotels | `voyager/booking-scraper` | Hotel data, scores | | Booking.com reviews | `voyager/booking-reviews-scraper` | Detailed hotel reviews | | TripAdvisor reviews | `maxcopell/tripadvisor-reviews` | Attraction/restaurant reviews | | Facebook reviews | `apify/facebook-reviews-scraper` | Page reviews | | Facebook comments | `apify/facebook-comments-scraper` | Post comment monitoring | | Facebook page metrics | `apify/facebook-pages-scraper` | Page ratings overview | | Facebook reactions | `apify/facebook-likes-scraper` | Reaction type analysis | | Instagram comments | `apify/instagram-comment-scraper` | Comment sentiment | | Instagram hashtags | `apify/instagram-hashtag-scraper` | Brand hashtag monitoring | | Instagram search | `apify/instagram-search-scraper` | Brand mention discovery | | Instagram tagged posts | `apify/instagram-tagged-scraper` | Brand tag tracking | | Instagram export | `apify/export-instagram-comments-posts` | Bulk comment export | | Instagram comprehensive | `apify/instagram-scraper` | Full Instagram monitoring | | Instagram API | `apify/instagram-api-scraper` | API-based monitoring | | YouTube comments | `streamers/youtube-comments-scraper` | Video comment sentiment | | TikTok comments | `clockworks/tiktok-comments-scraper` | TikTok sentiment |

Step 2: Fetch Actor Schema

Inspect the current Actor details and input schema using the authenticated Apify connector available in the session, or its Console page. Discover actual tool names and parameters before calling them. Do not parse `.env` with shell substitution or pass a token as a CLI header argument. Treat the Actor table as discovery candidates, not verified availability or schemas.

This returns:

• Actor description and README
• Required and optional input parameters
• Output fields (if available)

Step 3: Ask User Preferences

Before running, ask: 1. **Output format**:

• **Quick answer** - Display top few results in chat (no file saved)
• **CSV** - Bounded export, union of returned fields
• **JSON** - Bounded export in JSON format

2. **Number of results**: Based on character of use case

Step 4: Run the Script

**Quick answer (display in chat, no file):**

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT'

**CSV:**

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.csv \
  --format csv

**JSON:**

node --env-file=.env "<resolved-skill-directory>/reference/scripts/run_actor.js" \
  --actor "ACTOR_ID" \
  --input 'JSON_INPUT' \
  --output YYYY-MM-DD_OUTPUT_FILE.json \
  --format json

Step 5: Summarize Results

After completion, report:

• Number of reviews/mentions found
• File location and name
• Key fields available
• Suggested next steps (sentiment analysis, filtering)

Error Handling

`APIFY_TOKEN not found` - Ask user to create `.env` with `APIFY_TOKEN=your_token` `Connector unavailable` - Inspect the schema through the Console; do not claim an unavailable tool was called. `Actor not found` - Check Actor ID spelling `Run FAILED` - Ask user to check Apify console link in error output `Timeout` - The local timeout does not abort the remote run. Inspect the printed run ID in Console before retrying to avoid duplicate charges.

Run Boundaries

Before launch, confirm the selected Actor, authorized public-data scope, current input schema, result limits and pricing with the user’s request. Respect source terms and access controls; do not collect private group data or unnecessary personal contact information. Start with a small Actor-supported input limit. `--max-items` limits downloaded output only (default 1000); it does not cap Actor work or billing. The helper sends a skill-identifying User-Agent to Apify. Resolve the installed skill directory in the command placeholders. Output files must be new paths; exports preserve long values and heterogeneous fields. Quick answers fetch at most five records; minimize or redact personal fields before sharing.

Deduplicate reviews by platform and stable ID, record rating scale and retrieval time, and distinguish source rating from inferred sentiment. Counts from diff