This workflow follows the Chainllm → Google Sheets recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "NTF 06 - Review Monitoring Alert",
"tags": [
{
"name": "NTF-Playbook"
}
],
"settings": {
"executionOrder": "v1"
},
"nodes": [
{
"id": "sticky-readme",
"name": "README",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-60,
-520
],
"parameters": {
"content": "## NTF 06 - Review Monitoring Alert\n\n**Trigger:** Schedule, every 6 hours\n\n**Flow:**\n1. Schedule trigger fires\n2. Search Google for recent reviews via SerpAPI\n3. Also searches Reddit for brand mentions\n4. Claude classifies sentiment for each result\n5. Negative/mixed results trigger Slack alert with drafted response\n6. All results logged to Google Sheets\n\n**Setup:**\n- Add your SerpAPI key (free tier: 100 searches/month, see SETUP.md for cadence math)\n- Set BRAND_NAME and REVIEW_KEYWORDS in the Config node\n- Set your Slack channel ID\n- Connect Google Sheets credential and set spreadsheet ID",
"height": 360,
"width": 540,
"color": 6
}
},
{
"id": "schedule-trigger",
"name": "Every 6 Hours",
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.2,
"position": [
0,
0
],
"parameters": {
"rule": {
"interval": [
{
"field": "hours",
"hoursInterval": 6
}
]
}
}
},
{
"id": "config",
"name": "Config Brand Settings",
"type": "n8n-nodes-base.set",
"typeVersion": 3.4,
"position": [
220,
0
],
"parameters": {
"mode": "manual",
"assignments": {
"assignments": [
{
"id": "brand_name",
"name": "brand_name",
"value": "YOUR_BRAND_NAME",
"type": "string"
},
{
"id": "review_query",
"name": "review_query",
"value": "=\"\\\"\" + $json.brand_name + \"\\\" review site:google.com OR site:trustpilot.com OR site:g2.com\"",
"type": "expression"
},
{
"id": "reddit_query",
"name": "reddit_query",
"value": "={{ $json.brand_name + ' site:reddit.com' }}",
"type": "expression"
}
]
}
}
},
{
"id": "serp-reviews",
"name": "SerpAPI Review Search",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
440,
-120
],
"parameters": {
"method": "GET",
"url": "https://serpapi.com/search",
"qs": {
"parameters": [
{
"name": "q",
"value": "={{ $('Config Brand Settings').item.json.review_query }}"
},
{
"name": "api_key",
"value": "YOUR_SERPAPI_KEY"
},
{
"name": "num",
"value": "10"
},
{
"name": "tbs",
"value": "qdr:d"
}
]
},
"options": {
"response": {
"response": {
"responseFormat": "json"
}
}
}
}
},
{
"id": "serp-reddit",
"name": "SerpAPI Reddit Search",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
440,
80
],
"parameters": {
"method": "GET",
"url": "https://serpapi.com/search",
"qs": {
"parameters": [
{
"name": "q",
"value": "={{ $('Config Brand Settings').item.json.reddit_query }}"
},
{
"name": "api_key",
"value": "YOUR_SERPAPI_KEY"
},
{
"name": "num",
"value": "5"
},
{
"name": "tbs",
"value": "qdr:d"
}
]
},
"options": {
"response": {
"response": {
"responseFormat": "json"
}
}
}
}
},
{
"id": "merge-results",
"name": "Merge Search Results",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
660,
0
],
"parameters": {
"jsCode": "const reviews = $('SerpAPI Review Search').first().json.organic_results || [];\nconst reddit = $('SerpAPI Reddit Search').first().json.organic_results || [];\nconst all = [...reviews, ...reddit].map(r => ({\n title: r.title || '',\n snippet: r.snippet || '',\n link: r.link || '',\n source: r.displayed_link || r.link || ''\n}));\nreturn all.map(r => ({ json: r }));"
}
},
{
"id": "claude-llm",
"name": "Claude Sentiment Model",
"type": "@n8n/n8n-nodes-langchain.lmChatAnthropic",
"typeVersion": 1.3,
"position": [
880,
0
],
"parameters": {
"model": "claude-haiku-4-5-20251001",
"options": {
"maxTokens": 500
}
},
"credentials": {
"anthropicApi": {
"name": "<your credential>"
}
}
},
{
"id": "sentiment-chain",
"name": "Classify Sentiment",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"typeVersion": 1.4,
"position": [
880,
0
],
"parameters": {
"promptType": "define",
"text": "=Classify the sentiment of this review/mention and draft a response if needed.\n\nTitle: {{ $json.title }}\nSnippet: {{ $json.snippet }}\nSource: {{ $json.source }}\n\nReturn JSON with:\n- sentiment: \"positive\" | \"neutral\" | \"negative\" | \"mixed\"\n- severity: \"low\" | \"medium\" | \"high\" (only relevant for negative)\n- summary: one sentence describing what is said\n- draft_response: a professional response draft (2-3 sentences) if sentiment is negative or mixed, otherwise null\n\nReturn only valid JSON. No markdown fencing."
}
},
{
"id": "parse-sentiment",
"name": "Parse Sentiment",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1100,
0
],
"parameters": {
"jsCode": "const raw = $input.first().json.text || '{}';\nlet parsed = {};\ntry {\n parsed = JSON.parse(raw.replace(/```json|```/g, '').trim());\n} catch(e) {\n parsed = { sentiment: 'neutral', severity: 'low', summary: raw, draft_response: null };\n}\nconst prev = $input.first().json;\nreturn [{ json: { ...prev, ...parsed, checked_at: new Date().toISOString() } }];"
}
},
{
"id": "filter-negative",
"name": "Filter Negative Only",
"type": "n8n-nodes-base.filter",
"typeVersion": 1,
"position": [
1320,
-120
],
"parameters": {
"conditions": {
"conditions": [
{
"id": "sentiment-filter",
"leftValue": "={{ $json.sentiment }}",
"rightValue": "positive",
"operator": {
"type": "string",
"operation": "notEquals"
}
}
]
}
}
},
{
"id": "slack-alert",
"name": "Slack Review Alert",
"type": "n8n-nodes-base.slack",
"typeVersion": 2.3,
"position": [
1540,
-120
],
"parameters": {
"operation": "post",
"channel": "YOUR_SLACK_CHANNEL_ID",
"text": "=*Review Alert: {{ $json.sentiment | upper }} ({{ $json.severity }})*\n*Source:* {{ $json.source }}\n*Summary:* {{ $json.summary }}\n*Link:* {{ $json.link }}\n\n{{ $json.draft_response ? '*Draft Response:*\\n' + $json.draft_response : '' }}"
},
"credentials": {
"slackApi": {
"name": "<your credential>"
}
}
},
{
"id": "sheets-log",
"name": "Log to Google Sheets",
"type": "n8n-nodes-base.googleSheets",
"typeVersion": 4.5,
"position": [
1320,
80
],
"parameters": {
"operation": "append",
"documentId": {
"__rl": true,
"value": "YOUR_SPREADSHEET_ID",
"mode": "id"
},
"sheetName": {
"__rl": true,
"value": "Reviews",
"mode": "name"
},
"columns": {
"mappingMode": "defineBelow",
"value": {
"Checked At": "={{ $json.checked_at }}",
"Source": "={{ $json.source }}",
"Title": "={{ $json.title }}",
"Sentiment": "={{ $json.sentiment }}",
"Severity": "={{ $json.severity }}",
"Summary": "={{ $json.summary }}",
"Link": "={{ $json.link }}"
}
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"name": "<your credential>"
}
}
}
],
"connections": {
"Every 6 Hours": {
"main": [
[
{
"node": "Config Brand Settings",
"type": "main",
"index": 0
}
]
]
},
"Config Brand Settings": {
"main": [
[
{
"node": "SerpAPI Review Search",
"type": "main",
"index": 0
},
{
"node": "SerpAPI Reddit Search",
"type": "main",
"index": 0
}
]
]
},
"SerpAPI Review Search": {
"main": [
[
{
"node": "Merge Search Results",
"type": "main",
"index": 0
}
]
]
},
"SerpAPI Reddit Search": {
"main": [
[
{
"node": "Merge Search Results",
"type": "main",
"index": 1
}
]
]
},
"Merge Search Results": {
"main": [
[
{
"node": "Classify Sentiment",
"type": "main",
"index": 0
}
]
]
},
"Claude Sentiment Model": {
"ai_languageModel": [
[
{
"node": "Classify Sentiment",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Classify Sentiment": {
"main": [
[
{
"node": "Parse Sentiment",
"type": "main",
"index": 0
}
]
]
},
"Parse Sentiment": {
"main": [
[
{
"node": "Filter Negative Only",
"type": "main",
"index": 0
},
{
"node": "Log to Google Sheets",
"type": "main",
"index": 0
}
]
]
},
"Filter Negative Only": {
"main": [
[
{
"node": "Slack Review Alert",
"type": "main",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
anthropicApigoogleSheetsOAuth2ApislackApi
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How this works
This workflow delivers timely alerts on customer sentiment by monitoring online reviews and Reddit discussions every six hours, empowering brand managers to swiftly address emerging issues and capitalise on positive feedback. It suits marketing teams or customer service leads handling consumer-facing products, where proactive reputation management drives loyalty and sales. The core step involves merging search results from SerpAPI, then using Anthropic's Claude model to classify sentiment via a chained LLM process, culminating in Slack notifications for actionable insights.
Use this workflow when you need automated, AI-driven oversight of brand mentions across review sites and social platforms like Reddit, especially for time-sensitive campaigns or crisis prevention. Avoid it for one-off analyses or brands with minimal online presence, as the scheduled cadence suits ongoing monitoring rather than ad-hoc checks. Common variations include adjusting the frequency for daily alerts or integrating Google Sheets to log historical sentiment data for trend analysis.
About this workflow
NTF 06 - Review Monitoring Alert. Uses httpRequest, lmChatAnthropic, chainLlm, slack. Scheduled trigger; 12 nodes.
Source: https://github.com/MinaSaad1/n8n-review-monitoring/blob/main/workflows/01-review-monitoring.json — original creator credit. Request a take-down →
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