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": "SentinelAI \u2014 Brand Mention Ingestion Pipeline",
"nodes": [
{
"parameters": {
"rule": {
"interval": [
{
"field": "hours",
"hoursInterval": 6
}
]
}
},
"id": "node-schedule-trigger",
"name": "Schedule Trigger",
"type": "n8n-nodes-base.scheduleTrigger",
"typeVersion": 1.2,
"position": [
240,
300
],
"notes": "Runs every 6 hours. Adjust interval for real-time needs."
},
{
"parameters": {
"jsCode": "// Simulate fetching mentions from Twitter/X API\n// In production: replace with HTTP Request node hitting Twitter API v2\nconst brands = ['Nike', 'Tesla', 'Apple'];\nconst sources = ['Twitter'];\nconst now = new Date();\n\nconst tweets = [];\nfor (const brand of brands) {\n for (let i = 0; i < 5; i++) {\n tweets.push({\n id: `tw-${Date.now()}-${brand}-${i}`,\n brand,\n source: 'Twitter',\n author: `user_${Math.floor(Math.random() * 9999)}`,\n text: `Sample tweet mentioning ${brand} - item ${i}`,\n timestamp: now.toISOString(),\n region: 'US',\n language: 'en',\n engagement: Math.floor(Math.random() * 1000),\n url: `https://twitter.com/user/status/${Date.now()}${i}`\n });\n }\n}\nreturn tweets.map(t => ({ json: t }));",
"mode": "runOnceForAllItems"
},
"id": "node-twitter-source",
"name": "Twitter / X Source",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
480,
160
],
"notes": "Simulated. Replace jsCode body with HTTP Request to Twitter API v2 search/recent endpoint using Bearer Token."
},
{
"parameters": {
"jsCode": "// Simulate fetching Reddit posts via Reddit API (OAuth2)\n// In production: replace with HTTP Request node to https://oauth.reddit.com/search\nconst brands = ['Nike', 'Tesla', 'Apple'];\nconst subreddits = ['technology', 'apple', 'teslamotors', 'nike', 'hardware'];\nconst now = new Date();\n\nconst posts = [];\nfor (const brand of brands) {\n for (let i = 0; i < 4; i++) {\n posts.push({\n id: `rd-${Date.now()}-${brand}-${i}`,\n brand,\n source: 'Reddit',\n author: `redditor_${Math.floor(Math.random() * 9999)}`,\n text: `Reddit post about ${brand} from r/${subreddits[Math.floor(Math.random() * subreddits.length)]}`,\n timestamp: now.toISOString(),\n region: 'US',\n language: 'en',\n engagement: Math.floor(Math.random() * 500),\n url: `https://reddit.com/r/tech/comments/${Date.now()}${i}`\n });\n }\n}\nreturn posts.map(p => ({ json: p }));",
"mode": "runOnceForAllItems"
},
"id": "node-reddit-source",
"name": "Reddit Source",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
480,
300
],
"notes": "Simulated. Replace with HTTP Request node to Reddit Search API. Requires Reddit OAuth2 credentials."
},
{
"parameters": {
"jsCode": "// Simulate fetching news articles via NewsAPI or RSS\n// In production: replace with HTTP Request to https://newsapi.org/v2/everything\nconst brands = ['Nike', 'Tesla', 'Apple'];\nconst now = new Date();\n\nconst articles = [];\nfor (const brand of brands) {\n for (let i = 0; i < 3; i++) {\n articles.push({\n id: `nw-${Date.now()}-${brand}-${i}`,\n brand,\n source: 'News',\n author: `journalist_${Math.floor(Math.random() * 999)}`,\n text: `News article headline and excerpt about ${brand} \u2014 covering recent developments.`,\n timestamp: now.toISOString(),\n region: 'US',\n language: 'en',\n engagement: Math.floor(Math.random() * 2000),\n url: `https://news.example.com/article/${Date.now()}${i}`\n });\n }\n}\nreturn articles.map(a => ({ json: a }));",
"mode": "runOnceForAllItems"
},
"id": "node-news-source",
"name": "News / RSS Source",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
480,
440
],
"notes": "Simulated. Replace with HTTP Request to NewsAPI or an RSS Feed node. Supports any RSS/Atom feed URL."
},
{
"parameters": {
"jsCode": "// Simulate blog and review mentions\n// In production: scrape via HTTP Request + HTML Extract node\nconst brands = ['Nike', 'Tesla', 'Apple'];\nconst now = new Date();\n\nconst items = [];\nfor (const brand of brands) {\n items.push({\n id: `bl-${Date.now()}-${brand}`,\n brand,\n source: 'Blog',\n author: `blogger_${Math.floor(Math.random() * 999)}`,\n text: `Blog review post discussing ${brand} product experience in detail.`,\n timestamp: now.toISOString(),\n region: 'UK',\n language: 'en',\n engagement: Math.floor(Math.random() * 300),\n url: `https://blog.example.com/${brand.toLowerCase()}-review`\n });\n items.push({\n id: `rv-${Date.now()}-${brand}`,\n brand,\n source: 'Review',\n author: `reviewer_${Math.floor(Math.random() * 999)}`,\n text: `Customer review of ${brand} posted on review platform.`,\n timestamp: now.toISOString(),\n region: 'AU',\n language: 'en',\n engagement: Math.floor(Math.random() * 150),\n url: `https://reviews.example.com/${brand.toLowerCase()}`\n });\n}\nreturn items.map(i => ({ json: i }));",
"mode": "runOnceForAllItems"
},
"id": "node-blog-review-source",
"name": "Blog & Review Source",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
480,
580
],
"notes": "Simulated. Replace with HTTP Request + HTML Extract nodes for blog scraping, or connect to G2/Trustpilot APIs."
},
{
"parameters": {
"mode": "combine",
"combinationMode": "mergeByPosition",
"options": {}
},
"id": "node-merge",
"name": "Merge All Sources",
"type": "n8n-nodes-base.merge",
"typeVersion": 3,
"position": [
720,
370
],
"notes": "Merges mentions from all 4 sources into a single unified stream."
},
{
"parameters": {
"jsCode": "// Deduplication \u2014 remove mentions with duplicate IDs\n// In production: compare against database of already-processed IDs\nconst seen = new Set();\nconst unique = [];\nfor (const item of $input.all()) {\n const id = item.json.id;\n if (!seen.has(id)) {\n seen.add(id);\n unique.push(item);\n }\n}\nconsole.log(`Deduplication: ${$input.all().length} in -> ${unique.length} out`);\nreturn unique;",
"mode": "runOnceForAllItems"
},
"id": "node-deduplicate",
"name": "Deduplicate",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
960,
370
],
"notes": "Removes duplicate mention IDs. In production, cross-reference against a database table of processed IDs."
},
{
"parameters": {
"conditions": {
"options": {
"caseSensitive": false,
"leftValue": "",
"typeValidation": "strict"
},
"conditions": [
{
"id": "lang-check",
"leftValue": "={{ $json.language }}",
"rightValue": "en",
"operator": {
"type": "string",
"operation": "equals"
}
}
],
"combinator": "and"
}
},
"id": "node-language-filter",
"name": "Language Filter",
"type": "n8n-nodes-base.filter",
"typeVersion": 2,
"position": [
1200,
370
],
"notes": "Filter to supported languages. Extend with translation node (DeepL/Google Translate) for multilingual support."
},
{
"parameters": {
"command": "cd /app/classifier && python classify.py --input /app/data/mentions_raw.csv --output /app/data/mentions_classified.json"
},
"id": "node-classifier",
"name": "HuggingFace Classifier",
"type": "n8n-nodes-base.executeCommand",
"typeVersion": 1,
"position": [
1440,
370
],
"notes": "Runs classify.py which uses:\n- Model 1: cardiffnlp/twitter-roberta-base-sentiment-latest (sentiment)\n- Model 2: facebook/bart-large-mnli (zero-shot topics)\nAdjust path to match your deployment environment."
},
{
"parameters": {
"jsCode": "// Read the classified JSON output and parse results\nconst fs = require('fs');\nconst outputPath = '/app/data/mentions_classified.json';\n\ntry {\n const raw = fs.readFileSync(outputPath, 'utf8');\n const classified = JSON.parse(raw);\n console.log(`Loaded ${classified.length} classified mentions`);\n return classified.map(m => ({ json: m }));\n} catch (e) {\n console.error('Failed to read classified output:', e.message);\n return [{ json: { error: e.message } }];\n}",
"mode": "runOnceForAllItems"
},
"id": "node-load-results",
"name": "Load Classified Results",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1680,
370
],
"notes": "Reads the JSON output from the classifier script."
},
{
"parameters": {
"jsCode": "// Alert detection \u2014 flag negative spikes and crisis mentions\nconst mentions = $input.all().map(i => i.json);\nconst alerts = [];\nconst brands = [...new Set(mentions.map(m => m.brand))];\n\nfor (const brand of brands) {\n const brandMentions = mentions.filter(m => m.brand === brand);\n const negative = brandMentions.filter(m => m.sentiment === 'negative');\n const crisis = brandMentions.filter(m => m.is_crisis === true || m.is_crisis === 'true');\n\n // Crisis alert\n if (crisis.length > 0) {\n alerts.push({\n type: 'crisis',\n brand,\n severity: 'critical',\n message: `${crisis.length} crisis-level mention(s) detected for ${brand}`,\n count: crisis.length\n });\n }\n\n // Negative spike (simple threshold for N8N \u2014 full baseline logic is in analytics.ts)\n if (negative.length >= 5) {\n alerts.push({\n type: 'negative_spike',\n brand,\n severity: negative.length >= 10 ? 'high' : 'medium',\n message: `Negative spike: ${negative.length} negative mentions for ${brand} in this batch`,\n count: negative.length\n });\n }\n}\n\nconsole.log(`Generated ${alerts.length} alert(s)`);\nreturn alerts.length > 0\n ? alerts.map(a => ({ json: a }))\n : [{ json: { status: 'no_alerts' } }];",
"mode": "runOnceForAllItems"
},
"id": "node-alert-detection",
"name": "Alert Detection",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1920,
300
],
"notes": "Detects crisis signals and negative spikes. In production, send results to Slack, email, or PagerDuty via notification nodes."
},
{
"parameters": {
"jsCode": "// Write final classified mentions to the data file read by the Next.js app\n// In production: insert into PostgreSQL/MongoDB instead\nconst fs = require('fs');\nconst mentions = $input.all().map(i => i.json);\nconst outputPath = '/app/data/mentions_classified.json';\n\ntry {\n fs.writeFileSync(outputPath, JSON.stringify(mentions, null, 2), 'utf8');\n console.log(`Wrote ${mentions.length} mentions to ${outputPath}`);\n return [{ json: { status: 'success', count: mentions.length, path: outputPath } }];\n} catch (e) {\n return [{ json: { status: 'error', message: e.message } }];\n}",
"mode": "runOnceForAllItems"
},
"id": "node-write-output",
"name": "Write to Data Store",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1920,
440
],
"notes": "Writes output to mentions_classified.json. In production: replace with PostgreSQL Insert or MongoDB node."
}
],
"connections": {
"Schedule Trigger": {
"main": [
[
{
"node": "Twitter / X Source",
"type": "main",
"index": 0
},
{
"node": "Reddit Source",
"type": "main",
"index": 0
},
{
"node": "News / RSS Source",
"type": "main",
"index": 0
},
{
"node": "Blog & Review Source",
"type": "main",
"index": 0
}
]
]
},
"Twitter / X Source": {
"main": [
[
{
"node": "Merge All Sources",
"type": "main",
"index": 0
}
]
]
},
"Reddit Source": {
"main": [
[
{
"node": "Merge All Sources",
"type": "main",
"index": 1
}
]
]
},
"News / RSS Source": {
"main": [
[
{
"node": "Merge All Sources",
"type": "main",
"index": 2
}
]
]
},
"Blog & Review Source": {
"main": [
[
{
"node": "Merge All Sources",
"type": "main",
"index": 3
}
]
]
},
"Merge All Sources": {
"main": [
[
{
"node": "Deduplicate",
"type": "main",
"index": 0
}
]
]
},
"Deduplicate": {
"main": [
[
{
"node": "Language Filter",
"type": "main",
"index": 0
}
]
]
},
"Language Filter": {
"main": [
[
{
"node": "HuggingFace Classifier",
"type": "main",
"index": 0
}
]
]
},
"HuggingFace Classifier": {
"main": [
[
{
"node": "Load Classified Results",
"type": "main",
"index": 0
}
]
]
},
"Load Classified Results": {
"main": [
[
{
"node": "Alert Detection",
"type": "main",
"index": 0
},
{
"node": "Write to Data Store",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1",
"saveManualExecutions": true,
"callerPolicy": "workflowsFromSameOwner"
},
"staticData": null,
"tags": [
"sentiment",
"brand-monitoring",
"nlp"
],
"meta": {
"templateCredsSetupCompleted": false
}
}
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
SentinelAI — Brand Mention Ingestion Pipeline. Uses executeCommand. Scheduled trigger; 12 nodes.
Source: https://github.com/DeaAR0/sentiment-analysis/blob/8e3bb07f418f48dc6daa911e81de0f83529caad8/n8n/sentiment_pipeline.json — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
Complete backup solution that saves both workflows and credentials to local/server disk with optional FTP upload for off-site redundancy.
Read Write File, Email Send, Execute Command +3
💰 Money Machine - Full Cycle. Uses executeCommand. Scheduled trigger; 13 nodes.
Execute Command
IA Leilão Imóveis - Automação Semanal. Uses executeCommand, emailSend. Scheduled trigger; 8 nodes.
Execute Command, Email Send
JobSearch Stale Alert. Uses executeCommand, emailSend. Scheduled trigger; 4 nodes.
Execute Command, Email Send