n8n Social Media Monitoring: ติดตาม Brand Mentions และ Sentiment Analysis อัตโนมัติ

การติดตามว่าคนพูดถึง brand, สินค้า หรือบริการของคุณบน Social Media อย่างไรเป็นสิ่งสำคัญ แต่การทำ manual monitoring ข้าม Facebook, Twitter, Instagram, Reddit และแพลตฟอร์มอื่นๆ ใช้เวลามากและมักพลาดข้อมูลสำคัญ

ด้วย n8n และ AI คุณสามารถสร้างระบบ Social Media Monitoring ที่:

• ติดตาม brand mentions อัตโนมัติ
• วิเคราะห์ sentiment (บวก/ลบ/เป็นกลาง)
• แจ้งเตือนเมื่อมี crisis หรือ viral content
• ตอบกลับอัตโนมัติหรือ route ให้ team
• Track competitors
• Generate insights และ reports

บทความนี้จะสอนการสร้าง social listening system ที่ช่วยให้คุณรู้ทุกอย่างที่เกิดขึ้นกับ brand แบบ real-time

สารบัญ

• ทำไมต้องทำ Social Media Monitoring?
• Social Platforms ที่รองรับ
• เครื่องมือและ APIs
• สถาปัตยกรรมระบบ
• Workflow 1: Brand Mentions Monitoring
• Workflow 2: Sentiment Analysis with AI
• Workflow 3: Crisis Detection & Alerting
• Workflow 4: Competitor Monitoring
• Workflow 5: Auto-response System
• Analytics & Reporting
• Best Practices
• กรณีศึกษา: E-commerce Brand
• FAQ
• สรุป

ทำไมต้องทำ Social Media Monitoring? {#why-monitoring}

ปัญหาของ Manual Monitoring

1. Time-consuming - เช็คหลายแพลตฟอร์มใช้เวลาหลายชั่วโมง/วัน
2. Miss important mentions - ไม่สามารถดูทุกโพสต์ได้
3. Slow response - ตอบช้า พลาดโอกาส หรือ crisis ลุกลาม
4. No insights - ไม่มีข้อมูลวิเคราะห์เชิงลึก
5. Can’t track competitors - ไม่รู้ว่าคู่แข่งทำอะไร

ประโยชน์ของ Auto Monitoring

✅ Real-time alerts - รู้ทันทีเมื่อมีคนพูดถึง ✅ Sentiment analysis - เข้าใจ emotion ของลูกค้า ✅ Crisis prevention - จับ negative trends ก่อนลุกลาม ✅ Competitive intelligence - ติดตามคู่แข่ง ✅ Data-driven decisions - insights จากข้อมูลจริง ✅ Save time - ประหยัดเวลา 10-20 ชม./สัปดาห์

Use Cases

Brand Protection:

• ติดตาม negative reviews
• จัดการ PR crisis
• Fake news/misinformation

Customer Service:

• ตอบคำถามลูกค้าทันที
• Handle complaints
• Provide support

Marketing:

• ค้นหา influencers
• Track campaign performance
• Identify trends

Sales:

• ค้นหา leads
• Monitor buying signals
• Competitive positioning

Social Platforms ที่รองรับ {#platforms}

1. Twitter (X)

API:

• Twitter API v2
• Search tweets, mentions, hashtags
• Real-time streaming

n8n Node: ✅ Official

Free tier: 1,500 tweets/month (2024) Paid: $100/month (50K tweets)

2. Facebook/Instagram

API:

• Meta Graph API
• Page mentions, comments
• Instagram mentions (Business accounts)

n8n Node: ✅ Official

Limitations: ต้องเป็น Business page/account

3. Reddit

API:

• Reddit API
• Search posts, comments
• Subreddit monitoring

n8n Node: ⚠️ Via HTTP

Free tier: 100 requests/minute

4. YouTube

API:

• YouTube Data API
• Search videos, comments
• Channel monitoring

n8n Node: ✅ Official

Free tier: 10,000 units/day

5. TikTok

API:

• TikTok Business API
• Limited (mainly for ads)

n8n Node: ⚠️ Via HTTP

Alternative: Web scraping (ระวังละเมิด ToS)

6. LinkedIn

API:

• LinkedIn API
• Company page monitoring

n8n Node: ⚠️ Limited

Free tier: Restricted

7. Brand Monitoring Tools

ใช้ tools ที่รวมหลาย platforms:

Brand24:

• Monitor all platforms
• Sentiment analysis built-in
• API available

Mention:

• Similar to Brand24
• Good API

Meltwater, Brandwatch:

• Enterprise-level
• Expensive

เครื่องมือและ APIs {#tools-apis}

Monitoring APIs

1. Twitter API v2

// Search recent tweets
GET https://api.twitter.com/2/tweets/search/recent
?query=AIUnlocked OR @AIUnlocked
&max_results=100

2. Reddit API

// Search posts
GET https://oauth.reddit.com/search
?q=AIUnlocked&sort=new&limit=100

3. YouTube Data API

// Search videos
GET https://www.googleapis.com/youtube/v3/search
?part=snippet&q=AIUnlocked&type=video

Sentiment Analysis

OpenAI ChatGPT ⭐ แนะนำ

• เข้าใจบริบทดี
• Multi-language
• ราคา: ~$0.01 per analysis

Google Cloud Natural Language API

• Sentiment score + magnitude
• Entity recognition

Azure Text Analytics

• Sentiment analysis
• Key phrase extraction

Local models:

• Hugging Face Transformers
• ฟรี แต่ต้อง host เอง

Data Storage

• Google Sheets (ง่าย, visual)
• Airtable (flexible database)
• PostgreSQL (scalable, analytics)
• BigQuery (big data analytics)

สถาปัตยกรรมระบบ {#architecture}

Complete Monitoring System

Schedule Trigger (every 15 min)
    ↓
Multi-platform Search
    ├─→ Twitter API
    ├─→ Facebook/Instagram API
    ├─→ Reddit API
    └─→ YouTube API
    ↓
Collect & Deduplicate
    ↓
AI Sentiment Analysis
    ↓
Filter & Classify
    ├─→ Crisis (urgent) → Immediate alert
    ├─→ Negative → Route to support team
    ├─→ Positive → Thank you auto-reply
    └─→ Neutral → Archive
    ↓
Save to Database
    ↓
Generate Reports
    ↓
Send Notifications

Data Flow

Raw mentions → Clean data → Sentiment → Classification → Action

Workflow 1: Brand Mentions Monitoring {#brand-mentions}

Setup: Monitor “AI Unlocked” mentions across platforms

1. Schedule Trigger

// Cron: Every 15 minutes
Schedule Trigger:
  Interval: 15 minutes

2. Twitter Search

// HTTP Request - Twitter API v2
Method: GET
URL: https://api.twitter.com/2/tweets/search/recent

Headers:
  Authorization: Bearer {{ $credentials.twitter.bearerToken }}

Query Parameters:
  query: (AIUnlocked OR "AI Unlocked" OR @AIUnlocked) -is:retweet
  max_results: 100
  tweet.fields: created_at,author_id,public_metrics,entities
  expansions: author_id
  start_time: {{ $json.lastCheckTime }} // ISO 8601 format

// Response:
{
  "data": [
    {
      "id": "1234567890",
      "text": "Just finished the AI course from @AIUnlocked - amazing! 🚀",
      "created_at": "2025-10-23T10:30:00.000Z",
      "author_id": "987654321",
      "public_metrics": {
        "like_count": 15,
        "retweet_count": 3,
        "reply_count": 2
      }
    }
  ],
  "includes": {
    "users": [
      {
        "id": "987654321",
        "username": "john_doe",
        "name": "John Doe"
      }
    ]
  }
}

3. Facebook/Instagram Search

// Facebook Graph API
// Search page mentions

Method: GET
URL: https://graph.facebook.com/v18.0/{{ PAGE_ID }}/tagged

Query Parameters:
  fields: message,from,created_time,permalink_url,reactions.summary(true)
  access_token: {{ $credentials.facebook.token }}
  since: {{ $json.lastCheckTime }}

4. Reddit Search

// Reddit API
Method: GET
URL: https://oauth.reddit.com/search

Headers:
  Authorization: Bearer {{ $credentials.reddit.token }}
  User-Agent: n8n-bot/1.0

Query Parameters:
  q: "AI Unlocked"
  sort: new
  limit: 100
  t: hour // time filter (hour, day, week)

5. Combine All Results

// Merge Node
Mode: Merge By Index
Input 1: Twitter data
Input 2: Facebook data
Input 3: Reddit data

// Or Function Node - Combine all
const allMentions = [
  ...$items('twitter').map(t => ({ ...t.json, source: 'twitter' })),
  ...$items('facebook').map(f => ({ ...f.json, source: 'facebook' })),
  ...$items('reddit').map(r => ({ ...r.json, source: 'reddit' }))
];

return allMentions.map(m => ({ json: m }));

6. Deduplicate

// Code Node - Remove duplicates
const seen = new Set();
const unique = [];

$input.all().forEach(item => {
  // Create unique key (text + author)
  const key = `${item.json.text}-${item.json.author}`;

  if (!seen.has(key)) {
    seen.add(key);
    unique.push(item);
  }
});

return unique;

7. Enrich Data

// Code Node - Standardize format
const standardized = $input.all().map(item => {
  const data = item.json;

  return {
    json: {
      id: data.id,
      source: data.source,
      text: data.text || data.message || data.body,
      author: data.author || data.from?.name || data.author_username,
      author_id: data.author_id || data.from?.id,
      created_at: data.created_at || data.created_time,
      url: data.url || data.permalink_url,
      engagement: {
        likes: data.public_metrics?.like_count || data.reactions?.summary?.total_count || data.score,
        comments: data.public_metrics?.reply_count || data.comments?.count || data.num_comments,
        shares: data.public_metrics?.retweet_count || 0
      },
      raw: data
    }
  };
});

return standardized;

Workflow 2: Sentiment Analysis with AI {#sentiment-analysis}

AI-powered Sentiment Detection

1. OpenAI Sentiment Analysis

// OpenAI Chat Node
Model: gpt-4
Temperature: 0.3

System Message:
You are a sentiment analysis expert.
Analyze social media posts and return sentiment classification.

User Message:
Analyze the sentiment of this social media post:

Text: "{{ $json.text }}"
Author: {{ $json.author }}
Platform: {{ $json.source }}

Return JSON format:
{
  "sentiment": "positive" | "negative" | "neutral",
  "confidence": 0.95, // 0-1
  "emotion": "joy" | "anger" | "sadness" | "fear" | "surprise" | "neutral",
  "urgency": "low" | "medium" | "high",
  "topics": ["topic1", "topic2"],
  "reasoning": "brief explanation"
}

Consider:
- Sarcasm and context
- Emojis and tone
- Cultural nuances
- Intent (complaint, praise, question, etc.)

// Example Output:
{
  "sentiment": "positive",
  "confidence": 0.92,
  "emotion": "joy",
  "urgency": "low",
  "topics": ["course quality", "learning experience"],
  "reasoning": "User expresses genuine satisfaction with the AI course using positive words and emojis"
}

2. Add Sentiment to Data

// Code Node - Merge sentiment with original data
const items = $input.all();

const enriched = items.map((item, index) => {
  const sentiment = $items('sentiment')[index].json;

  return {
    json: {
      ...item.json,
      sentiment: sentiment.sentiment,
      sentiment_confidence: sentiment.confidence,
      emotion: sentiment.emotion,
      urgency: sentiment.urgency,
      topics: sentiment.topics,
      sentiment_reasoning: sentiment.reasoning
    }
  };
});

return enriched;

3. Calculate Sentiment Score

// Code Node - Numerical sentiment score
// -1 (very negative) to +1 (very positive)

const calculateScore = (sentiment, confidence) => {
  const baseScores = {
    'positive': 1,
    'neutral': 0,
    'negative': -1
  };

  return baseScores[sentiment] * confidence;
};

items.forEach(item => {
  item.json.sentiment_score = calculateScore(
    item.json.sentiment,
    item.json.sentiment_confidence
  );
});

return items;

Workflow 3: Crisis Detection & Alerting {#crisis-detection}

Detect and Alert on Negative Trends

1. Crisis Detection Logic

// Code Node - Detect crisis signals

const detectCrisis = (items) => {
  const now = Date.now();
  const last24h = items.filter(i =>
    (now - new Date(i.json.created_at)) < 24 * 60 * 60 * 1000
  );

  // Crisis indicators:
  const negativePosts = last24h.filter(i => i.json.sentiment === 'negative');
  const negativeRate = negativePosts.length / last24h.length;

  const highEngagement = negativePosts.filter(i =>
    i.json.engagement.likes + i.json.engagement.comments + i.json.engagement.shares > 100
  );

  const urgentPosts = last24h.filter(i => i.json.urgency === 'high');

  // Crisis if:
  // 1. > 50% negative in last 24h
  // 2. OR > 5 high-engagement negative posts
  // 3. OR > 10 high-urgency posts

  const isCrisis =
    negativeRate > 0.5 ||
    highEngagement.length > 5 ||
    urgentPosts.length > 10;

  return {
    isCrisis,
    negativeCount: negativePosts.length,
    negativeRate: (negativeRate * 100).toFixed(1) + '%',
    highEngagementNegative: highEngagement.length,
    urgentCount: urgentPosts.length,
    topNegativePosts: negativePosts
      .sort((a, b) => b.json.engagement.likes - a.json.engagement.likes)
      .slice(0, 5)
  };
};

const crisisData = detectCrisis($input.all());

return { json: crisisData };

2. IF Node - Check if Crisis

// IF Node
{{ $json.isCrisis === true }}

3. Send Crisis Alert

// Slack Node - Critical Alert
Channel: #social-media-crisis
Message:
🚨 SOCIAL MEDIA CRISIS DETECTED! 🚨

Negative Rate: {{ $json.negativeRate }}
Negative Posts (24h): {{ $json.negativeCount }}
High Engagement Negative: {{ $json.highEngagementNegative }}
Urgent Posts: {{ $json.urgentCount }}

Top Negative Posts:
{{ $json.topNegativePosts.map(p => `
• ${p.text.substring(0, 100)}...
  Source: ${p.source}
  Engagement: ${p.engagement.likes} likes
  URL: ${p.url}
`).join('\n') }}

@channel - Please investigate immediately!

[View Full Report] [Take Action]

4. Email to Management

// Gmail Node
To: management@company.com
Subject: 🚨 Social Media Crisis Alert - Immediate Action Required
Body:
[HTML formatted email with details, graphs, action items]

5. Create Incident Ticket

// Jira / Linear / ClickUp Node
Create Issue:
  Title: Social Media Crisis - {{ $now }}
  Description: Crisis details...
  Priority: Critical
  Assignee: Social Media Manager
  Labels: crisis, social-media, urgent

Workflow 4: Competitor Monitoring {#competitor-monitoring}

Track what competitors are doing

1. Competitor Keywords

// Set Node - Competitor keywords
const competitors = [
  { name: 'CompetitorA', keywords: ['CompetitorA', '@competitorA', 'hashtag1'] },
  { name: 'CompetitorB', keywords: ['CompetitorB', '@competitorB', 'hashtag2'] },
  { name: 'CompetitorC', keywords: ['CompetitorC', '@competitorC', 'hashtag3'] }
];

return competitors.map(c => ({ json: c }));

2. Search for Each Competitor

// Loop through competitors
// Twitter API search for each

Query: {{ $json.keywords.join(' OR ') }}

3. Analyze Competitor Activity

// Code Node - Analyze what they're doing

const analyzeCompetitor = (posts) => {
  return {
    totalPosts: posts.length,
    avgEngagement: calculateAvg(posts.map(p => p.engagement.total)),
    topPosts: posts.sort((a, b) => b.engagement.total - a.engagement.total).slice(0, 5),
    commonTopics: extractTopics(posts),
    sentiment: {
      positive: posts.filter(p => p.sentiment === 'positive').length,
      negative: posts.filter(p => p.sentiment === 'negative').length,
      neutral: posts.filter(p => p.sentiment === 'neutral').length
    },
    trends: detectTrends(posts)
  };
};

4. Compare with Your Brand

// Code Node - Competitive analysis
const comparison = {
  yourBrand: analyzeCompetitor(yourPosts),
  competitorA: analyzeCompetitor(competitorAPosts),
  competitorB: analyzeCompetitor(competitorBPosts),

  insights: {
    whoIsWinning: determineLeader([yourBrand, competitorA, competitorB]),
    engagementGap: calculateGap(yourBrand, competitors),
    opportunitiesDetected: findOpportunities(competitors)
  }
};

5. Weekly Competitor Report

// Google Sheets Node - Update dashboard
Sheet: Competitor Intelligence
Data:
  Week: {{ $json.week }}
  Your Engagement: {{ $json.yourBrand.avgEngagement }}
  Competitor A: {{ $json.competitorA.avgEngagement }}
  Competitor B: {{ $json.competitorB.avgEngagement }}
  Insights: {{ $json.insights.summary }}

Workflow 5: Auto-response System {#auto-response}

Automatically respond to mentions

1. Filter Mentions Needing Response

// IF Node - Response criteria
{{
  $json.sentiment === 'positive' ||
  ($json.sentiment === 'neutral' && $json.text.includes('?')) ||
  ($json.sentiment === 'negative' && $json.urgency === 'high')
}}

2. Generate Appropriate Response

// OpenAI Chat Node
Model: gpt-4
Temperature: 0.7

System Message:
You are the social media manager for AI Unlocked.
Brand voice: Friendly, helpful, professional.

Generate an appropriate response to this social media post:

Original Post:
{{ $json.text }}

Sentiment: {{ $json.sentiment }}
Emotion: {{ $json.emotion }}
Topics: {{ $json.topics.join(', ') }}

Guidelines:
- If positive: Thank them warmly
- If question: Provide helpful answer or direct to support
- If negative: Acknowledge concern, apologize if needed, offer solution
- Keep it concise (< 280 chars for Twitter)
- Use appropriate tone for platform
- Include call-to-action when relevant

Return only the response text, no JSON.

// Example Output:
"Thank you so much for the kind words! 😊 We're thrilled the course helped you. Keep learning and feel free to reach out if you have questions! 🚀"

3. Human Approval (Optional)

// For sensitive/negative posts, get human approval

// Slack Node - Request approval
Channel: #social-media-responses
Message:
📝 Response Draft - Approval Needed

Original Post:
{{ $json.text }}

Draft Response:
{{ $json.draftResponse }}

Sentiment: {{ $json.sentiment }}
Urgency: {{ $json.urgency }}

Approve: /approve {{ $json.id }}
Edit: /edit {{ $json.id }}
Reject: /reject {{ $json.id }}

// Wait for approval
// Webhook receives approval command

4. Post Response

// Switch Node - Route by platform

// Twitter:
Twitter Node - Reply to Tweet
Reply To: {{ $json.tweet_id }}
Text: {{ $json.response }}

// Facebook:
Facebook Node - Comment on Post
Post ID: {{ $json.post_id }}
Message: {{ $json.response }}

// Instagram:
Instagram Node - Comment
Media ID: {{ $json.media_id }}
Text: {{ $json.response }}

5. Log Response

// Google Sheets Node - Log all responses
Sheet: Response Log
Data:
  Date: {{ $now }}
  Platform: {{ $json.source }}
  Original Post: {{ $json.text }}
  Our Response: {{ $json.response }}
  Sentiment: {{ $json.sentiment }}
  Approved By: {{ $json.approver || 'Auto' }}

Analytics & Reporting {#analytics}

Daily/Weekly Reports

1. Aggregate Metrics

// Code Node - Calculate daily metrics
const today = $input.all();

const metrics = {
  date: new Date().toISOString().split('T')[0],
  totalMentions: today.length,
  byPlatform: {
    twitter: today.filter(i => i.json.source === 'twitter').length,
    facebook: today.filter(i => i.json.source === 'facebook').length,
    instagram: today.filter(i => i.json.source === 'instagram').length,
    reddit: today.filter(i => i.json.source === 'reddit').length
  },
  sentiment: {
    positive: today.filter(i => i.json.sentiment === 'positive').length,
    neutral: today.filter(i => i.json.sentiment === 'neutral').length,
    negative: today.filter(i => i.json.sentiment === 'negative').length
  },
  avgSentimentScore: calculateAvg(today.map(i => i.json.sentiment_score)),
  totalEngagement: sumEngagement(today),
  topPosts: today.sort((a, b) => b.json.engagement.total - a.json.engagement.total).slice(0, 10),
  topTopics: extractFrequentTopics(today),
  responseRate: calculateResponseRate(today)
};

return { json: metrics };

2. Save to Database

// PostgreSQL Node
INSERT INTO daily_metrics (
  date, total_mentions, positive, neutral, negative,
  avg_sentiment_score, total_engagement, response_rate
) VALUES (
  :date, :totalMentions, :positive, :neutral, :negative,
  :avgSentimentScore, :totalEngagement, :responseRate
)

3. Generate Visual Report

// Google Sheets Node - Update dashboard
Sheet: Analytics Dashboard

// Create charts:
- Line chart: Mentions over time
- Pie chart: Sentiment distribution
- Bar chart: Platform breakdown
- Table: Top posts

4. Weekly Summary Email

// Schedule: Every Monday 9 AM

// Gmail Node
To: team@company.com
Subject: 📊 Social Media Weekly Report - {{ $json.weekStart }} to {{ $json.weekEnd }}

Body:
<!DOCTYPE html>
<html>
<body>
  <h2>Social Media Performance - Week {{ $json.weekNumber }}</h2>

  <h3>Overview</h3>
  <ul>
    <li>Total Mentions: {{ $json.totalMentions }} ({{ $json.changePercent }}% vs last week)</li>
    <li>Avg Sentiment: {{ $json.avgSentiment }} ({{ $json.sentimentTrend }})</li>
    <li>Response Rate: {{ $json.responseRate }}%</li>
  </ul>

  <h3>Sentiment Breakdown</h3>
  <ul>
    <li>Positive: {{ $json.positive }} ({{ $json.positivePercent }}%)</li>
    <li>Neutral: {{ $json.neutral }} ({{ $json.neutralPercent }}%)</li>
    <li>Negative: {{ $json.negative }} ({{ $json.negativePercent }}%)</li>
  </ul>

  <h3>Top Performing Posts</h3>
  {{ $json.topPosts.map(post => `
    <div style="border: 1px solid #ddd; padding: 10px; margin: 10px 0;">
      <p><strong>${post.source}</strong> - ${post.engagement.total} engagements</p>
      <p>${post.text}</p>
      <a href="${post.url}">View Post</a>
    </div>
  `).join('') }}

  <h3>Key Insights</h3>
  <ul>
    {{ $json.insights.map(insight => `<li>${insight}</li>`).join('') }}
  </ul>

  <p><a href="https://dashboard.company.com/social">View Full Dashboard</a></p>
</body>
</html>

Best Practices {#best-practices}

1. Keywords & Queries

Good keyword strategy:

const keywords = [
  // Brand names
  'AIUnlocked',
  'AI Unlocked',
  '@AIUnlocked',

  // Variations & misspellings
  'AI-Unlocked',
  'A.I. Unlocked',

  // Hashtags
  '#AIUnlocked',
  '#AIคอร์ส',

  // Products/Services
  'คอร์ส AI',
  'Vibe Coding',

  // CEO/Founders (if applicable)
  '@FounderName'
];

// Exclude noise:
const excludeKeywords = [
  '-spam',
  '-advertisement',
  '-bot'
];

2. Rate Limiting

// Respect API rate limits

// Twitter: 450 requests / 15 min
// Reddit: 100 requests / min
// Facebook: varies by endpoint

// Solution: Queue + throttle
const REQUESTS_PER_MINUTE = 50;
await sleep(60000 / REQUESTS_PER_MINUTE); // between requests

3. Data Retention

// Keep data organized and clean

// Hot data (last 7 days): Keep all details
// Warm data (7-30 days): Keep aggregated
// Cold data (> 30 days): Archive or delete

// Cleanup workflow:
Schedule: Daily
→ Archive data > 30 days to S3
→ Delete raw data > 90 days
→ Keep only metrics

4. Alert Fatigue

// Avoid too many notifications

// Good alert strategy:
- Crisis: Immediate (Slack + Email + SMS)
- High urgency: Within 1 hour (Slack)
- Medium: Daily digest (Email)
- Low: Weekly report (Email)

// Implement alert throttling:
if (lastAlertSent < 1 hour ago) {
  skip(); // don't spam
}

5. Privacy & Compliance

// Respect user privacy

// Don't store:
- Private messages
- Personal info (unless consent)
- Deleted posts

// Anonymize data:
mentions.forEach(m => {
  m.author = hashUserId(m.author); // anonymize
  delete m.email; // remove PII
});

กรณีศึกษา: E-commerce Brand {#case-study}

บริษัท: Fashion E-commerce

ความท้าทาย:

• ลูกค้าร้องเรียนบน social media → ตอบช้า
• ไม่รู้ว่าคู่แข่งทำอะไร
• พลาด influencer opportunities
• ไม่มีข้อมูล insights

Solution ด้วย n8n Social Monitoring:

Setup:

Platforms Monitored:
- Facebook Page (posts, comments)
- Instagram (mentions, hashtags #brandname)
- Twitter (brand mentions, product names)
- Pantip (forum threads)
- YouTube (video reviews)

Keywords:
- Brand name + variations
- Product names
- Hashtags
- Competitor names

Frequency: Every 15 minutes

Workflows:

1. Real-time Monitoring

Every 15 min:
→ Search all platforms
→ AI sentiment analysis
→ Route by sentiment:
  - Negative → Slack alert (support team)
  - Positive → Auto thank-you
  - Question → Draft answer → human approval
→ Save to database

2. Crisis Detection

Daily at 9 AM:
→ Analyze last 24h data
→ Detect negative trends
→ IF crisis:
  → Alert management (email + SMS)
  → Create emergency meeting
  → Prepare PR statement

3. Influencer Discovery

Weekly:
→ Find high-engagement positive posts
→ Identify potential influencers (> 10K followers)
→ Add to prospect list
→ Email marketing team

4. Competitor Intelligence

Weekly:
→ Monitor 3 main competitors
→ Analyze their campaigns
→ Identify what works
→ Generate insights report

ผลลัพธ์ (6 เดือน):

📊 Metrics:

• Monitored: 15,000+ mentions/เดือน
• Response time: 4 hours → 30 minutes (↓ 87.5%)
• Customer satisfaction: 3.2 → 4.5/5 (↑ 40%)

🚨 Crisis Prevention:

• จับ 3 potential crises ก่อนลุกลาม
• ประหยัดค่า PR มากกว่า 500,000 บาท

💡 Insights:

• ค้นพบ 15 micro-influencers
• ปรับ campaign ตาม feedback → engagement ↑ 60%
• เรียนรู้ competitor strategies

⏱️ Time Saved:

• ประหยัดเวลา 20 ชม./สัปดาห์
• Team โฟกัสที่ strategy แทน manual monitoring

💰 ROI:

ลงทุน:
- n8n: $20/เดือน
- APIs: $50/เดือน
- OpenAI: $100/เดือน
Total: $170/เดือน

ประหยัด/รายได้เพิ่ม:
- เวลา team: ~$2,000/เดือน
- ป้องกัน crisis: ~$10,000/เดือน (เฉลี่ย)
- Influencer partnerships: ~$5,000/เดือน

ROI: ~9,900%

FAQ {#faq}

Q1: ต้อง monitor 24/7 ไหม?

A: ไม่จำเป็น

แนะนำ:

• Real-time monitoring: ทุก 15-30 นาที (ช่วงทำงาน)
• Off-hours: ทุก 1-2 ชั่วโมง
• Overnight: ทุก 4-6 ชั่วโมง (หรือปิดก็ได้)

ยกเว้น:

• Brand ใหญ่มาก หรือ
• มี crisis ที่กำลังเกิด → monitor ทุก 5-10 นาที

Q2: Sentiment analysis แม่นยำแค่ไหน?

A: ขึ้นกับ AI model

ChatGPT-4:

• Accuracy: ~85-90%
• เข้าใจ sarcasm ได้บ้าง
• รองรับภาษาไทยดี

Tips เพิ่ม accuracy:

• ให้บริบท (platform, author type)
• ใช้ confidence score threshold
• Human review สำหรับ critical cases

Q3: ราคาเท่าไหร่?

Setup พื้นฐาน (SME):

n8n self-hosted: ฟรี + VPS $10
Twitter API: $100/เดือน (50K tweets)
OpenAI API: ~$50/เดือน (sentiment analysis)
Total: ~$160/เดือน

vs SaaS tools:

• Brand24: $79-399/เดือน
• Mention: $99-999/เดือน
• Brandwatch: $1,000+/เดือน

n8n ถูกกว่า 5-10 เท่า!

Q4: ทำได้หลายภาษาไหม?

A: ได้!

ChatGPT รองรับ:

• ภาษาไทย ✅
• อังกฤษ ✅
• จีน, ญี่ปุ่น, เกาหลี ✅
• 50+ ภาษาอื่นๆ ✅

Tip: ระบุภาษาใน prompt

System: "Analyze sentiment of this Thai social media post..."

Q5: Auto-reply ปลอดภัยไหม?

A: ปลอดภัย ถ้ามี safeguards

Best practices:

• Human approval สำหรับ negative posts
• Limit auto-reply (เช่น แค่ positive/thank you)
• Review AI responses ก่อน deploy
• มี kill switch (หยุด auto-reply ได้ทันที)

Don’t auto-reply:

• Negative complaints
• Legal issues
• Crisis situations
• Sensitive topics

Q6: Track competitors ละเมิดกฎหมายไหม?

A: ไม่ ถ้าเป็นข้อมูล public

Legal: ✅ Monitor public posts ✅ Analyze public data ✅ Competitive intelligence

Illegal: ❌ Hack accounts ❌ Scrape private data ❌ Impersonate competitors

Ethical:

• ใช้ข้อมูล public เท่านั้น
• Respect ToS ของแต่ละแพลตฟอร์ม
• Don’t spam/harass

สรุป {#conclusion}

Social Media Monitoring ด้วย n8n + AI ช่วยให้คุณรู้ทุกอย่างที่เกิดขึ้นกับ brand แบบ real-time ตอบสนองเร็ว และป้องกัน crisis

ข้อดีหลัก

✅ Real-time monitoring รู้ทันทีเมื่อมีคนพูดถึง ✅ AI sentiment analysis เข้าใจ emotions ของลูกค้า ✅ Crisis prevention จับปัญหาก่อนลุกลาม ✅ Auto-response ตอบลูกค้าเร็วขึ้น ✅ Competitive intelligence รู้ว่าคู่แข่งทำอะไร ✅ Data-driven insights ตัดสินใจด้วยข้อมูล ✅ Cost-effective ถูกกว่า SaaS tools 5-10 เท่า

เริ่มต้นอย่างไร

1. เลือก platforms ที่สำคัญที่สุด (2-3 platforms)
2. Setup n8n + APIs
3. สร้าง basic monitoring workflow
4. เพิ่ม sentiment analysis
5. Setup alerts
6. Scale up

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บทความที่เกี่ยวข้อง

• n8n Email Marketing Automation
• สร้างระบบโพสต์คอนเทนต์อัตโนมัติด้วย n8n
• AI Copywriting สำหรับ Marketing
• n8n CRM Automation
• คอร์ส AI สำหรับผู้เริ่มต้น

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สนใจเรียนคอร์ส n8n และ AI Automation? เรียนรู้การสร้างระบบ monitoring และ automation ที่ช่วยปกป้อง brand 👉 ดูคอร์สออนไลน์ | ติดต่อสอบถาม