Social Media Sentiment Analysis Dashboard with Custom AI for Twitter, Reddit…

The workflow JSON

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{
  "id": "SocialMediaSentimentDashboard2024",
  "meta": {
    "templateCredsSetupCompleted": false
  },
  "name": "Social Media Sentiment Dashboard",
  "tags": [
    "social-media",
    "sentiment-analysis",
    "brand-monitoring",
    "crisis-management",
    "dashboard",
    "automation"
  ],
  "nodes": [
    {
      "id": "social-media-trigger",
      "name": "Social Media Monitor Trigger",
      "type": "n8n-nodes-base.scheduleTrigger",
      "position": [
        400,
        800
      ],
      "parameters": {
        "rule": {
          "interval": [
            {
              "field": "hours",
              "hoursInterval": 4
            }
          ]
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "manual-sentiment-trigger",
      "name": "Manual Sentiment Check Webhook",
      "type": "n8n-nodes-base.webhook",
      "position": [
        400,
        600
      ],
      "parameters": {
        "path": "sentiment-webhook",
        "options": {
          "noResponseBody": false
        },
        "httpMethod": "GET"
      },
      "typeVersion": 1.1
    },
    {
      "id": "twitter-scraper",
      "name": "Twitter Brand Mentions Scraper",
      "type": "n8n-nodes-scrapegraphai.scrapegraphAi",
      "position": [
        800,
        500
      ],
      "parameters": {
        "userPrompt": "Extract social media posts and mentions from this Twitter search page. Focus on posts that mention our brand or company. Use this schema for response: { \"request_id\": \"unique_id\", \"status\": \"completed\", \"platform\": \"Twitter\", \"posts\": [{ \"post_id\": \"tweet_id\", \"author\": \"@username\", \"author_followers\": 1500, \"content\": \"Full tweet text here\", \"timestamp\": \"2024-01-15T10:30:00Z\", \"likes\": 25, \"retweets\": 5, \"replies\": 3, \"post_url\": \"https://twitter.com/user/status/123456\", \"mentions\": [\"@YourBrand\", \"@competitor\"], \"hashtags\": [\"#tech\", \"#innovation\"], \"media_urls\": [\"https://image-url.com\"], \"post_type\": \"original\", \"language\": \"en\", \"verified_account\": false }] }",
        "websiteUrl": "https://twitter.com/search?q=%22YourBrand%22%20OR%20%22YourCompany%22&src=typed_query&f=live"
      },
      "credentials": {
        "scrapegraphAIApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "reddit-scraper",
      "name": "Reddit Brand Discussion Scraper",
      "type": "n8n-nodes-scrapegraphai.scrapegraphAi",
      "position": [
        800,
        700
      ],
      "parameters": {
        "userPrompt": "Extract Reddit posts and comments mentioning our brand. Focus on discussions, reviews, and mentions. Use this schema: { \"request_id\": \"unique_id\", \"status\": \"completed\", \"platform\": \"Reddit\", \"posts\": [{ \"post_id\": \"reddit_post_id\", \"subreddit\": \"r/technology\", \"author\": \"username\", \"title\": \"Post title if applicable\", \"content\": \"Full post or comment text\", \"timestamp\": \"2024-01-15T10:30:00Z\", \"upvotes\": 150, \"downvotes\": 10, \"score\": 140, \"comments_count\": 25, \"post_url\": \"https://reddit.com/r/sub/comments/abc123\", \"post_type\": \"post\", \"flair\": \"Discussion\", \"is_pinned\": false, \"is_locked\": false }] }",
        "websiteUrl": "https://www.reddit.com/search/?q=%22YourBrand%22&type=comment&sort=new"
      },
      "credentials": {
        "scrapegraphAIApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "linkedin-scraper",
      "name": "LinkedIn Professional Mentions Scraper",
      "type": "n8n-nodes-scrapegraphai.scrapegraphAi",
      "position": [
        800,
        900
      ],
      "parameters": {
        "userPrompt": "Extract LinkedIn posts and professional discussions about our brand. Focus on business content and professional opinions. Use this schema: { \"request_id\": \"unique_id\", \"status\": \"completed\", \"platform\": \"LinkedIn\", \"posts\": [{ \"post_id\": \"linkedin_activity_id\", \"author\": \"Full Name\", \"author_title\": \"CEO at Company\", \"author_company\": \"Company Name\", \"content\": \"Full LinkedIn post text\", \"timestamp\": \"2024-01-15T10:30:00Z\", \"likes\": 45, \"comments\": 8, \"shares\": 12, \"post_url\": \"https://linkedin.com/posts/activity-123456\", \"post_type\": \"article\", \"industry_tags\": [\"Technology\", \"Innovation\"], \"is_promoted\": false }] }",
        "websiteUrl": "https://www.linkedin.com/search/results/content/?keywords=%22YourBrand%22&origin=GLOBAL_SEARCH_HEADER&sid=123"
      },
      "credentials": {
        "scrapegraphAIApi": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "sentiment-analysis-processor",
      "name": "Advanced Sentiment Analysis & Brand Intelligence",
      "type": "n8n-nodes-base.code",
      "notes": "Processes social media data with\nadvanced sentiment analysis and\nbrand intelligence algorithms",
      "position": [
        1400,
        700
      ],
      "parameters": {
        "jsCode": "// Advanced Social Media Sentiment Analysis & Intelligence Processing\nconst inputData = $input.all();\nconst processedPosts = [];\n\n// Configuration for sentiment analysis and brand monitoring\nconst sentimentConfig = {\n  brandKeywords: {\n    'your_brand': ['YourBrand', 'YourCompany', 'YourProduct', '@yourbrand'],\n    'competitors': {\n      'Competitor1': ['Competitor1', 'CompetitorProduct1', '@competitor1'],\n      'Competitor2': ['Competitor2', 'CompetitorProduct2', '@competitor2'],\n      'Competitor3': ['Competitor3', 'CompetitorProduct3', '@competitor3']\n    }\n  },\n  sentimentKeywords: {\n    positive: {\n      strong: ['amazing', 'excellent', 'outstanding', 'fantastic', 'incredible', 'perfect', 'brilliant', 'phenomenal'],\n      moderate: ['good', 'great', 'nice', 'helpful', 'useful', 'recommended', 'satisfied', 'happy', 'pleased'],\n      mild: ['ok', 'fine', 'decent', 'adequate', 'reasonable', 'acceptable']\n    },\n    negative: {\n      strong: ['terrible', 'awful', 'horrible', 'disgusting', 'worst', 'hate', 'disaster', 'nightmare'],\n      moderate: ['bad', 'poor', 'disappointing', 'frustrated', 'annoying', 'waste', 'regret', 'failed'],\n      mild: ['meh', 'not great', 'could be better', 'issues', 'problems', 'concerns']\n    },\n    neutral: ['said', 'mentioned', 'announced', 'reported', 'according', 'stated', 'explained', 'noted']\n  },\n  influencerThresholds: {\n    twitter: { mega: 1000000, macro: 100000, micro: 10000, nano: 1000 },\n    linkedin: { thought_leader: 50000, professional: 10000, specialist: 5000 },\n    reddit: { power_user: 10000, active_user: 1000, regular_user: 100 }\n  }\n};\n\n// Advanced sentiment analysis function\nfunction analyzeSentiment(text, platform) {\n  const cleanText = text.toLowerCase();\n  let sentimentScore = 0;\n  let sentimentStrength = 0;\n  let emotionalIntensity = 0;\n  \n  // Analyze positive sentiment\n  Object.entries(sentimentConfig.sentimentKeywords.positive).forEach(([strength, words]) => {\n    const multiplier = strength === 'strong' ? 3 : strength === 'moderate' ? 2 : 1;\n    words.forEach(word => {\n      if (cleanText.includes(word)) {\n        sentimentScore += multiplier;\n        sentimentStrength += multiplier;\n        emotionalIntensity += multiplier * 0.5;\n      }\n    });\n  });\n  \n  // Analyze negative sentiment\n  Object.entries(sentimentConfig.sentimentKeywords.negative).forEach(([strength, words]) => {\n    const multiplier = strength === 'strong' ? 3 : strength === 'moderate' ? 2 : 1;\n    words.forEach(word => {\n      if (cleanText.includes(word)) {\n        sentimentScore -= multiplier;\n        sentimentStrength += multiplier;\n        emotionalIntensity += multiplier * 0.5;\n      }\n    });\n  });\n  \n  // Check for emotional indicators\n  const emotionalMarkers = {\n    excitement: ['!', '!!!', '\ud83d\udd25', '\ud83d\ude80', '\ud83d\udcaf', '\u2b50'],\n    disappointment: ['\ud83d\ude1e', '\ud83d\ude22', '\ud83d\udc94', '\ud83e\udd26', '\ud83d\ude24'],\n    question: ['?', '??', '\ud83e\udd14'],\n    sarcasm: ['/s', 'sure...', 'yeah right', 'obviously']\n  };\n  \n  let emotionalContext = 'neutral';\n  Object.entries(emotionalMarkers).forEach(([emotion, markers]) => {\n    markers.forEach(marker => {\n      if (text.includes(marker)) {\n        emotionalContext = emotion;\n        emotionalIntensity += 0.5;\n      }\n    });\n  });\n  \n  // Determine overall sentiment\n  let finalSentiment = 'neutral';\n  let confidence = 0;\n  \n  if (sentimentScore > 0) {\n    finalSentiment = sentimentScore >= 3 ? 'very_positive' : 'positive';\n    confidence = Math.min(sentimentStrength / 5, 1);\n  } else if (sentimentScore < 0) {\n    finalSentiment = sentimentScore <= -3 ? 'very_negative' : 'negative';\n    confidence = Math.min(sentimentStrength / 5, 1);\n  } else {\n    confidence = 0.3; // Default neutral confidence\n  }\n  \n  return {\n    sentiment: finalSentiment,\n    score: sentimentScore,\n    confidence: Math.round(confidence * 100),\n    emotional_intensity: Math.round(emotionalIntensity * 100) / 100,\n    emotional_context: emotionalContext,\n    strength: sentimentStrength\n  };\n}\n\n// Brand mention detection function\nfunction detectBrandMentions(text) {\n  const mentions = {\n    your_brand: [],\n    competitors: [],\n    total_mentions: 0\n  };\n  \n  const cleanText = text.toLowerCase();\n  \n  // Check for your brand mentions\n  sentimentConfig.brandKeywords.your_brand.forEach(keyword => {\n    if (cleanText.includes(keyword.toLowerCase())) {\n      mentions.your_brand.push({\n        keyword: keyword,\n        context_snippet: extractContext(text, keyword, 50)\n      });\n      mentions.total_mentions++;\n    }\n  });\n  \n  // Check for competitor mentions\n  Object.entries(sentimentConfig.brandKeywords.competitors).forEach(([competitor, keywords]) => {\n    keywords.forEach(keyword => {\n      if (cleanText.includes(keyword.toLowerCase())) {\n        mentions.competitors.push({\n          competitor: competitor,\n          keyword: keyword,\n          context_snippet: extractContext(text, keyword, 50)\n        });\n        mentions.total_mentions++;\n      }\n    });\n  });\n  \n  return mentions;\n}\n\n// Extract context around mentions\nfunction extractContext(text, keyword, radius) {\n  const index = text.toLowerCase().indexOf(keyword.toLowerCase());\n  if (index === -1) return '';\n  \n  const start = Math.max(0, index - radius);\n  const end = Math.min(text.length, index + keyword.length + radius);\n  return text.substring(start, end).trim();\n}\n\n// Influencer tier classification\nfunction classifyInfluencer(platform, followers, engagement) {\n  const thresholds = sentimentConfig.influencerThresholds[platform.toLowerCase()] || {};\n  \n  if (platform.toLowerCase() === 'twitter') {\n    if (followers >= thresholds.mega) return 'mega_influencer';\n    if (followers >= thresholds.macro) return 'macro_influencer';\n    if (followers >= thresholds.micro) return 'micro_influencer';\n    if (followers >= thresholds.nano) return 'nano_influencer';\n    return 'regular_user';\n  }\n  \n  if (platform.toLowerCase() === 'linkedin') {\n    if (followers >= thresholds.thought_leader) return 'thought_leader';\n    if (followers >= thresholds.professional) return 'professional_influencer';\n    if (followers >= thresholds.specialist) return 'specialist';\n    return 'professional';\n  }\n  \n  if (platform.toLowerCase() === 'reddit') {\n    const karma = followers; // Using followers as karma approximation\n    if (karma >= thresholds.power_user) return 'power_user';\n    if (karma >= thresholds.active_user) return 'active_user';\n    if (karma >= thresholds.regular_user) return 'regular_user';\n    return 'new_user';\n  }\n  \n  return 'unknown';\n}\n\n// Calculate engagement rate\nfunction calculateEngagementRate(post, platform) {\n  let totalEngagement = 0;\n  let followerCount = 1; // Avoid division by zero\n  \n  if (platform.toLowerCase() === 'twitter') {\n    totalEngagement = (post.likes || 0) + (post.retweets || 0) + (post.replies || 0);\n    followerCount = post.author_followers || 1;\n  } else if (platform.toLowerCase() === 'linkedin') {\n    totalEngagement = (post.likes || 0) + (post.comments || 0) + (post.shares || 0);\n    followerCount = 1000; // Default estimate for LinkedIn\n  } else if (platform.toLowerCase() === 'reddit') {\n    totalEngagement = (post.upvotes || 0) + (post.comments_count || 0);\n    followerCount = 100; // Reddit doesn't have followers in the same way\n  }\n  \n  return totalEngagement / followerCount;\n}\n\n// Process priority calculation\nfunction calculatePriority(post, sentiment, mentions, influencerTier, engagementRate) {\n  let priorityScore = 0;\n  \n  // Sentiment impact\n  if (sentiment.sentiment === 'very_negative') priorityScore += 10;\n  else if (sentiment.sentiment === 'negative') priorityScore += 7;\n  else if (sentiment.sentiment === 'very_positive') priorityScore += 5;\n  else if (sentiment.sentiment === 'positive') priorityScore += 3;\n  \n  // Brand mention impact\n  if (mentions.your_brand.length > 0) priorityScore += 8;\n  if (mentions.competitors.length > 0) priorityScore += 4;\n  \n  // Influencer tier impact\n  const influencerScores = {\n    'mega_influencer': 10, 'thought_leader': 10, 'power_user': 8,\n    'macro_influencer': 8, 'professional_influencer': 7, 'active_user': 6,\n    'micro_influencer': 6, 'specialist': 5, 'regular_user': 3,\n    'nano_influencer': 4, 'professional': 4, 'new_user': 1\n  };\n  priorityScore += influencerScores[influencerTier] || 1;\n  \n  // Engagement rate impact\n  if (engagementRate > 0.1) priorityScore += 5;\n  else if (engagementRate > 0.05) priorityScore += 3;\n  else if (engagementRate > 0.01) priorityScore += 1;\n  \n  // Determine priority level\n  if (priorityScore >= 15) return 'Critical';\n  if (priorityScore >= 10) return 'High';\n  if (priorityScore >= 6) return 'Medium';\n  return 'Low';\n}\n\n// Process each input from different social media platforms\ninputData.forEach(input => {\n  if (input.json.result && input.json.result.posts) {\n    const platform = input.json.result.platform || 'Unknown';\n    \n    input.json.result.posts.forEach(post => {\n      const sentimentAnalysis = analyzeSentiment(post.content, platform);\n      const brandMentions = detectBrandMentions(post.content);\n      const engagementRate = calculateEngagementRate(post, platform);\n      \n      let followerCount = 0;\n      if (platform.toLowerCase() === 'twitter') followerCount = post.author_followers || 0;\n      else if (platform.toLowerCase() === 'reddit') followerCount = post.score || 0;\n      else followerCount = 1000; // Default for LinkedIn\n      \n      const influencerTier = classifyInfluencer(platform, followerCount, engagementRate);\n      const priority = calculatePriority(post, sentimentAnalysis, brandMentions, influencerTier, engagementRate);\n      \n      processedPosts.push({\n        json: {\n          // Post Identification\n          post_id: post.post_id || `${platform.toLowerCase()}_${Date.now()}_${Math.random().toString(36).substr(2, 5)}`,\n          platform: platform,\n          post_url: post.post_url,\n          timestamp: post.timestamp,\n          \n          // Author Information\n          author: post.author,\n          author_title: post.author_title || '',\n          author_company: post.author_company || '',\n          author_followers: followerCount,\n          influencer_tier: influencerTier,\n          verified_account: post.verified_account || false,\n          \n          // Content Analysis\n          content: post.content,\n          content_length: post.content.length,\n          language: post.language || 'unknown',\n          post_type: post.post_type || 'post',\n          \n          // Engagement Metrics\n          likes: post.likes || post.upvotes || 0,\n          shares: post.retweets || post.shares || 0,\n          comments: post.replies || post.comments || post.comments_count || 0,\n          engagement_rate: Math.round(engagementRate * 10000) / 100, // Percentage with 2 decimals\n          total_engagement: (post.likes || post.upvotes || 0) + (post.retweets || post.shares || 0) + (post.replies || post.comments || post.comments_count || 0),\n          \n          // Sentiment Analysis Results\n          sentiment: sentimentAnalysis.sentiment,\n          sentiment_score: sentimentAnalysis.score,\n          sentiment_confidence: sentimentAnalysis.confidence,\n          emotional_intensity: sentimentAnalysis.emotional_intensity,\n          emotional_context: sentimentAnalysis.emotional_context,\n          \n          // Brand Intelligence\n          mentions_your_brand: brandMentions.your_brand.length > 0,\n          your_brand_mentions: brandMentions.your_brand,\n          competitor_mentions: brandMentions.competitors,\n          total_brand_mentions: brandMentions.total_mentions,\n          \n          // Priority and Classification\n          priority_level: priority,\n          requires_response: priority === 'Critical' || (priority === 'High' && sentimentAnalysis.sentiment.includes('negative')),\n          crisis_potential: sentimentAnalysis.sentiment === 'very_negative' && brandMentions.your_brand.length > 0 && ['mega_influencer', 'thought_leader', 'power_user'].includes(influencerTier),\n          \n          // Additional Context\n          hashtags: post.hashtags || [],\n          mentions: post.mentions || [],\n          media_urls: post.media_urls || [],\n          subreddit: post.subreddit || '',\n          industry_tags: post.industry_tags || [],\n          \n          // Metadata\n          processed_at: new Date().toISOString(),\n          monitoring_session: `session_${new Date().toISOString().split('T')[0].replace(/-/g, '')}`\n        }\n      });\n    });\n  }\n});\n\nreturn processedPosts;"
      },
      "notesInFlow": true,
      "typeVersion": 2
    },
    {
      "id": "google-sheets-dashboard",
      "name": "Google Sheets Sentiment Dashboard",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        2200,
        700
      ],
      "parameters": {
        "columns": {
          "value": {},
          "schema": [
            {
              "id": "post_id",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Post ID",
              "defaultMatch": true,
              "canBeUsedToMatch": true
            },
            {
              "id": "platform",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Platform",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "author",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Author",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "content",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Content",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "sentiment",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Sentiment",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "sentiment_confidence",
              "type": "number",
              "display": true,
              "required": false,
              "displayName": "Confidence %",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "mentions_your_brand",
              "type": "boolean",
              "display": true,
              "required": false,
              "displayName": "Mentions Brand",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "priority_level",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Priority",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "engagement_rate",
              "type": "number",
              "display": true,
              "required": false,
              "displayName": "Engagement Rate %",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "influencer_tier",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Influencer Tier",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "crisis_potential",
              "type": "boolean",
              "display": true,
              "required": false,
              "displayName": "Crisis Potential",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "timestamp",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Timestamp",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            },
            {
              "id": "post_url",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "Post URL",
              "defaultMatch": false,
              "canBeUsedToMatch": false
            }
          ],
          "mappingMode": "autoMapInputData",
          "matchingColumns": [
            "post_id"
          ]
        },
        "options": {},
        "operation": "appendOrUpdate",
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": "gid=0",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/edit#gid=0",
          "cachedResultName": "Sentiment Data"
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": "1XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",
          "cachedResultUrl": "https://docs.google.com/spreadsheets/d/1XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX/edit#gid=0",
          "cachedResultName": "Social Media Sentiment Dashboard"
        },
        "authentication": "serviceAccount"
      },
      "credentials": {
        "googleSheetsOAuth2Api": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 4.4
    },
    {
      "id": "crisis-alert-filter",
      "name": "Crisis & Priority Alert Filter",
      "type": "n8n-nodes-base.if",
      "position": [
        1400,
        450
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "conditions": [
            {
              "id": "crisis-alert",
              "operator": {
                "type": "boolean",
                "operation": "true"
              },
              "leftValue": "={{ $json.crisis_potential }}",
              "rightValue": true
            },
            {
              "id": "critical-priority",
              "operator": {
                "type": "string",
                "operation": "equals"
              },
              "leftValue": "={{ $json.priority_level }}",
              "rightValue": "Critical"
            },
            {
              "id": "negative-brand-mention",
              "operator": {
                "type": "boolean",
                "operation": "true"
              },
              "leftValue": "={{ $json.mentions_your_brand && ($json.sentiment === 'negative' || $json.sentiment === 'very_negative') }}",
              "rightValue": true
            }
          ],
          "combineOperation": "any"
        }
      },
      "typeVersion": 2
    },
    {
      "id": "slack-crisis-alert",
      "name": "Slack Crisis & Priority Alert",
      "type": "n8n-nodes-base.slack",
      "position": [
        1800,
        450
      ],
      "parameters": {
        "text": "\ud83d\udea8 **SOCIAL MEDIA ALERT** \ud83d\udea8\n\n{% if $json.crisis_potential %}\ud83d\udd34 **CRISIS POTENTIAL DETECTED** \ud83d\udd34{% else %}\u26a0\ufe0f **HIGH PRIORITY MENTION** \u26a0\ufe0f{% endif %}\n\n**Platform**: {{ $json.platform }}\n**Author**: {{ $json.author }}{% if $json.author_followers %} ({{ $json.author_followers }} followers){% endif %}\n**Influencer Tier**: {{ $json.influencer_tier | replace('_', ' ') | title }}\n\n**Content Preview**:\n_{{ $json.content | truncate(200) }}_\n\n**Sentiment Analysis**:\n{% if $json.sentiment == 'very_negative' %}\ud83d\udd34 VERY NEGATIVE{% elif $json.sentiment == 'negative' %}\ud83d\udfe0 NEGATIVE{% elif $json.sentiment == 'positive' %}\ud83d\udfe2 POSITIVE{% elif $json.sentiment == 'very_positive' %}\ud83d\udc9a VERY POSITIVE{% else %}\u26aa NEUTRAL{% endif %} ({{ $json.sentiment_confidence }}% confidence)\n\n**Engagement Metrics**:\n\ud83d\udc4d {{ $json.likes }} likes | \ud83d\udd04 {{ $json.shares }} shares | \ud83d\udcac {{ $json.comments }} comments\n\ud83d\udcca **Engagement Rate**: {{ $json.engagement_rate }}%\n\n**Brand Intelligence**:\n{% if $json.mentions_your_brand %}\ud83c\udfaf **MENTIONS YOUR BRAND**{% endif %}\n{% if $json.competitor_mentions|length > 0 %}\ud83d\udc65 Competitor mentions: {{ $json.competitor_mentions|length }}{% endif %}\n\n**Priority Level**: {{ $json.priority_level }}\n{% if $json.requires_response %}\ud83d\udca1 **Response Required**{% endif %}\n\n\ud83d\udd17 [**View Post**]({{ $json.post_url }})\n\n*Detected at {{ $json.processed_at | date('short') }} | Session: {{ $json.monitoring_session }}*",
        "select": "channel",
        "channelId": {
          "__rl": true,
          "mode": "name",
          "value": "C1234567890"
        },
        "otherOptions": {
          "includeLinkPreviews": true
        },
        "authentication": "oAuth2"
      },
      "credentials": {
        "slackOAuth2Api": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "positive-sentiment-filter",
      "name": "Positive Sentiment Filter",
      "type": "n8n-nodes-base.if",
      "position": [
        1400,
        950
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "conditions": [
            {
              "id": "positive-sentiment",
              "operator": {
                "type": "string",
                "operation": "equals"
              },
              "leftValue": "={{ $json.sentiment }}",
              "rightValue": "positive"
            },
            {
              "id": "very-positive-sentiment",
              "operator": {
                "type": "string",
                "operation": "equals"
              },
              "leftValue": "={{ $json.sentiment }}",
              "rightValue": "very_positive"
            }
          ],
          "combineOperation": "any"
        }
      },
      "typeVersion": 2
    },
    {
      "id": "slack-positive-alert",
      "name": "Slack Positive Mention Alert",
      "type": "n8n-nodes-base.slack",
      "position": [
        1800,
        950
      ],
      "parameters": {
        "text": "\ud83c\udf1f **POSITIVE BRAND MENTION** \ud83c\udf1f\n\n**{{ $json.author }}** on **{{ $json.platform }}** said something great about us!\n\n{% if $json.sentiment == 'very_positive' %}\ud83d\udc9a **VERY POSITIVE**{% else %}\ud83d\udfe2 **POSITIVE**{% endif %} ({{ $json.sentiment_confidence }}% confidence)\n\n**Content**:\n_{{ $json.content | truncate(300) }}_\n\n**Engagement**: \ud83d\udc4d {{ $json.likes }} | \ud83d\udd04 {{ $json.shares }} | \ud83d\udcac {{ $json.comments }}\n**Influencer Tier**: {{ $json.influencer_tier | replace('_', ' ') | title }}\n\n\ud83d\udd17 [View Post]({{ $json.post_url }})\n\n\ud83d\udca1 Consider engaging with this positive mention!",
        "select": "channel",
        "channelId": {
          "__rl": true,
          "mode": "name",
          "value": "C0987654321"
        },
        "otherOptions": {
          "includeLinkPreviews": true
        },
        "authentication": "oAuth2"
      },
      "credentials": {
        "slackOAuth2Api": {
          "name": "<your credential>"
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "sticky-triggers",
      "name": "Sticky Note - Triggers",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        280,
        350
      ],
      "parameters": {
        "color": 6,
        "width": 350,
        "height": 450,
        "content": "# Step 1: Social Media Monitoring Triggers \ud83d\udcf1\n\nDual trigger system for comprehensive social media sentiment monitoring:\n\n## Automated Schedule Trigger\n- **Frequency**: Every 4 hours for real-time monitoring\n- **Purpose**: Continuous brand sentiment tracking\n- **Coverage**: Ensures no important mentions are missed\n\n## Manual Webhook Trigger  \n- **Purpose**: On-demand sentiment analysis\n- **Usage**: Crisis management or immediate checks\n- **Endpoint**: `/sentiment-webhook`\n\n## Benefits\n- Real-time brand monitoring\n- Crisis detection and prevention\n- Competitive intelligence gathering\n- Customer satisfaction tracking"
      },
      "typeVersion": 1
    },
    {
      "id": "sticky-scraping",
      "name": "Sticky Note - Social Scraping",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        680,
        200
      ],
      "parameters": {
        "color": 6,
        "width": 350,
        "height": 450,
        "content": "# Step 2: Multi-Platform Social Scraping \ud83c\udf10\n\nParallel scraping from major social media platforms:\n\n## Covered Platforms\n- **Twitter**: Real-time mentions and discussions\n- **Reddit**: Community discussions and reviews\n- **LinkedIn**: Professional opinions and B2B mentions\n\n## AI-Powered Extraction\n- Smart content parsing with ScrapeGraphAI\n- Structured data extraction\n- Context-aware content analysis\n- Platform-specific optimization\n\n## Extensible Design\n- Easy to add Instagram, Facebook, TikTok\n- Custom keyword tracking\n- Hashtag and mention monitoring\n- Multi-language support"
      },
      "typeVersion": 1
    },
    {
      "id": "sticky-analysis",
      "name": "Sticky Note - Sentiment Analysis",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1280,
        200
      ],
      "parameters": {
        "color": 6,
        "width": 350,
        "height": 450,
        "content": "# Step 3: Advanced Sentiment Analysis \ud83e\udde0\n\nSophisticated AI-powered sentiment and brand intelligence:\n\n## Sentiment Detection\n- **Multi-level Analysis**: Very positive to very negative\n- **Confidence Scoring**: Reliability percentage\n- **Emotional Context**: Excitement, disappointment, sarcasm\n- **Cultural Nuance**: Platform-specific language patterns\n\n## Brand Intelligence\n- **Your Brand Tracking**: Direct mentions and variations\n- **Competitor Monitoring**: Comparative brand analysis\n- **Context Extraction**: Relevant conversation snippets\n- **Crisis Detection**: Early warning system\n\n## Influencer Classification\n- **Tier Analysis**: Mega, macro, micro, nano influencers\n- **Engagement Scoring**: Reach and interaction rates\n- **Authority Assessment**: Industry expertise evaluation"
      },
      "typeVersion": 1
    },
    {
      "id": "sticky-dashboard",
      "name": "Sticky Note - Dashboard & Alerts",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1680,
        200
      ],
      "parameters": {
        "color": 6,
        "width": 350,
        "height": 450,
        "content": "# Step 4: Smart Alerts & Dashboard \ud83d\udcca\n\nIntelligent notification system and data visualization:\n\n## Crisis Management\n- **Priority Filtering**: Critical, High, Medium, Low\n- **Crisis Potential**: Early warning for PR issues\n- **Response Triggers**: Automated escalation rules\n- **Real-time Notifications**: Immediate Slack alerts\n\n## Google Sheets Dashboard\n- **Live Data**: Real-time sentiment tracking\n- **Historical Analysis**: Trend identification\n- **Visual Reports**: Charts and pivot tables\n- **Export Ready**: Stakeholder reporting\n\n## Dual Alert Channels\n- **Crisis Channel**: Urgent negative mentions\n- **Positive Channel**: Celebration and engagement opportunities\n- **Rich Formatting**: Actionable information display"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "social-sentiment-v1.0",
  "connections": {
    "Positive Sentiment Filter": {
      "main": [
        [
          {
            "node": "Slack Positive Mention Alert",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Social Media Monitor Trigger": {
      "main": [
        [
          {
            "node": "Twitter Brand Mentions Scraper",
            "type": "main",
            "index": 0
          },
          {
            "node": "Reddit Brand Discussion Scraper",
            "type": "main",
            "index": 0
          },
          {
            "node": "LinkedIn Professional Mentions Scraper",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Crisis & Priority Alert Filter": {
      "main": [
        [
          {
            "node": "Slack Crisis & Priority Alert",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Manual Sentiment Check Webhook": {
      "main": [
        [
          {
            "node": "Twitter Brand Mentions Scraper",
            "type": "main",
            "index": 0
          },
          {
            "node": "Reddit Brand Discussion Scraper",
            "type": "main",
            "index": 0
          },
          {
            "node": "LinkedIn Professional Mentions Scraper",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Twitter Brand Mentions Scraper": {
      "main": [
        [
          {
            "node": "Advanced Sentiment Analysis & Brand Intelligence",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Reddit Brand Discussion Scraper": {
      "main": [
        [
          {
            "node": "Advanced Sentiment Analysis & Brand Intelligence",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "LinkedIn Professional Mentions Scraper": {
      "main": [
        [
          {
            "node": "Advanced Sentiment Analysis & Brand Intelligence",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Advanced Sentiment Analysis & Brand Intelligence": {
      "main": [
        [
          {
            "node": "Google Sheets Sentiment Dashboard",
            "type": "main",
            "index": 0
          },
          {
            "node": "Crisis & Priority Alert Filter",
            "type": "main",
            "index": 0
          },
          {
            "node": "Positive Sentiment Filter",
            "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.

• googleSheetsOAuth2Api
• scrapegraphAIApi
• slackOAuth2Api

About this workflow

Social media managers and community managers Marketing teams monitoring brand reputation PR professionals tracking public sentiment Customer service teams identifying trending issues Business analysts measuring social media ROI Brand managers protecting brand reputation Product…

Source: https://n8n.io/workflows/6430/ — original creator credit. Request a take-down →

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