Extractor de sentimiento y palabras clave de comentarios de YouTube

• •Bot Token de Telegram
• •Pueden requerirse credenciales de autenticación para la API de destino
• •Credenciales de API de Google Sheets

{
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  "meta": {
    "instanceId": "0d045f8fe3802ff2be0bb9a9ea445ee6c9ed61973377effe00767e483681e2f4",
    "templateCredsSetupCompleted": true
  },
  "name": "YouTube Comment Sentiment & Keyword Extractor",
  "tags": [],
  "nodes": [
    {
      "id": "55d27fc7-ac2c-4ccc-a98f-f8c4ab7547c4",
      "name": "Disparador Programado",
      "type": "n8n-nodes-base.scheduleTrigger",
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      "parameters": {
        "rule": {
          "interval": [
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          ]
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      },
      "typeVersion": 1.2
    },
    {
      "id": "bd198bd2-705f-4fc9-ba30-c1facfa06359",
      "name": "Obtener fila(s) en hoja",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
        220,
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      ],
      "parameters": {
        "options": {},
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": ""
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": ""
        }
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      "typeVersion": 4.6
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    },
    {
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      "name": "HTTP Request",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
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      ],
      "parameters": {
        "url": "=https://www.googleapis.com/youtube/v3/commentThreads?part=snippet&videoId={{ $('Get row(s) in sheet').item.json.video_id }}&maxResults=100&key={{ $json['Youtube API Key'] }}",
        "options": {}
      },
      "typeVersion": 4.2
    },
    {
      "id": "93821e4a-b8d9-4364-aa9c-2e9200c49691",
      "name": "Youtube API Key",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "77fd67c8-e3a8-44a9-a347-5d10797ab01b",
              "name": "Youtube API Key",
              "type": "string",
              "value": "YOUR_YOUTUBE_API_KEY"
            }
          ]
        }
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    },
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      "type": "n8n-nodes-base.code",
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      "parameters": {
        "jsCode": "// Input: HTTP Request result (object with .items)\n// Output: one item per comment, flat data ready to write\nconst out = [];\nconst root = items[0].json || {};\nfor (const it of (root.items || [])) {\n  const s = it.snippet || {};\n  const tl = (s.topLevelComment || {}).snippet || {};\n  out.push({\n    json: {\n      comment_id: it.id,\n      video_id: s.videoId,\n      author: tl.authorDisplayName,\n      comment_text: tl.textOriginal,\n      like_count: tl.likeCount || 0,\n      published_at: tl.publishedAt,\n      author_channel_id: (tl.authorChannelId || {}).value,\n      author_channel_url: tl.authorChannelUrl\n    }\n  });\n}\nreturn out;\n"
      },
      "typeVersion": 2
    },
    {
      "id": "bfd6ff22-ae88-4d21-ac46-9a92c518cd87",
      "name": "Cadena Básica de LLM",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
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      ],
      "parameters": {
        "text": "=Data to analyze:\nyoutube_comment_text: \"{{ $json.comment_text }}\"\nlike_count: {{ $json.like_count }}\n\nConsider like_count only as a weak signal (slightly increase |score| if like_count is high and sentiment is clear).\nReturn the JSON only.\n",
        "batching": {},
        "messages": {
          "messageValues": [
            {
              "message": "=You are a concise social-listening analyst. \nReturn ONLY valid JSON (no markdown, no backticks).\nRules:\n- sentiment ∈ [\"positive\",\"neutral\",\"negative\"] based on overall tone.\n- score is a float in [-1, 1]; map highly positive ≈ 0.7..1, neutral ≈ -0.2..0.2, highly negative ≈ -1..-0.7.\n- keywords is an array of 3–8 short keywords (lowercase, ascii, no emojis/hashtags/stopwords, no duplicates).\n- language is a 2-letter ISO 639-1 code (e.g., \"id\", \"en\").\n- Remove emojis, excessive punctuation, URLs from consideration (but keep meaning).\n- If text is spam/promotional or meaningless, set sentiment=\"neutral\" and score≈0 and still extract topic keywords.\n- Output must be STRICT JSON with exactly these keys: sentiment, score, keywords, language.\n"
            }
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        },
        "promptType": "define",
        "hasOutputParser": true
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      "executeOnce": false,
      "typeVersion": 1.7
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    {
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      "name": "Analizador de Salida Estructurada",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
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      "parameters": {
        "jsonSchemaExample": "{\n  \"sentiment\": \"neutral\",\n  \"score\": 0.05,\n  \"keywords\": [\"jogja\", \"nyimak\", \"harapan\", \"promo\"],\n  \"language\": \"id\"\n}"
      },
      "typeVersion": 1.3
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      "name": "Modelo de Chat OpenRouter",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenRouter",
      "position": [
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      "parameters": {
        "model": "google/gemini-2.0-flash-exp:free",
        "options": {}
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      "parameters": {
        "jsCode": "// n8n Code node: Normalize LLM JSON -> {sentiment, score, keywords, language}\nconst out = [];\n\nfor (const item of items) {\n  // 1) Get candidate payload from several possible formats\n  let payload = item.json?.output ?? item.json?.data ?? item.json;\n\n  // 2) If string, try to parse JSON\n  if (typeof payload === 'string') {\n    try { payload = JSON.parse(payload); } catch { payload = {}; }\n  }\n\n  // 3) If there's still payload.output as string JSON, parse it too\n  if (payload && typeof payload.output === 'string') {\n    try { payload = JSON.parse(payload.output); } catch {}\n  }\n\n  // 4) Normalize fields\n  const allowed = new Set(['positive','neutral','negative']);\n  let sentiment = String(payload?.sentiment ?? '').toLowerCase();\n  if (!allowed.has(sentiment)) sentiment = 'neutral';\n\n  let score = Number(payload?.score);\n  if (!Number.isFinite(score)) score = 0;\n  if (score > 1) score = 1;\n  if (score < -1) score = -1;\n\n  let keywords = Array.isArray(payload?.keywords) ? payload.keywords : [];\n  keywords = keywords\n    .map(k => String(k).toLowerCase().trim())\n    .filter(k => k && k.length <= 32)\n    .slice(0, 8);\n\n  let language = String(payload?.language ?? '').slice(0, 2).toLowerCase();\n  if (!language) language = 'en';\n\n  out.push({ json: { sentiment, score, keywords, language } });\n}\n\nreturn out;\n"
      },
      "typeVersion": 2
    },
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      "name": "Añadir o actualizar fila en hoja",
      "type": "n8n-nodes-base.googleSheets",
      "position": [
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      ],
      "parameters": {
        "columns": {
          "value": {
            "author": "={{ $('Code').item.json.author }}",
            "keywords": "={{ $json.keywords }}",
            "video_id": "={{ $('Code').item.json.video_id }}",
            "sentiment": "={{ $json.sentiment }}",
            "comment_id": "={{ $('Code').item.json.comment_id }}",
            "like_count": "={{ $('Code').item.json.like_count }}",
            "comment_text": "={{ $('Code').item.json.comment_text }}",
            "published_at": "={{ $('Code').item.json.published_at }}",
            "sentiment_score": "={{ $json.score }}",
            "author_channel_id": "={{ $('Code').item.json.author_channel_id }}",
            "author_channel_url": "={{ $('Code').item.json.author_channel_url }}"
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            {
              "id": "video_id",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "video_id",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "comment_id",
              "type": "string",
              "display": true,
              "removed": false,
              "required": false,
              "displayName": "comment_id",
              "defaultMatch": false,
              "canBeUsedToMatch": true
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              "display": true,
              "required": false,
              "displayName": "author",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
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              "display": true,
              "required": false,
              "displayName": "comment_text",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "sentiment",
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              "display": true,
              "required": false,
              "displayName": "sentiment",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "sentiment_score",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "sentiment_score",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "keywords",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "keywords",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "like_count",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "like_count",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "published_at",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "published_at",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "author_channel_id",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "author_channel_id",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            },
            {
              "id": "author_channel_url",
              "type": "string",
              "display": true,
              "required": false,
              "displayName": "author_channel_url",
              "defaultMatch": false,
              "canBeUsedToMatch": true
            }
          ],
          "mappingMode": "defineBelow",
          "matchingColumns": [
            "comment_id"
          ],
          "attemptToConvertTypes": false,
          "convertFieldsToString": false
        },
        "options": {},
        "operation": "appendOrUpdate",
        "sheetName": {
          "__rl": true,
          "mode": "list",
          "value": ""
        },
        "documentId": {
          "__rl": true,
          "mode": "list",
          "value": ""
        }
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      "typeVersion": 4.6
    },
    {
      "id": "ddfbf71a-2c92-415d-b3aa-fe087c9c9835",
      "name": "Agregar",
      "type": "n8n-nodes-base.aggregate",
      "position": [
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      ],
      "parameters": {
        "options": {},
        "fieldsToAggregate": {
          "fieldToAggregate": [
            {
              "fieldToAggregate": "sentiment"
            },
            {
              "fieldToAggregate": "sentiment_score"
            },
            {
              "fieldToAggregate": "keywords"
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "655ebe44-a4df-4184-87b9-ac18c5734c62",
      "name": "Código2",
      "type": "n8n-nodes-base.code",
      "position": [
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      ],
      "parameters": {
        "jsCode": "// n8n Code node: Summarize sentiments & top keywords\nconst isAggregatedArrays =\n  items.length === 1 &&\n  items[0].json &&\n  (Array.isArray(items[0].json.sentiment) || Array.isArray(items[0].json.keywords));\n\nfunction summarizeFromArrays(j) {\n  const sentiments = Array.isArray(j.sentiment) ? j.sentiment : [];\n  const scores = Array.isArray(j.sentiment_score) ? j.sentiment_score : [];\n  let keywordsNested = Array.isArray(j.keywords) ? j.keywords : [];\n\n  // ensure keywordsNested = array of arrays\n  keywordsNested = keywordsNested.map(k =>\n    Array.isArray(k) ? k : (typeof k === 'string' ? k.split(',') : [])\n  );\n\n  const total = Math.max(sentiments.length, keywordsNested.length, scores.length, 0);\n  const cnt = { positive: 0, neutral: 0, negative: 0 };\n  const kwFreq = {};\n\n  for (let i = 0; i < total; i++) {\n    const s = String((sentiments[i] ?? 'neutral')).toLowerCase();\n    const normS = (s === 'positive' || s === 'negative' || s === 'neutral') ? s : 'neutral';\n    cnt[normS]++;\n\n    const kws = Array.isArray(keywordsNested[i]) ? keywordsNested[i] : [];\n    for (let k of kws) {\n      const key = String(k).toLowerCase().trim();\n      if (!key) continue;\n      kwFreq[key] = (kwFreq[key] || 0) + 1;\n    }\n  }\n\n  const pct = v => total ? Math.round((v / total) * 100) : 0;\n  const top_keywords = Object.entries(kwFreq)\n    .sort((a, b) => (b[1] - a[1]) || a[0].localeCompare(b[0]))\n    .slice(0, 10)\n    .map(([k, v]) => `${k} (${v})`);\n\n  return {\n    total_comments: total,\n    positive: cnt.positive,\n    neutral: cnt.neutral,\n    negative: cnt.negative,\n    positive_pct: pct(cnt.positive),\n    neutral_pct: pct(cnt.neutral),\n    negative_pct: pct(cnt.negative),\n    top_keywords\n  };\n}\n\nfunction summarizeFromItems(items) {\n  const cnt = { positive: 0, neutral: 0, negative: 0 };\n  const kwFreq = {};\n  let total = 0;\n\n  for (const it of items) {\n    const s = String(it.json?.sentiment ?? 'neutral').toLowerCase();\n    const normS = (s === 'positive' || s === 'negative' || s === 'neutral') ? s : 'neutral';\n    cnt[normS]++;\n    total++;\n\n    const kws = Array.isArray(it.json?.keywords) ? it.json.keywords : [];\n    for (let k of kws) {\n      const key = String(k).toLowerCase().trim();\n      if (!key) continue;\n      kwFreq[key] = (kwFreq[key] || 0) + 1;\n    }\n  }\n\n  const pct = v => total ? Math.round((v / total) * 100) : 0;\n  const top_keywords = Object.entries(kwFreq)\n    .sort((a, b) => (b[1] - a[1]) || a[0].localeCompare(b[0]))\n    .slice(0, 10)\n    .map(([k, v]) => `${k} (${v})`);\n\n  return {\n    total_comments: total,\n    positive: cnt.positive,\n    neutral: cnt.neutral,\n    negative: cnt.negative,\n    positive_pct: pct(cnt.positive),\n    neutral_pct: pct(cnt.neutral),\n    negative_pct: pct(cnt.negative),\n    top_keywords\n  };\n}\n\nconst summary = isAggregatedArrays\n  ? summarizeFromArrays(items[0].json)\n  : summarizeFromItems(items);\n\nreturn [{ json: summary }];\n"
      },
      "typeVersion": 2
    },
    {
      "id": "c1f271f1-bc1b-4447-9902-aacbfd0e9750",
      "name": "Enviar mensaje de texto",
      "type": "n8n-nodes-base.telegram",
      "position": [
        2660,
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      ],
      "parameters": {
        "text": "==📊 YouTube Comments Report\nVideo : {{ $('Get row(s) in sheet').item.json.video_url }}\nTotal: {{ $json.total_comments }}\n👍 Positive: {{$json.positive}} ({{ $json.positive_pct }}%)\n😐 Neutral:  {{$json.neutral}} ({{ $json.neutral_pct }}%)\n👎 Negative: {{$json.negative}} ({{ $json.negative_pct }}%)\n\n🔎 Top Keywords:\n{{ $json.top_keywords.join(\", \") }}\n\n_Ran at {{new Date().toLocaleString()}}_\n",
        "chatId": "YOUR_TELEGRAM_CHAT_ID",
        "additionalFields": {
          "appendAttribution": false
        }
      },
      "typeVersion": 1.2
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      "typeVersion": 1
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    {
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      "name": "Nota Adhesiva1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -660,
        -400
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      "parameters": {
        "width": 580,
        "height": 1040,
        "content": "# YouTube Comment Sentiment & Keyword Extractor\n\nThis workflow automatically retrieves comments from YouTube videos listed in Google Sheets, analyzes sentiment and keywords using an LLM, stores the results, then generates an aggregate summary and sends a report to Telegram.\n\n\n## ✨ Key Features\n- **Automated Scheduling** – Runs the process at regular intervals.\n- **Google Sheets Integration** – Fetches a list of videos and stores analysis results directly in Google Sheets.\n- **YouTube Comment Retrieval** – Retrieves top-level comments (up to 100 per video; can be extended with pagination).\n- **Comment Parsing** – Flattens the API response into one item per comment.\n- **AI Sentiment & Keyword Analysis** – Powered by an LLM model to extract:\n  - Sentiment (`positive`, `neutral`, `negative`)\n  - Sentiment score (-1 to 1)\n  - Keywords (cleaned from stopwords, emojis, hashtags)\n  - Language (ISO 639-1, 2-letter code)\n- **Data Normalization** – Ensures LLM output is consistent and safe to write to the sheet.\n- **Telegram Reporting** – Sends a concise report for each video directly to a Telegram chat.\n\n---\n\n## 🔐 Required Credentials\n- **Google Sheets OAuth2 API** – Read & write access to the spreadsheet.\n- **YouTube Data API Key** – To fetch comments via `commentThreads`.\n- **OpenRouter API Key** – To access the LLM model.\n- **Telegram Bot API** – To send reports via a Telegram bot.\n\n---\n\n## 🎁 Benefits\n- **End-to-End Automation** – From comment retrieval to reporting, no manual steps required.\n- **No Duplication** – Each comment is uniquely tracked by `comment_id`.\n- **Data Transparency** – Raw and analyzed data stored neatly in Google Sheets.\n- **Actionable Insights** – Sentiment and keyword summaries ready for content strategy decisions.\n- **Scalable** – Can be extended for pagination, reply comment analysis, or additional metrics.\n- **Cross-Platform** – Direct Telegram integration for easy monitoring on any device.\n"
      },
      "typeVersion": 1
    },
    {
      "id": "53fbf0dd-4131-4833-abca-96a4ca171241",
      "name": "Nota Adhesiva",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -40,
        -220
      ],
      "parameters": {
        "color": 2,
        "width": 660,
        "height": 400,
        "content": "- Run the workflow on a schedule.\n- Fetch the data row that contains the video_id and video_url from the spreadsheet.\n- Add a YouTube API key field so that subsequent records (HTTP requests to the YouTube API) can access it."
      },
      "typeVersion": 1
    },
    {
      "id": "a7500b30-f211-4311-843e-0b94a1c7efa4",
      "name": "Nota Adhesiva2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        660,
        -300
      ],
      "parameters": {
        "color": 4,
        "width": 660,
        "height": 560,
        "content": "- Fetch data from the previous node and process it either one by one or in batches.\n- Call the YouTube Data API endpoint.\n- Receive the JSON results from the YouTube API and flatten the response so that each comment becomes a separate item."
      },
      "typeVersion": 1
    },
    {
      "id": "da86e6c7-637a-4a02-8755-e687d22cb8f7",
      "name": "Nota Adhesiva3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1360,
        -80
      ],
      "parameters": {
        "color": 5,
        "width": 800,
        "height": 560,
        "content": "- Use the LLM model to analyze the comment text.\n- Ensure the LLM output follows a strict JSON structure based on the example schema, with no incorrect formats or keys.\n- Normalize the LLM JSON output.\n- Save the analysis results along with the comment metadata to Google Sheets."
      },
      "typeVersion": 1
    },
    {
      "id": "83530f28-16df-418f-8763-5b6096b860e5",
      "name": "Nota Adhesiva4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2180,
        -80
      ],
      "parameters": {
        "color": 6,
        "width": 640,
        "height": 560,
        "content": "- Combine values from multiple comment analysis columns.\n- Create a summary based on the aggregated results.\n- Send the summary report to a Telegram chat."
      },
      "typeVersion": 1
    }
  ],
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