房地产市场情绪分析
中级
这是一个Market Research, AI Summarization领域的自动化工作流，包含 15 个节点。主要使用 Code, Telegram, ScheduleTrigger, ScrapegraphAi 等节点。 使用ScrapeGraphAI和Telegram分析房地产市场情绪
前置要求
- •Telegram Bot Token
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "VhEwspDqzu7ssFVE",
"meta": {
"instanceId": "f4b0efaa33080e7774e0d9285c40c7abcd2c6f7cf1a8b901fa7106170dd4cda3",
"templateCredsSetupCompleted": true
},
"name": "我的工作流 2",
"tags": [
{
"id": "DxXGubfBzRKh6L8T",
"name": "Revenue Optimization",
"createdAt": "2025-07-25T16:24:30.370Z",
"updatedAt": "2025-07-25T16:24:30.370Z"
},
{
"id": "IxkcJ2IpYIxivoHV",
"name": "Content Strategy",
"createdAt": "2025-07-25T12:57:37.677Z",
"updatedAt": "2025-07-25T12:57:37.677Z"
},
{
"id": "PAKIJ2Mm9EvRcR3u",
"name": "Trend Monitoring",
"createdAt": "2025-07-25T12:57:37.670Z",
"updatedAt": "2025-07-25T12:57:37.670Z"
},
{
"id": "YtfXmaZk44MYedPO",
"name": "Dynamic Pricing",
"createdAt": "2025-07-25T16:24:30.369Z",
"updatedAt": "2025-07-25T16:24:30.369Z"
},
{
"id": "wJ30mjhtrposO8Qt",
"name": "Simple RAG",
"createdAt": "2025-07-28T12:55:14.424Z",
"updatedAt": "2025-07-28T12:55:14.424Z"
}
],
"nodes": [
{
"id": "ca1ec51f-e86e-44f4-b00f-6588ec3bf456",
"name": "计划触发器",
"type": "n8n-nodes-base.scheduleTrigger",
"position": [
1056,
272
],
"parameters": {
"rule": {
"interval": [
{
"field": "cronExpression"
}
]
}
},
"typeVersion": 1.2
},
{
"id": "f7d52e3a-2ede-4754-ab26-20e4d96f86c3",
"name": "ScrapeGraphAI - 房地产论坛",
"type": "n8n-nodes-scrapegraphai.scrapegraphAi",
"position": [
1392,
400
],
"parameters": {
"userPrompt": "Extract recent forum discussions about real estate market conditions, investment opportunities, and market sentiment. Focus on posts from the last 24 hours. Return data in this format: { \"discussions\": [{ \"title\": \"discussion title\", \"author\": \"username\", \"date\": \"date posted\", \"content\": \"main discussion content\", \"replies_count\": \"number of replies\", \"sentiment_indicators\": [\"bullish\", \"bearish\", \"neutral indicators\"], \"url\": \"discussion url\" }] }",
"websiteUrl": "https://www.biggerpockets.com/forums/real-estate-investing"
},
"typeVersion": 1
},
{
"id": "728be835-5b88-4132-9289-fcf12f31d554",
"name": "ScrapeGraphAI - 房地产新闻",
"type": "n8n-nodes-scrapegraphai.scrapegraphAi",
"position": [
1392,
640
],
"parameters": {
"userPrompt": "Extract the latest real estate investment news and market analysis from the past 24 hours. Focus on market trends, economic indicators, and expert opinions. Return data as: { \"news_articles\": [{ \"headline\": \"article title\", \"author\": \"author name\", \"date\": \"publication date\", \"summary\": \"article summary\", \"key_points\": [\"important points\"], \"market_impact\": \"positive/negative/neutral\", \"url\": \"article url\" }] }",
"websiteUrl": "https://www.realestateinvestingtrust.com/news"
},
"typeVersion": 1
},
{
"id": "6b2e44ec-8c2c-419a-a145-e367bacab041",
"name": "情感分析器",
"type": "n8n-nodes-base.code",
"position": [
1664,
432
],
"parameters": {
"jsCode": "// Combine forum and news data for sentiment analysis\nconst forumData = $input.all()[0]?.json?.result?.discussions || [];\nconst newsData = $input.all()[1]?.json?.result?.news_articles || [];\n\nfunction analyzeSentiment(text) {\n const bullishKeywords = ['buy', 'opportunity', 'growth', 'investment', 'profit', 'rising', 'bullish', 'positive', 'gain', 'uptrend'];\n const bearishKeywords = ['sell', 'crash', 'decline', 'loss', 'falling', 'bearish', 'negative', 'risk', 'downturn', 'bubble'];\n \n const textLower = text.toLowerCase();\n const bullishCount = bullishKeywords.filter(word => textLower.includes(word)).length;\n const bearishCount = bearishKeywords.filter(word => textLower.includes(word)).length;\n \n if (bullishCount > bearishCount) return 'bullish';\n if (bearishCount > bullishCount) return 'bearish';\n return 'neutral';\n}\n\nfunction calculateSentimentScore(sentiment) {\n switch(sentiment) {\n case 'bullish': return 1;\n case 'bearish': return -1;\n default: return 0;\n }\n}\n\nconst forumSentiments = forumData.map(discussion => {\n const sentiment = analyzeSentiment(discussion.title + ' ' + discussion.content);\n return {\n source: 'forum',\n title: discussion.title,\n sentiment: sentiment,\n score: calculateSentimentScore(sentiment),\n url: discussion.url,\n date: discussion.date\n };\n});\n\nconst newsSentiments = newsData.map(article => {\n const sentiment = analyzeSentiment(article.headline + ' ' + article.summary);\n return {\n source: 'news',\n title: article.headline,\n sentiment: sentiment,\n score: calculateSentimentScore(sentiment),\n url: article.url,\n date: article.date,\n market_impact: article.market_impact\n };\n});\n\nconst allSentiments = [...forumSentiments, ...newsSentiments];\nconst totalScore = allSentiments.reduce((sum, item) => sum + item.score, 0);\nconst averageScore = totalScore / allSentiments.length || 0;\n\nlet overallSentiment = 'neutral';\nif (averageScore > 0.3) overallSentiment = 'bullish';\nelse if (averageScore < -0.3) overallSentiment = 'bearish';\n\nreturn [{\n json: {\n overall_sentiment: overallSentiment,\n sentiment_score: averageScore,\n total_sources: allSentiments.length,\n bullish_count: allSentiments.filter(s => s.sentiment === 'bullish').length,\n bearish_count: allSentiments.filter(s => s.sentiment === 'bearish').length,\n neutral_count: allSentiments.filter(s => s.sentiment === 'neutral').length,\n detailed_analysis: allSentiments,\n analysis_timestamp: new Date().toISOString()\n }\n}];"
},
"typeVersion": 2
},
{
"id": "ed209cd0-b393-4bb8-9e59-5fc605ab1f77",
"name": "市场预测器",
"type": "n8n-nodes-base.code",
"position": [
1968,
416
],
"parameters": {
"jsCode": "// Market prediction based on sentiment analysis\nconst sentimentData = $input.all()[0].json;\n\nfunction predictMarketDirection(sentimentScore, bullishCount, bearishCount, totalSources) {\n let prediction = 'hold';\n let confidence = 0;\n let reasoning = [];\n \n // Strong bullish sentiment\n if (sentimentScore > 0.5 && bullishCount > bearishCount * 2) {\n prediction = 'strong_buy';\n confidence = Math.min(90, 60 + (sentimentScore * 30));\n reasoning.push('Strong bullish sentiment detected across multiple sources');\n }\n // Moderate bullish sentiment\n else if (sentimentScore > 0.2) {\n prediction = 'buy';\n confidence = Math.min(75, 50 + (sentimentScore * 25));\n reasoning.push('Moderate positive sentiment in the market');\n }\n // Strong bearish sentiment\n else if (sentimentScore < -0.5 && bearishCount > bullishCount * 2) {\n prediction = 'strong_sell';\n confidence = Math.min(90, 60 + (Math.abs(sentimentScore) * 30));\n reasoning.push('Strong bearish sentiment detected - consider exiting positions');\n }\n // Moderate bearish sentiment\n else if (sentimentScore < -0.2) {\n prediction = 'sell';\n confidence = Math.min(75, 50 + (Math.abs(sentimentScore) * 25));\n reasoning.push('Negative sentiment trend - caution advised');\n }\n // Neutral/mixed signals\n else {\n prediction = 'hold';\n confidence = 40;\n reasoning.push('Mixed signals in market sentiment - maintain current positions');\n }\n \n // Adjust confidence based on data volume\n if (totalSources < 10) {\n confidence *= 0.8;\n reasoning.push('Limited data sources - lower confidence');\n }\n \n return {\n prediction,\n confidence: Math.round(confidence),\n reasoning\n };\n}\n\nconst marketPrediction = predictMarketDirection(\n sentimentData.sentiment_score,\n sentimentData.bullish_count,\n sentimentData.bearish_count,\n sentimentData.total_sources\n);\n\n// Generate investment recommendations\nlet recommendations = [];\nswitch(marketPrediction.prediction) {\n case 'strong_buy':\n recommendations = [\n 'Consider increasing real estate investment allocation',\n 'Look for value opportunities in growth markets',\n 'Accelerate planned property acquisitions'\n ];\n break;\n case 'buy':\n recommendations = [\n 'Moderate increase in real estate exposure',\n 'Focus on cash-flowing properties',\n 'Monitor market closely for entry points'\n ];\n break;\n case 'strong_sell':\n recommendations = [\n 'Consider reducing real estate exposure',\n 'Postpone new acquisitions',\n 'Review portfolio for underperforming assets'\n ];\n break;\n case 'sell':\n recommendations = [\n 'Exercise caution with new investments',\n 'Consider taking profits on recent gains',\n 'Increase cash reserves'\n ];\n break;\n default:\n recommendations = [\n 'Maintain current investment strategy',\n 'Continue regular market monitoring',\n 'Stay prepared for market shifts'\n ];\n}\n\nreturn [{\n json: {\n ...sentimentData,\n market_prediction: marketPrediction.prediction,\n confidence_level: marketPrediction.confidence,\n reasoning: marketPrediction.reasoning,\n investment_recommendations: recommendations,\n risk_level: marketPrediction.confidence > 70 ? 'low' : marketPrediction.confidence > 50 ? 'medium' : 'high',\n prediction_timestamp: new Date().toISOString()\n }\n}];"
},
"typeVersion": 2
},
{
"id": "11367ba0-9a2c-4352-85c5-945596f2aab0",
"name": "时机优化器",
"type": "n8n-nodes-base.code",
"position": [
2272,
416
],
"parameters": {
"jsCode": "// Timing optimization based on market prediction and historical patterns\nconst predictionData = $input.all()[0].json;\n\nfunction optimizeTiming(prediction, confidence, sentimentScore) {\n let timing_recommendation = 'monitor';\n let urgency = 'low';\n let optimal_timeframe = '1-3 months';\n let timing_factors = [];\n \n // Determine optimal timing based on prediction strength\n if (prediction === 'strong_buy' && confidence > 75) {\n timing_recommendation = 'act_immediately';\n urgency = 'high';\n optimal_timeframe = '1-2 weeks';\n timing_factors.push('Strong market signals indicate immediate action needed');\n }\n else if (prediction === 'buy' && confidence > 60) {\n timing_recommendation = 'act_soon';\n urgency = 'medium';\n optimal_timeframe = '2-4 weeks';\n timing_factors.push('Positive sentiment building - act within optimal window');\n }\n else if (prediction === 'strong_sell') {\n timing_recommendation = 'exit_positions';\n urgency = 'high';\n optimal_timeframe = '1-2 weeks';\n timing_factors.push('Strong negative signals - consider immediate exit strategy');\n }\n else if (prediction === 'sell') {\n timing_recommendation = 'reduce_exposure';\n urgency = 'medium';\n optimal_timeframe = '2-6 weeks';\n timing_factors.push('Negative trend developing - gradual position reduction advised');\n }\n \n // Add seasonal considerations (simplified)\n const currentMonth = new Date().getMonth();\n const seasonalFactors = {\n spring: [2, 3, 4], // March, April, May\n summer: [5, 6, 7], // June, July, August\n fall: [8, 9, 10], // September, October, November\n winter: [11, 0, 1] // December, January, February\n };\n \n let currentSeason = 'winter';\n for (const [season, months] of Object.entries(seasonalFactors)) {\n if (months.includes(currentMonth)) {\n currentSeason = season;\n break;\n }\n }\n \n if (currentSeason === 'spring' || currentSeason === 'summer') {\n timing_factors.push('Peak real estate season - higher activity expected');\n } else {\n timing_factors.push('Off-season period - potentially better deals available');\n }\n \n return {\n timing_recommendation,\n urgency,\n optimal_timeframe,\n timing_factors,\n current_season: currentSeason\n };\n}\n\nconst timingAnalysis = optimizeTiming(\n predictionData.market_prediction,\n predictionData.confidence_level,\n predictionData.sentiment_score\n);\n\nreturn [{\n json: {\n ...predictionData,\n timing_recommendation: timingAnalysis.timing_recommendation,\n urgency_level: timingAnalysis.urgency,\n optimal_timeframe: timingAnalysis.optimal_timeframe,\n timing_factors: timingAnalysis.timing_factors,\n current_season: timingAnalysis.current_season,\n timing_analysis_timestamp: new Date().toISOString()\n }\n}];"
},
"typeVersion": 2
},
{
"id": "a430a788-98ec-469e-9724-dca80245a1e9",
"name": "投资顾问提醒",
"type": "n8n-nodes-base.code",
"position": [
2592,
416
],
"parameters": {
"jsCode": "// Generate comprehensive investment advisor alert\nconst analysisData = $input.all()[0].json;\n\nfunction generateAlert(data) {\n const {\n overall_sentiment,\n sentiment_score,\n market_prediction,\n confidence_level,\n timing_recommendation,\n urgency_level,\n optimal_timeframe,\n investment_recommendations,\n risk_level\n } = data;\n \n // Determine alert priority\n let alertPriority = 'normal';\n if (urgency_level === 'high' && confidence_level > 70) {\n alertPriority = 'critical';\n } else if (urgency_level === 'medium' || confidence_level > 60) {\n alertPriority = 'high';\n }\n \n // Generate alert title\n const alertTitle = `🏠 RE Market Alert: ${market_prediction.toUpperCase().replace('_', ' ')} Signal (${confidence_level}% confidence)`;\n \n // Generate detailed message\n let message = `📊 **REAL ESTATE MARKET ANALYSIS REPORT**\\n\\n`;\n message += `🎯 **Overall Sentiment:** ${overall_sentiment.toUpperCase()}\\n`;\n message += `📈 **Market Prediction:** ${market_prediction.replace('_', ' ').toUpperCase()}\\n`;\n message += `🎲 **Confidence Level:** ${confidence_level}%\\n`;\n message += `⏰ **Timing:** ${timing_recommendation.replace('_', ' ').toUpperCase()}\\n`;\n message += `🚨 **Urgency:** ${urgency_level.toUpperCase()}\\n`;\n message += `📅 **Optimal Timeframe:** ${optimal_timeframe}\\n`;\n message += `⚠️ **Risk Level:** ${risk_level.toUpperCase()}\\n\\n`;\n \n message += `💡 **KEY RECOMMENDATIONS:**\\n`;\n investment_recommendations.forEach((rec, index) => {\n message += `${index + 1}. ${rec}\\n`;\n });\n \n message += `\\n🔍 **ANALYSIS SUMMARY:**\\n`;\n message += `• Total sources analyzed: ${data.total_sources}\\n`;\n message += `• Bullish signals: ${data.bullish_count}\\n`;\n message += `• Bearish signals: ${data.bearish_count}\\n`;\n message += `• Neutral signals: ${data.neutral_count}\\n`;\n \n message += `\\n📋 **TIMING FACTORS:**\\n`;\n data.timing_factors.forEach((factor, index) => {\n message += `• ${factor}\\n`;\n });\n \n message += `\\n🕐 **Report Generated:** ${new Date().toLocaleString()}\\n`;\n message += `\\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━`;\n \n return {\n alert_title: alertTitle,\n alert_message: message,\n alert_priority: alertPriority,\n raw_data: data\n };\n}\n\nconst alert = generateAlert(analysisData);\n\nreturn [{\n json: {\n ...alert,\n notification_timestamp: new Date().toISOString()\n }\n}];"
},
"typeVersion": 2
},
{
"id": "d2bd5cbb-7de7-45f3-b106-2200aba40fa4",
"name": "Telegram 提醒",
"type": "n8n-nodes-base.telegram",
"position": [
2896,
400
],
"webhookId": "e0f221b9-7ee1-4113-850e-1fc0202811f1",
"parameters": {
"text": "={{ $json.alert_message }}",
"chatId": "@real_estate_alerts",
"additionalFields": {
"parse_mode": "Markdown"
}
},
"typeVersion": 1.2
},
{
"id": "15984e52-8b3a-4739-956c-e3d24197ff3f",
"name": "便签 - 触发器",
"type": "n8n-nodes-base.stickyNote",
"position": [
976,
-352
],
"parameters": {
"color": 2,
"width": 300,
"height": 1136,
"content": "# 步骤1：定时触发器 ⏱️"
},
"typeVersion": 1
},
{
"id": "de278e05-8034-4c28-a838-79933be72677",
"name": "便签 - 数据收集",
"type": "n8n-nodes-base.stickyNote",
"position": [
1280,
-352
],
"parameters": {
"color": 3,
"width": 300,
"height": 1136,
"content": "# 步骤2：数据收集 🔍"
},
"typeVersion": 1
},
{
"id": "e14ea293-64f2-4751-a573-5b9f2bed9a8a",
"name": "便签 - 情感分析",
"type": "n8n-nodes-base.stickyNote",
"position": [
1584,
-352
],
"parameters": {
"color": 4,
"width": 300,
"height": 1136,
"content": "# 步骤3：情感分析 🧠"
},
"typeVersion": 1
},
{
"id": "a1d2afa7-e8dc-4e19-8fb3-ac199f9726a9",
"name": "便签 - 市场预测",
"type": "n8n-nodes-base.stickyNote",
"position": [
1888,
-352
],
"parameters": {
"color": 5,
"width": 300,
"height": 1136,
"content": "# 步骤4：市场预测 🔮"
},
"typeVersion": 1
},
{
"id": "ce6eb84f-96ca-47a6-9b9a-8e16c1b77b13",
"name": "便签 - 时机优化",
"type": "n8n-nodes-base.stickyNote",
"position": [
2192,
-352
],
"parameters": {
"color": 6,
"width": 300,
"height": 1136,
"content": "# 步骤5：时机优化 ⏰"
},
"typeVersion": 1
},
{
"id": "77039ec5-4612-49fb-a4b4-1f4e5c21ec56",
"name": "便签 - 投资提醒",
"type": "n8n-nodes-base.stickyNote",
"position": [
2496,
-352
],
"parameters": {
"color": 7,
"width": 300,
"height": 1136,
"content": "# 步骤6：投资提醒 🚨"
},
"typeVersion": 1
},
{
"id": "05197e73-8d6b-45e2-8202-6c828a45a9c7",
"name": "便签 - Telegram 投递",
"type": "n8n-nodes-base.stickyNote",
"position": [
2800,
-352
],
"parameters": {
"width": 300,
"height": 1136,
"content": "# 步骤7：Telegram 投递 📱"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "df60cb6d-f920-46a7-843a-9d4c9bdd76d9",
"connections": {
"Market Predictor": {
"main": [
[
{
"node": "Timing Optimizer",
"type": "main",
"index": 0
}
]
]
},
"Schedule Trigger": {
"main": [
[
{
"node": "ScrapeGraphAI - RE Forums",
"type": "main",
"index": 0
},
{
"node": "ScrapeGraphAI - RE News",
"type": "main",
"index": 0
}
]
]
},
"Timing Optimizer": {
"main": [
[
{
"node": "Investment Advisor Alert",
"type": "main",
"index": 0
}
]
]
},
"Sentiment Analyzer": {
"main": [
[
{
"node": "Market Predictor",
"type": "main",
"index": 0
}
]
]
},
"ScrapeGraphAI - RE News": {
"main": [
[
{
"node": "Sentiment Analyzer",
"type": "main",
"index": 0
}
]
]
},
"Investment Advisor Alert": {
"main": [
[
{
"node": "Telegram Alert",
"type": "main",
"index": 0
}
]
]
},
"ScrapeGraphAI - RE Forums": {
"main": [
[
{
"node": "Sentiment Analyzer",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
中级 - 市场调研, AI 摘要总结
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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