Social Listening API for Brands: Monitor Conversations at Scale

Every day, thousands of conversations happen about your brand on social media.

Social listening APIs help you capture, analyze, and act on these conversations automatically. For reliable social media data extraction, you need an API that handles the complexity for you.

Looking for the best solution? See our best social media APIs comparison.

What is Social Listening?

Social listening = monitoring social media for:

• Brand mentions
• Product discussions
• Competitor comparisons
• Industry trends
• Customer sentiment

It goes beyond just notifications—it's about understanding the conversation.

Why APIs Over Tools?

Feature	Social Listening Tools	API Approach
Cost	$500-$2,000+/month	Pay per query
Data ownership	Locked in platform	Your database
Customization	Limited	Unlimited
Integration	Predefined	Any system
Historical data	Platform limits	Store forever

Core Use Cases

1. Brand Mention Tracking

Monitor what people say about your brand:

async function trackBrandMentions(brandName, platforms) {
  const mentions = [];
  
  // Search across platforms
  for (const platform of platforms) {
    const results = await sociavault[platform].search({
      keyword: brandName,
      since: getYesterday(),
      count: 500
    });
    
    mentions.push(...results.map(post => ({
      platform,
      content: post.caption || post.text,
      author: post.author,
      engagement: post.likes + post.comments,
      sentiment: analyzeSentiment(post.caption),
      url: post.url,
      timestamp: post.created_at
    })));
  }
  
  return mentions;
}

2. Sentiment Analysis

Understand how people feel about your brand:

async function analyzeBrandSentiment(brandName) {
  const mentions = await trackBrandMentions(brandName, [
    'instagram',
    'tiktok',
    'twitter'
  ]);
  
  const sentimentScores = mentions.map(m => ({
    ...m,
    sentiment: classifySentiment(m.content)
  }));
  
  return {
    totalMentions: mentions.length,
    positive: sentimentScores.filter(m => m.sentiment > 0.3).length,
    neutral: sentimentScores.filter(m => 
      m.sentiment >= -0.3 && m.sentiment <= 0.3
    ).length,
    negative: sentimentScores.filter(m => m.sentiment < -0.3).length,
    averageSentiment: average(sentimentScores.map(m => m.sentiment)),
    topPositive: sentimentScores
      .filter(m => m.sentiment > 0.5)
      .sort((a, b) => b.engagement - a.engagement)
      .slice(0, 5),
    topNegative: sentimentScores
      .filter(m => m.sentiment < -0.3)
      .sort((a, b) => b.engagement - a.engagement)
      .slice(0, 5)
  };
}

3. Crisis Detection

Get alerts when negative sentiment spikes:

async function monitorCrisis(brandName) {
  // Get last 24 hours
  const recent = await getBrandMentions(brandName, { hours: 24 });
  
  // Get previous 24 hours for comparison
  const previous = await getBrandMentions(brandName, { 
    hours: 24, 
    offset: 24 
  });
  
  const recentNegative = recent.filter(m => m.sentiment < -0.3);
  const previousNegative = previous.filter(m => m.sentiment < -0.3);
  
  const spike = recentNegative.length / (previousNegative.length || 1);
  
  if (spike > 2) {
    // Alert: Negative mentions doubled
    return {
      alert: true,
      severity: spike > 5 ? &apos;critical&apos; : &apos;warning&apos;,
      negativeMentions: recentNegative,
      topIssues: identifyCommonThemes(recentNegative),
      suggestedResponse: generateResponseSuggestion(recentNegative)
    };
  }
  
  return { alert: false };
}

4. Competitor Monitoring

Track what people say about competitors:

async function competitorListening(competitors) {
  const insights = {};
  
  for (const competitor of competitors) {
    const mentions = await trackBrandMentions(competitor);
    
    insights[competitor] = {
      mentionVolume: mentions.length,
      sentiment: calculateAverageSentiment(mentions),
      topPraises: extractPraises(mentions),
      topComplaints: extractComplaints(mentions),
      comparedToUs: mentions.filter(m => 
        m.content.toLowerCase().includes('vs') ||
        m.content.toLowerCase().includes('better than') ||
        m.content.toLowerCase().includes('switched from')
      )
    };
  }
  
  return insights;
}

5. Product Feedback Mining

Extract feature requests and complaints:

async function extractProductFeedback(productName) {
  const mentions = await trackBrandMentions(productName);
  
  // Categorize feedback
  const categorized = mentions.map(mention => ({
    ...mention,
    type: categorizeFeedback(mention.content),
    feature: extractFeatureMention(mention.content)
  }));
  
  return {
    featureRequests: categorized
      .filter(m => m.type === 'feature_request')
      .reduce((acc, m) => {
        acc[m.feature] = (acc[m.feature] || 0) + 1;
        return acc;
      }, {}),
    bugs: categorized.filter(m => m.type === 'bug_report'),
    praise: categorized.filter(m => m.type === 'praise'),
    complaints: categorized.filter(m => m.type === 'complaint')
  };
}

Building a Listening Dashboard

Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  SociaVault API │────▶│  Data Pipeline  │────▶│    Database     │
└─────────────────┘     └─────────────────┘     └─────────────────┘
                                                        │
                                                        ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│    Alerts       │◀────│  Analysis Jobs  │◀────│   Dashboard     │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Data Collection Schedule

// cron.js - Run every 15 minutes
const cron = require('node-cron');

cron.schedule('*/15 * * * *', async () => {
  const brands = await db.getBrandsToMonitor();
  
  for (const brand of brands) {
    const mentions = await trackBrandMentions(brand.name);
    
    // Store new mentions
    await db.insertMentions(brand.id, mentions);
    
    // Check for alerts
    const alert = await checkForAlerts(brand.id, mentions);
    if (alert) {
      await sendAlert(brand.alertEmail, alert);
    }
  }
});

Database Schema

-- Brands to monitor
CREATE TABLE brands (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255),
  keywords TEXT[], -- Additional keywords to track
  competitors TEXT[],
  alert_email VARCHAR(255)
);

-- Mentions
CREATE TABLE mentions (
  id SERIAL PRIMARY KEY,
  brand_id INTEGER REFERENCES brands(id),
  platform VARCHAR(50),
  author_username VARCHAR(255),
  content TEXT,
  sentiment DECIMAL,
  engagement INTEGER,
  url TEXT,
  posted_at TIMESTAMP,
  collected_at TIMESTAMP DEFAULT NOW()
);

-- Daily aggregates
CREATE TABLE daily_metrics (
  id SERIAL PRIMARY KEY,
  brand_id INTEGER REFERENCES brands(id),
  date DATE,
  mention_count INTEGER,
  avg_sentiment DECIMAL,
  positive_count INTEGER,
  negative_count INTEGER,
  top_mentions JSONB
);

Sentiment Analysis Options

Option 1: Simple Keyword-Based

function simpleSentiment(text) {
  const positive = ['love', 'great', 'amazing', 'best', 'awesome'];
  const negative = ['hate', 'bad', 'worst', 'terrible', 'awful'];
  
  const lower = text.toLowerCase();
  const posScore = positive.filter(w => lower.includes(w)).length;
  const negScore = negative.filter(w => lower.includes(w)).length;
  
  return (posScore - negScore) / Math.max(posScore + negScore, 1);
}

Option 2: AI-Powered (Recommended)

async function aiSentiment(text) {
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [{
      role: 'system',
      content: 'Analyze sentiment. Return JSON: {"score": -1 to 1, "label": "positive/negative/neutral", "reason": "brief explanation"}'
    }, {
      role: 'user',
      content: text
    }],
    response_format: { type: 'json_object' }
  });
  
  return JSON.parse(response.choices[0].message.content);
}

Alert System

Alert Types

1. Volume spike - Unusual number of mentions
2. Sentiment drop - Negative sentiment increasing
3. Viral mention - High-engagement post about brand
4. Competitor comparison - Being compared to competitors

Implementation

async function checkAlerts(brandId, newMentions) {
  const alerts = [];
  const baseline = await getBaseline(brandId);
  
  // Volume spike
  if (newMentions.length > baseline.avgMentions * 2) {
    alerts.push({
      type: 'volume_spike',
      severity: 'medium',
      message: `Mentions are ${Math.round(newMentions.length / baseline.avgMentions)}x higher than usual`
    });
  }
  
  // Sentiment drop
  const avgSentiment = average(newMentions.map(m => m.sentiment));
  if (avgSentiment < baseline.avgSentiment - 0.3) {
    alerts.push({
      type: &apos;sentiment_drop&apos;,
      severity: &apos;high&apos;,
      message: `Sentiment dropped to ${avgSentiment.toFixed(2)} (baseline: ${baseline.avgSentiment.toFixed(2)})`
    });
  }
  
  // Viral mention
  const viralMentions = newMentions.filter(m => m.engagement > 10000);
  for (const mention of viralMentions) {
    alerts.push({
      type: &apos;viral_mention&apos;,
      severity: mention.sentiment < 0 ? &apos;high&apos; : &apos;low&apos;,
      message: `Viral post (${mention.engagement} engagements): ${mention.url}`,
      mention
    });
  }
  
  return alerts;
}

Cost Estimation

Per-Brand Monthly Usage

Activity	Frequency	Credits
Brand monitoring	96/day (every 15 min)	~3,000
Competitor tracking (3)	12/day	~1,000
Trend analysis	Daily	~500
Total/brand		~4,500

Plan Recommendations

• 1-2 brands: Growth plan ($79 for 20,000 credits)
• 5-10 brands: Pro plan ($199 for 50,000 credits)
• Enterprise: Custom pricing with volume discounts

Integration Examples

Slack Alerts

async function sendSlackAlert(alert) {
  await fetch(process.env.SLACK_WEBHOOK_URL, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
      text: `🚨 ${alert.type}: ${alert.message}`,
      blocks: [{
        type: 'section',
        text: {
          type: 'mrkdwn',
          text: `*${alert.type.toUpperCase()}*\n${alert.message}`
        }
      }]
    })
  });
}

CRM Integration

async function createCRMTicket(negativeMention) {
  await hubspot.createTicket({
    subject: `Social Media Complaint: ${negativeMention.author}`,
    description: negativeMention.content,
    source: negativeMention.platform,
    priority: negativeMention.engagement > 1000 ? 'high' : 'medium'
  });
}

Getting Started

1. Sign up for SociaVault - 50 free credits
2. Define your keywords - Brand name, products, common misspellings
3. Set up data collection - Start with hourly, increase frequency as needed
4. Configure alerts - Start with viral mentions and sentiment drops
5. Build dashboard - Or use existing tools with our API

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Need help setting up social listening? Our team has helped Fortune 500 brands implement listening at scale. Contact us for a consultation.