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Social Media Sentiment Analyzer

🎯 Overview

The Social Media Sentiment Analyzer is an advanced AI-powered actor that analyzes sentiment, emotions, and engagement metrics from social media posts across multiple platforms including Instagram, Twitter/X, TikTok, LinkedIn, and Facebook.

Perfect for brand monitoring, market research, competitive intelligence, and campaign analysis.

✨ Key Features

Sentiment Analysis

• Three-tier sentiment classification: Positive, Negative, Neutral
• Sentiment scoring: Quantified sentiment values from -1 to 1
• Real-time analysis: Process posts instantly

Emotion Detection

Identifies five primary emotions:

• 😊 Joy - happiness, excitement, enthusiasm
• 😠 Anger - frustration, rage, disappointment
• 😢 Sadness - unhappiness, despair, disappointment
• 😨 Fear - anxiety, worry, concern
• 😲 Surprise - shock, astonishment, amazement

Engagement Metrics

• Engagement Rate: Calculates interaction ratio (likes, comments, shares vs views)
• Virality Index: Measures share-to-like ratio for viral potential
• Total Interactions: Aggregates all engagement types

Content Analysis

• Hashtag Extraction: Identifies and counts hashtags
• User Mentions: Detects @mentions for network analysis
• Keyword Filtering: Filter results by specific keywords or emotions
• Trend Identification: Automatically identifies trending topics

📋 Input Format

{
"posts":[
{
"id":"post_123",
"platform":"instagram",
"author":"brandname",
"content":"Amazing new product launch! #excited #innovation",
"timestamp":"2025-12-27T19:00:00Z",
"likes":1250,
"comments":89,
"shares":42,
"views":15000
}
],
"filterBySentiment":null,
"filterByEmotion":null,
"minEngagement":0,
"exportFormat":"json"
}
 ```
 ### Input Parameters
 | Parameter | Type | Required | Description |
 |-----------|------|----------|-------------|
 | `posts` | Array | Yes | Array of social media post objects |
 | `filterBySentiment` | String | No | Filter by 'positive', 'negative', or 'neutral' |
 | `filterByEmotion` | String | No | Filter by emotion type (joy, anger, sadness, fear, surprise) |
 | `minEngagement` | Number | No | Minimum engagement rate threshold (0-100) |
 | `exportFormat` | String | No | Output format: 'json' or 'csv' (default: 'json') |
 ### Post Object Fields
 | Field | Type | Required | Description |
 |-------|------|----------|-------------|
 | `id` | String | Yes | Unique post identifier |
 | `platform` | String | Yes | Social media platform (instagram, twitter, tiktok, linkedin, facebook) |
 | `author` | String | Yes | Post author/username |
 | `content` | String | Yes | Post text content |
 | `timestamp` | String | No | ISO 8601 timestamp |
 | `likes` | Number | No | Number of likes (default:0) |
 | `comments` | Number | No | Number of comments (default:0) |
 | `shares` | Number | No | Number of shares (default:0) |
 | `views` | Number | No | Number of views/impressions (default:1) |
 ## 📤 Output Format
 ```json
{
"results":[
{
"platform":"instagram",
"postId":"post_123",
"author":"brandname",
"content":"Amazing new product launch! #excited #innovation",
"timestamp":"2025-12-27T19:00:00Z",
"sentiment":"positive",
"sentimentScore":0.85,
"emotions":[
{
"emotion":"joy",
"confidence":0.95
},
{
"emotion":"surprise",
"confidence":0.6
}
],
"engagement":{
"likes":1250,
"comments":89,
"shares":42,
"views":15000,
"engagementRate":8.95,
"virality Index":3.36,
"totalInteractions":1381
},
"hashtags":["#excited","#innovation"],
"mentionedUsers":[],
"analysisTimestamp":"2025-12-27T19:05:00Z"
}
],
"summary":{
"totalPostsAnalyzed":1,
"totalPostsMatched":1,
"sentimentDistribution":{
"positive":1,
"negative":0,
"neutral":0
},
"averageSentimentScore":0.85,
"topEmotions":[
{
"emotion":"joy",
"count":1
}
],
"topHashtags":[
{
"hashtag":"#excited",
"count":1
},
{
"hashtag":"#innovation",
"count":1
}
],
"totalEngagement":1381,
"analysisTimestamp":"2025-12-27T19:05:00Z"
}
}
 ```
 ## 🚀 Quick Start
 ### Basic Usage
1. **Prepare your posts data** in the input format above
2. **Run the actor** with your posts array
3. **Receive analyzed results** with sentiment, emotions, and engagement metrics
 ### Example: Analyze Brand Campaign
 ```json
{
"posts":[
{
"id":"post_456",
"platform":"twitter",
"author":"YourBrand",
"content":"Excited to announce our new partnership with TechCorp! This is fantastic news for our users. #partnership #growth",
"timestamp":"2025-12-27T10:30:00Z",
"likes":5432,
"comments":234,
"shares":892,
"views":125000
}
],
"filterBySentiment":"positive",
"minEngagement":2
}
 ```
 ## 💡 Use Cases
 ### Brand Monitoring
 Monitor how customers feel about your brand across all platforms
 - Track sentiment trends over time
 - Identify negative sentiment spikes
 - Celebrate viral positive moments
 ### Competitive Intelligence
 Analyze competitor social media sentiment
 - Compare sentiment across brands
 - Understand audience perception shifts
 - Track campaign effectiveness
 ### Campaign Analysis
 Measure sentiment impact of marketing campaigns
 - Pre/post campaign sentiment comparison
 - Identify high-performing content themes
 - Optimize messaging based on emotional response
 ### Market Research
 Understand industry sentiment and trends
 - Track industry sentiment trends
 - Identify emerging topics and concerns
 - Monitor influencer sentiment
 ### Customer Feedback Analysis
 Analyze customer reactions to product launches
 - Detect customer pain points
 - Identify customer joy moments
 - Improve product development insights
 ## 🔧 Technical Details
 ### Technology Stack
 - **Runtime**: Node.js with Apify SDK
 - **NLP Engine**: Lexicon-based sentiment analysis
 - **Data Storage**: Apify Dataset format
 ### Performance Characteristics
 - **Processing Speed**: ~100 posts per second
 - **Memory Usage**: ~512 MB base
 - **Timeout**:60 minutes (adjustable)
 ### Sentiment Algorithm
 Uses a comprehensive lexicon of positive and negative keywords combined with contextual analysis to determine sentiment polarity and strength.
 ### Emotion Detection
 Employs keyword matching with confidence scoring to identify which of the five emotions are present in the text.
 ## 📊 Output Metrics Explained
 ### Sentiment Score
 - **Range**:-1.0 to 1.0
 - **>0.1**: Positive sentiment
 - **-0.1 to 0.1**: Neutral sentiment
 - **<-0.1**: Negative sentiment
 ### Engagement Rate
 Formula: `(likes + comments×2 + shares×3) / views × 100`
 ### Virality Index
 Formula: `(shares / (likes + 1)) × 100`
 ## 🎓 Examples
 ### Example 1: Product Launch Analysis
 Analyze reactions to a product launch across multiple platforms.
 ### Example 2: Crisis Management
 Quickly identify negative sentiment spikes and affected content.
 ### Example 3: Influencer Vetting
 Analyze influencer sentiment and audience engagement quality.
 ## 📝 Notes
 - Sentiment analysis is probabilistic and may not be 100% accurate
 - Sarcasm and irony may be misclassified
 - Multiple languages support is planned for future versions
 - Real-time API streaming coming in v2.0
 ## 🤝 Support & Feedback
 Need help? Have suggestions? Contact our support team or create an issue on GitHub.
 ## 📄 License
 This actor is provided under the Apify platform license.
 ## 🔄 Version History
 ### v0.0.1 (Current)
 - Initial release
 - Core sentiment analysis
 - Emotion detection
 - Engagement metrics
 - Multi-platform support
 ---
 **Made with ❤️ for the Apify community**