🚀 AI-Powered Social Media Sentiment Analysis Tool

A comprehensive, AI-driven platform for analyzing emotional tone and sentiment across social media platforms. This tool helps brands, organizations, and researchers understand public opinion, detect crises early, and make data-driven decisions.

🎯 Key Features

📊 Multi-Platform Analysis

• Reddit: Posts and comments from relevant subreddits
• YouTube: Video comments analysis
• Web Scraping: Custom data collection from various sources

🧠 Advanced AI Analysis

• Multi-Level Sentiment: Basic (positive/negative/neutral) + Advanced emotions
• VADER + TextBlob: Enhanced sentiment accuracy
• Gemini AI Integration: Context-aware interpretation
• Emotion Detection: Joy, anger, fear, sadness, surprise, disgust

🎯 Use Cases

🛡️ Brand Monitoring

• Track brand sentiment across platforms
• Monitor product launches and campaigns
• Competitive analysis and benchmarking

⚡ Crisis Management

• Real-time crisis detection with scoring (0-100)
• Automated alerts for negative sentiment spikes
• Early warning system for reputation management

📈 Market Research

• Customer sentiment analysis
• Product feedback analysis
• Market trend identification

📢 Campaign Tracking

• Marketing campaign effectiveness
• Influencer impact measurement
• ROI analysis through sentiment

🗳️ Public Opinion Analysis

• Political sentiment tracking
• Social issue monitoring
• Real-time opinion polls

🚀 Quick Start

1. Installation

# Clone the repository
git clone <repository-url>
cd NLP-social media-Sentiment-Analysis-main

# Run setup script
python setup.py

2. Configuration (Optional)

Edit config.py to add your API keys:

# Reddit API (Optional - fallback to web scraping)
REDDIT_CLIENT_ID = "your_client_id"
REDDIT_CLIENT_SECRET = "your_client_secret"

3. Launch Application

python app.py

Open your browser to: http://localhost:5000

📊 Dashboard Overview

Analysis Dashboard

• Query Input: Brand name, product, or any topic
• Platform Selection: Choose Reddit, YouTube
• Analysis Type: Brand monitoring, crisis management, etc.
• Sample Size: 50-500 posts for analysis

Real-Time Monitoring

• Live Sentiment Tracking: Continuous monitoring
• Crisis Alerts: Automated notifications
• Trend Analysis: Historical sentiment patterns

Visualizations

• Sentiment Distribution: Pie charts showing positive/negative/neutral
• Emotion Analysis: Bar charts for detailed emotions
• Platform Comparison: Cross-platform sentiment analysis
• Crisis Gauge: Risk assessment visualization

🔧 Technical Architecture

Backend Stack

• Flask: Web framework
• NLTK + VADER: Sentiment analysis engine
• Gemini AI: Advanced language understanding
• Selenium: Web scraping automation
• Pandas: Data processing and analysis

Frontend Stack

• Tailwind CSS: Modern responsive design
• JavaScript: Interactive dashboard
• Chart.js/Plotly: Data visualizations
• Font Awesome: Icons and UI elements

Data Pipeline

1. Data Collection: Multi-platform scraping
2. Preprocessing: Text cleaning and normalization
3. Sentiment Analysis: AI-powered classification
4. Emotion Detection: Advanced psychological analysis
5. Insights Generation: AI-powered recommendations
6. Visualization: Interactive charts and reports

📈 Analysis Capabilities

Sentiment Metrics

• Compound Score: Overall sentiment (-1 to +1)
• Positive/Negative/Neutral: Percentage distribution
• Subjectivity: Opinion vs. factual content
• Polarity: Emotional intensity

Emotion Detection

• Joy: Happiness, excitement, delight
• Anger: Frustration, irritation, rage
• Fear: Anxiety, worry, concern
• Sadness: Depression, grief, disappointment
• Surprise: Shock, amazement, wonder
• Disgust: Revulsion, distaste, rejection

Crisis Scoring

• Algorithm: Weighted combination of negative sentiment and anger/fear emotions
• Scale: 0-100 (0 = No risk, 100 = Critical crisis)
• Thresholds:
  • 0-30: Low risk (Green)
  • 31-70: Medium risk (Orange)
  • 71-100: High risk (Red)

📋 Use Case Examples

Brand Monitoring Example

Query: "iPhone 15"
Platforms: Reddit
Analysis: Product launch sentiment
Result: 73% positive, Crisis Score: 15/100
Insight: "Highly positive reception, strong market appeal"

Crisis Management Example

Query: "Company XYZ data breach"
Platforms: Reddit, YouTube
Analysis: Crisis detection
Result: 65% negative, Crisis Score: 85/100
Alert: "HIGH CRISIS RISK: Immediate action recommended"

Campaign Tracking Example

Query: "#BrandCampaign2024"
Platforms: Reddit
Analysis: Marketing campaign effectiveness
Result: 58% positive, High engagement
Insight: "Campaign resonating well with target audience"

🚀 Future Enhancements

Planned Features

• Real-time streaming: WebSocket integration
• Machine Learning: Custom sentiment models
• Geographic Analysis: Location-based sentiment
• Influencer Tracking: Key opinion leader analysis
• Historical Trends: Long-term pattern analysis
• Multi-language Support: Global sentiment analysis

---

Built with ❤️ using AI and advanced analytics for better social media understanding.

🚀 Quick Start

1. Clone the repository

git clone https://github.com/yourusername/movieai-analysis.git
cd movieai-analysis

2. Set up environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Configure API Keys

# Add to .env file
GEMINI_API_KEY=your_api_key_here

4. Run the application

python app.py

🛠️ Tech Stack

Backend

• 🐍 Python 3.8+
• 🌶️ Flask web framework
• 🧠 NLTK for NLP tasks
• 🤖 Google Gemini API
• 🌐 Selenium for web scraping
• 📊 Matplotlib for visualizations

Frontend

• 💨 TailwindCSS
• 🎨 Glass-morphism UI
• 📱 Responsive design
• 🔄 Async JavaScript
• 🎯 Dynamic charts

🔄 How It Works

1. Data Collection 🌐

   • Scrapes movie data from Wikipedia
   • Falls back to Gemini AI for missing data
   • Collects reviews and critical reception

2. Analysis Pipeline 🔬

   • Performs sentiment analysis on reviews
   • Generates sentiment scores and ratings
   • Creates visual representations

3. Report Generation 📊

   • Creates detailed PDF reports
   • Includes sentiment analysis charts
   • Adds AI-generated insights

4. User Interface 🎨

   • Modern glass-morphism design
   • Real-time analysis updates
   • Interactive visualizations

📊 Sample Analysis

The platform provides:

• 📈 Sentiment distribution charts
• 🎭 Rating comparisons
• 💡 AI-generated insights
• 📝 Comprehensive reports

🔧 Advanced Configuration

Customizing Analysis Parameters

# In app.py
SENTIMENT_THRESHOLD = 0.05
MAX_FEATURES = 1000
ANALYSIS_DEPTH = 'deep'

Styling Modifications

/* In style.css */
.glass-effect {
    backdrop-filter: blur(8px);
    background: rgba(255, 255, 255, 0.1);
}

📚 API Documentation

Analyze Endpoint

POST /analyze
{
    "movie_name": "string",
    "analysis_depth": "string",
    "include_ai_insights": boolean
}

🤝 Contributing

1. Fork the repository
2. Create your feature branch: git checkout -b feature/AmazingFeature
3. Commit your changes: git commit -m 'Add some AmazingFeature'
4. Push to the branch: git push origin feature/AmazingFeature
5. Open a pull request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• NLTK team for natural language processing tools
• Google Gemini API for AI insights
• Unsplash for beautiful images
• TailwindCSS for modern styling
• Flask for robust backend framework

---

Made with ❤️ by Your Name