brand_reputation_monitor

Brand Reputation Monitor

A powerful AI-powered brand reputation monitoring tool that analyzes news coverage, sentiment, and brand insights to help businesses track their reputation in real-time. This application uses Nebius AI for intelligent analysis, Memori for persistent context, Bright Data for real-time web scraping, and Agno agents for comprehensive brand intelligence.

Features ✨

📰 News Analysis: Real-time monitoring of news articles and press coverage about your brand
💭 Sentiment Analysis: AI-powered sentiment tracking (positive, negative, neutral)
🔍 Brand Insights: Actionable insights extracted from news and public perception
🤖 Conversational AI: Natural chat interface with Nebius AI-powered follow-up questions
💾 Memory Integration: Stores conversation context using Memori with SQLite for persistent learning
🌐 Real-Time Web Scraping: Uses Bright Data to extract current news and brand data
🎯 Keyword-Based Monitoring: Custom search queries for targeted brand tracking
📱 Interactive Chat: Natural conversation flow with contextual responses
🔄 Context-Aware Responses: Searches memory before answering for consistent insights
⚙️ Easy Configuration: Simple setup with API keys via intuitive sidebar
🔒 Evidence-Based Analysis: Only includes real URLs and sources from actual web research

Prerequisites 🛠️

• Python 3.10+
• Nebius Token Factory API key (Get it here)
• Bright Data API credentials
• SQLite (included with Python)

Installation 📥

1. Clone the repository:

git clone https://github.com/Arindam200/awesome-ai-apps/brand-reputation-monitor.git
cd memory_agents/brand_reputation_monitor

2. Install the required dependencies:

# Using pip
pip install -r requirements.txt

# Or using uv (recommended)
uv sync

3. Create a .env file in the project root and add your API credentials:

# Nebius AI Configuration
NEBIUS_API_KEY=your_nebius_api_key

# Bright Data Configuration
BRIGHTDATA_API_KEY=your_brightdata_api_key

Note: This application uses Nebius Token Factory for intelligent brand analysis. Get your API key from Nebius Token Factory.

Usage 🚀

1. Start the Streamlit application:

streamlit run app.py

2. Open your web browser and navigate to the provided local URL (typically http://localhost:8501)

3. Configure your API keys in the sidebar:

   • Nebius AI API Key
   • Bright Data API Key
   • Click "Save API Keys"

4. Start monitoring your brand:

   • Enter your company name
   • Provide search keywords (e.g., "apple news, apple reviews, apple controversy")
   • Let the AI analyze and provide insights
   • Ask follow-up questions about the results

How It Works 🔄

1. Brand Monitoring Workflow

The application uses a sophisticated workflow to monitor and analyze brand reputation:

News Collection

• Searches Google News for relevant articles using your keywords
• Scrapes news pages to extract comprehensive content
• Identifies the most relevant articles for brand reputation monitoring

Content Analysis

• Scrapes individual news articles for detailed content
• Uses AI to extract titles, summaries, and sentiment
• Generates actionable insights about brand reputation

Sentiment Analysis

• Analyzes sentiment as positive, negative, or neutral
• Provides context for sentiment drivers
• Tracks sentiment patterns across different news sources

Insight Generation

• Extracts 3-5 actionable insights per article
• Focuses on brand reputation implications
• Provides concise, strategic recommendations

2. Conversation Flow

Step 1: Company Introduction

• System asks for your company name
• Stores context for personalized monitoring

Step 2: Keyword Configuration

• Specify search keywords for brand monitoring
• Examples: "company news", "company reviews", "company controversy"
• System validates and processes keywords

Step 3: Real-Time Analysis

• AI system performs comprehensive web research using Bright Data
• Scrapes news articles and analyzes content
• Generates detailed brand reputation report
• All findings stored in Memori for future reference

Step 4: Interactive Follow-Up

• Ask questions about the analysis results
• Request clarification on insights or sentiment
• System searches Memori before answering for consistency
• All conversations tracked for context-aware responses

Example Workflow 🔄

1. Launch App: Open the application and enter your API keys
2. Enter Company: "Apple"
3. Set Keywords: "apple news, apple reviews, apple controversy, apple announcement"
4. AI Analysis: System scrapes and analyzes relevant news articles
5. Review Report: Receive detailed analysis with sentiment and insights
6. Follow-Up: Ask questions like "What's the overall sentiment?" or "What are the main concerns?"
7. New Analysis: Request analysis with different keywords or ask for deeper insights

Brand Analysis 📊

The AI system analyzes your brand across multiple dimensions:

News Coverage Analysis

📰 Article Titles: Key headlines mentioning your brand
📝 Content Summaries: Concise summaries of news content
🔗 Source URLs: Direct links to original articles
📊 Coverage Volume: Number of articles and mentions

Sentiment Analysis

😊 Positive Sentiment: Positive news and favorable coverage
😞 Negative Sentiment: Critical coverage and negative mentions
😐 Neutral Sentiment: Factual reporting without bias
📈 Sentiment Trends: Patterns in sentiment over time

Brand Insights

💡 Strategic Insights: Actionable recommendations for brand management
⚠️ Risk Indicators: Potential reputation risks and concerns
🎯 Opportunity Areas: Positive trends and growth opportunities
📋 Action Items: Specific steps to improve brand reputation

API Configuration 🔑

Nebius Token Factory

• Model: Qwen/Qwen3-Coder-480B-A35B-Instruct (via Nebius Token Factory)
• Purpose: News analysis, sentiment analysis, and insight generation
• Framework: Agno agents for structured AI interactions
• Get API Key: Nebius Token Factory

Bright Data

• SERP Zone: sdk_serp (for Google News searches)
• Web Unlocker Zone: unlocker (for article scraping)
• Purpose: Real-time web data extraction
• Scope: News websites, press releases, media coverage

Memori with SQLite

• Database: SQLite (local file: memori.db)
• Purpose: Persistent conversation memory and context storage
• Features: Automatic context search, conversation tracking
• Advantage: No external database setup required

Architecture 🏗️

Modular Design

• UI Layer (app.py): Streamlit interface and conversation flow
• Workflow Layer (workflow.py): Core brand monitoring functions
• Memory Layer: Memori integration for context persistence
• Scraping Layer: Bright Data tools for web research

Key Components

1. Conversation Manager: Handles user interaction and flow states
2. Brand Analysis Engine: Nebius AI-powered news analysis with Agno agents
3. Web Research Engine: Bright Data integration for real-time scraping
4. Memory System: Memori for context storage and retrieval
5. Context Search: Automatic memory search before responding

Intelligence Features 📱

Keyword-Based Monitoring ✅

• Custom search queries for targeted brand tracking
• Flexible keyword combinations for comprehensive coverage
• Real-time news aggregation and analysis
• Focused reputation monitoring

Source Verification ✅

• Only includes exact URLs actually scraped
• Never fabricates or adds placeholder sources
• Complete transparency in research sources
• Direct links to original news articles

Memory-First Approach ✅

• Always searches Memori before answering
• Maintains conversation context across sessions
• Provides consistent insights over time
• Learns from all previous analyses

Example Use Cases 💡

Brand Monitoring

• "Monitor Apple's reputation with keywords: apple news, apple reviews"
• "Track Tesla's sentiment with: tesla news, tesla controversy, tesla stock"
• "Analyze Microsoft's brand perception with: microsoft news, microsoft reviews"

Crisis Management

• "What's the current sentiment about our brand?"
• "Are there any negative mentions we should address?"
• "What are the main concerns mentioned in recent news?"

Competitive Analysis

• "Compare our sentiment to competitor X"
• "What are customers saying about our brand vs competitors?"
• "How is our brand performing in the news compared to last month?"

Follow-Up Analysis

• "Explain the insights from the analysis"
• "What company are we analyzing?"
• "What keywords did we search for?"
• "Can you summarize the key findings?"

File Structure 📁

brand-reputation-monitor/
├── app.py                 # Main Streamlit application
├── workflow.py           # Core brand monitoring functions
├── config.json           # Default configuration
├── requirements.txt      # Python dependencies
├── README.md            # This file
├── assets/              # Logo and image files
│   ├── gibson.svg       # GibsonAI logo
│   └── brightdata_logo.png
└── memori.db           # SQLite database (created automatically)

Contributing 🤝

Contributions are welcome! Please feel free to submit a Pull Request.

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

Troubleshooting 🔧

API Key Errors

• Check API keys are correctly entered in sidebar
• Verify environment variables in .env file
• Ensure both Nebius AI and Bright Data keys are valid
• Get Nebius Token Factory API key from Nebius Token Factory

Bright Data Issues

• Ensure Bright Data API key is valid and has credits
• Check internet connection for web scraping
• Verify Bright Data zones are properly configured

Memori Initialization

• SQLite database created automatically on first use
• Check file permissions in project directory
• Database file memori.db should be created automatically

Analysis Performance

• First analysis may take 2-5 minutes (web research)
• Ensure Bright Data has sufficient credits
• Check internet connection for web scraping
• Large keyword lists may take longer to process

License 📄

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