YouTube Comment Sentiment Analysis Dashboard

I'll create a full-stack Next.js application that analyzes YouTube comments for sentiment and displays analytics in a responsive dashboard.

Database Schema

Here's the MySQL database schema for storing the YouTube comment sentiment analysis data:

System Architecture

Features and Implementation Details

1. YouTube Data Fetching

   • The application uses the YouTube Data API to fetch video details and comments
   • Comments are retrieved in batches to handle videos with large numbers of comments

2. Sentiment Analysis

   • Comments are analyzed using a machine learning model to classify them as positive, negative, or neutral
   • The sentiment analysis service can be easily swapped with different models or APIs

3. Data Storage

   • All analysis results are stored in a MySQL database for historical tracking
   • The schema supports efficient querying for dashboard analytics

4. User Interface

   • Responsive design works on mobile, tablet, and desktop
   • Real-time analysis with loading states for better user experience
   • Comprehensive dashboard with visualizations using Recharts

5. Analytics

   • Track sentiment trends over time
   • Compare sentiment across different videos
   • Filter and search historical analyses

Deployment Instructions

1. Set up a MySQL database and run the schema creation script

2. Configure environment variables:

   • YOUTUBE_API_KEY - Your YouTube Data API key
   • DB_HOST, DB_USER, DB_PASSWORD, DB_NAME - MySQL connection details

3. Deploy to Vercel:

   vercel

This application provides a complete solution for analyzing YouTube comment sentiment, with a responsive UI, backend processing, and database storage. The modular architecture makes it easy to extend with additional features or different ML models in the future.

Please make sure to add the following environment variables to your project:

Great! Now that you have all the necessary environment variables set up, your YouTube Comment Sentiment Analysis application is ready to go. Let me walk you through how to use the application and suggest some potential enhancements.

Getting Started

1. Home Page: The landing page provides a quick overview of the application's capabilities and a form to quickly analyze a YouTube video.

2. Analyze Page: Enter any YouTube video URL to analyze its comments. The system will:

   • Fetch the video details and comments using the YouTube API
   • Analyze the sentiment of each comment
   • Store the results in your MySQL database
   • Display a summary of positive, negative, and neutral comments

3. Dashboard: View aggregated analytics across all your analyzed videos, including:

   • Total videos and comments analyzed
   • Overall sentiment distribution
   • Sentiment trends over time
   • Performance of your most recent videos
4. History: Access all your previous analyses with search functionality to find specific videos.

How the Sentiment Analysis Works

The application uses a machine learning approach to analyze comment sentiment:

1. Data Collection: Comments are fetched from the YouTube API using your YOUTUBE_API_KEY
2. Text Processing: Each comment is processed to extract meaningful features
3. Sentiment Classification: The system classifies each comment as positive, negative, or neutral
4. Aggregation: Results are aggregated to provide overall sentiment metrics
5. Storage: All data is stored in your MySQL database for historical tracking

Potential Enhancements

Here are some ways you could enhance the application:

1. Advanced ML Models: Replace the basic sentiment analysis with more sophisticated models like:

   • Fine-tuned BERT or RoBERTa models for more accurate sentiment classification
   • Multi-language support for international channels
   • Emotion detection beyond just positive/negative/neutral

2. Additional Analytics:

   • Comment topic clustering to identify common themes
   • Keyword extraction to find frequently mentioned terms
   • Sentiment change over time for individual videos

3. Channel-Level Analysis:

   • Automatically analyze all videos from a channel
   • Compare sentiment across different video categories
   • Identify trends in audience sentiment

4. User Management:

   • Add authentication to support multiple users
   • Role-based access control for team collaboration
   • Custom dashboards for different stakeholders

5. Notification System:

   • Set up alerts for negative sentiment spikes
   • Scheduled reports via email
   • Integration with other platforms (Slack, Discord, etc.)

Technical Implementation Notes

• The application uses Next.js App Router for efficient server-side rendering and API routes
• Sentiment analysis is performed using a combination of lexicon-based approaches and machine learning
• The MySQL database schema is optimized for both storage and quick retrieval of analytics data
• The UI is fully responsive and built with Tailwind CSS and shadcn/ui components

To start analyzing YouTube comments, simply navigate to the Analyze page and enter a YouTube video URL. The system will handle the rest!

Would you like me to explain any specific part of the application in more detail?