Video Sentiment Analysis (vsentiment)

A Streamlit application that analyzes sentiment from video recordings by:

1. Extracting audio from video
2. Transcribing speech to text using OpenAI Whisper
3. Performing sentiment analysis using German BERT model
4. Visualizing sentiment changes over time

Features

• Video upload interface
• High-quality speech-to-text transcription (Whisper)
• German language sentiment analysis (oliverguhr/german-sentiment-bert)
• Facial expression analysis (DeepFace)
• Combined time-series visualization of:
  • Text sentiment
  • Facial emotions (angry, disgust, fear, happy, sad, surprise, neutral)
• Automatic cleanup of temporary files

Installation

1. Clone this repository
2. Install dependencies:

pip install -r requirements.txt

3. Download Whisper model (will happen automatically on first run)

Container Deployment (Docker)

1. Build the container image:

docker build -t vsentiment .

2. Run the container:

docker run -p 8501:8501 vsentiment

3. Access the application at:

http://localhost:8501

Note: For GPU support, install NVIDIA Container Toolkit and add --gpus all to the docker run command.

Usage

1. Run the application:

streamlit run app.py

2. Open http://localhost:8501 in your browser
3. Upload a video file (MP4 or MOV)
4. View analysis results including:
   • Full transcript
   • Sentiment timeline chart
   • Overall sentiment score

Technical Details

• Speech Recognition: OpenAI Whisper (base model)
• Sentiment Analysis: German BERT model (oliverguhr/german-sentiment-bert)
• Visualization: Matplotlib with Streamlit
• Audio Processing: MoviePy

Project Structure

vsentiment/
├── app.py              # Main MCP server implementation
├── requirements.txt    # Python dependencies
├── README.md           # Project documentation
├── PLANNING.md         # Project planning
├── TASK.md             # Task tracking
└── .streamlit/         # Streamlit config
    └── config.toml

Code Standards

• Follows PEP8 standards
• Uses type hints for all functions
• Google-style docstrings for documentation
• Formatted with Black
• Uses Pydantic for data validation

Future Improvements

• Frame-level video segmentation for more granular analysis
• Multi-language support
• Whisper model upgrades (small/medium)
• Facial expression analysis integration