Sentiment Analysis for Images

This project performs sentiment analysis on a sequence of images using computer vision techniques. Each image is assigned a sentiment score in the form of a percentage across categories of Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral, the universal basic emotions defined by Psychologist Paul Ekman, using the fer library from Justin Shenk https://github.com/JustinShenk/fer The code generates an Excel file with the image name, the percentages for each emotion, and the dominant emotion.

Project Structure

sentiment-analysis-project
├── main.py                # Entry point of the application
├── requirements.txt       # Project dependencies
├── .gitignore             # Files and directories to ignore in Git
├── example_images         # folder with example images
├── example_results.xlsx   # example results from running the example images
└── README.md              # Project documentation

Installation

1. Clone the repository:

   git clone https://github.com/hleve/Visual_Analysis_Tool
   cd sentiment-analysis-project
   

2. Create a virtual environment:

   python -m venv venv
   

3. Activate the virtual environment:

   On Windows:

   venv\Scripts\activate
   

   On macOS and Linux:

   source venv/bin/activate
   

4. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Place your images in a designated directory.

2. Update the image directory path in main.py.

3. Update the output directory path in main.py.

4. Run the application:

   python main.py
   

Output

The application will generate an Excel file containing the image names and their corresponding sentiment scores.

Contributing

Feel free to submit issues or pull requests for improvements or bug fixes.