Welcome to EmotionLib

Advanced library for media content analysis ensuring safety and enhanced user experiences.

What is it?

EmotionLib is a robust C library designed for media content analysis. It identifies unsafe content, enhances recommendations, and classifies emotional tones for safer and engaging user experiences.

Key Metrics

Content Classification

> 99.9% Accuracy

MPAA Rating Prediction

> 0.92 R² Score

Architecture

Filter

Classifies frames into safe, explicit, or violent categories with high precision.

Filter Model Example

Positiveness

Evaluates emotional tone of frames, distinguishing between positivity and negativity.

Positiveness Model Example

SAMP / Safeness and MPAA

Predicts MPAA ratings and flags unsafe content by analyzing per-frame outputs from other components.

Samp Model Example

Integration

Easily integrate EmotionLib into your applications with the following steps:

  1. git clone https://github.com/EmotionEngineer/EmotionLib.git
  3. make CFLAGS=-fno-lto
Visit GitHub Repository