With the rise of short-form video content such as reels, TikTok, and Instagram Stories, there is a growing need for tools that can help users analyze and express emotions in their videos.
To address this problem, we p ropose a Deep Learning-based approach for emotion detection in social media reels. Our goal is to develop a model that can accurately and automatically detect emotions such as happiness, sadness, anger, fear, and surprise in short-form video content.
it based on three phases
- Instagram Database (Instagram-Graph-API)
- Modeling (Keras Modeling)
- Video Analytics (OpenCV)
