Nirmalvekariya/Video-Sentiment-Analysis

This project is a standard academic/tutorial implementation of facial emotion recognition (FER) using a CNN. With a reported accuracy of 72% and no updates in over five years (velocity 0.0), it is technically obsolete compared to modern standards. The 40 stars and 10 forks suggest it has served primarily as a reference for students rather than a production-grade tool. Frontier labs and major cloud providers (AWS Rekognition, Azure Face API, Google Cloud Vision) have long since commoditized this capability with significantly higher accuracy and robust temporal analysis. Furthermore, the advent of multimodal LLMs like GPT-4o and Gemini 1.5 Pro allows for nuanced sentiment analysis that considers context, speech, and facial cues simultaneously, rendering standalone FER models like this one irrelevant for most commercial use cases. There is no moat, no unique data advantage, and the codebase relies on legacy versions of Keras/TensorFlow.