• Mr. K.V. SAIPHANI
• S. SUSUMANTH
• S. SIVA NAGENDRA
• K. RAVI KUMAR
• J. RAVI KUMAR

This study addresses a gap in research by focusing on the cognitive and emotional impacts of e-learning platforms, offering a novel approach to assess user experiences. It proposes a non-invasive method to gauge the psychological effects of these platforms by utilizing feedback data in the form of videos, analyzing emotions within the videos to determine user sentiment. This automated process enables quick comprehension of feedback for individuals without specialized knowledge of the system. The study introduces feedback analysis for e-learning platforms using deep learning techniques, specifically focusing on facial expression analysis. Convolutional Neural Networks (CNNs), a type of deep learning model, have gained popularity for their ability to accurately interpret emotions from facial images. These models can efficiently learn and extract crucial features by training on extensive datasets of labeled facial expression images. The application described takes video feedback from various e-learning platforms as input and conducts emotion analysis on the videos.

Machine Learning Driven Emotion Identification For Feedback Analysis In E-learning Platforms

"Machine Learning Driven Emotion Identification For Feedback Analysis In E-learning Platforms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.l708-l713, April-2024, Available :http://www.jetir.org/papers/JETIR2404B95.pdf

"Machine Learning Driven Emotion Identification For Feedback Analysis In E-learning Platforms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppl708-l713, April-2024, Available at : http://www.jetir.org/papers/JETIR2404B95.pdf