Sentimental analysis on E-Learning videos using Hybrid Algorithm based on Naïve Bayes and SVM

Abstract

E-learning has piqued the interest of companies, educational institutions, and people alike. E-learning systems are becoming increasingly prominent as an educational trend. It typically refers to educational attempts spread via the use of computers in an attempt to transmit information. Students can engage with other students and discuss questions about certain topics thanks to e-Learning platforms and similar technologies. Teachers, on the other hand, frequently remain outside of this process and are unaware of the learning issues that exist in their classes. Adopting a Sentiment Analysis approach for detecting the student mood throughout the learning process might be a solution for better learning method. In this paper, we used sentimental analysis on E-learning data. SVM and Naïve Bayes algorithms are fused to be used as a Hybrid algorithm for better accuracy. Performance analysis shows that state-of-art methods like Naïve Bayes and SVM algorithms give 90% and 94% respectively whereas our proposed hybrid method gives approximately 97% of accuracy.