Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation

Course recommendation is a key for achievement in student’s academic path. However, it challenging to appropriately select course content among numerous online education resources, due the differences users’ knowledge structures. Therefore, this paper develops novel sentiment classification approach recommending courses using Taylor-chimp Optimization Algorithm enabled Random Multimodal Deep Learning (Taylor ChOA-based RMDL). Here, proposed Taylor ChOA newly devised by combination of concept and Chimp (ChOA). Initially, review done find optimal course, thereafter feature extraction performed extracting various significant features needed further processing. Finally, RMDL, which trained optimization algorithm, named ChOA. Thus, positively reviewed are obtained from classified sentiments improving procedure. Extensive experiments conducted E-Khool dataset Coursera dataset. Empirical results demonstrate that RMDL model significantly outperforms state-of-the-art methods tasks.