Abstract
Nonlinear models of neural networks demonstrate the ability to autonomously extract significant attributes from a given target, thus facilitating automatic analysis of classroom emotions. This article introduces an online auxiliary tool for analyzing emotional states in virtual classrooms using the nonlinear vision algorithm Transformer. This research uses multimodal fusion, students' auditory input, facial expression and text data as the foundational elements of sentiment analysis. In addition, a modal feature extractor has been developed to extract multimodal emotions using convolutional and gated cycle unit (GRU) architectures. In addition, inspired by the Transformer algorithm, a cross-modal Transformer algorithm is proposed to enhance the processing of multimodal information. The experiments demonstrate that the training performance of the proposed model surpasses that of similar methods, with its recall, precision, accuracy, and F1 values achieving 0.8587, 0.8365, 0.8890, and 0.8754, respectively, which is superior accuracy in capturing students' emotional states, thus having important implications in assessing students' engagement in educational courses.