Research on Text Emotion Analysis Based on BMCBMA Model in Online Education Environment

Abstract

This paper suggests a text emotion analysis approach built on the BMCBMA model for use in an online education environment aiming at the issues of poor text utilization, challenge in effective information extraction, and a failure to effectively recognize word polysemy in existing text emotion analysis task research. Initially, a language model is constructed using Bidirectional Encoder Representations from Transformers (BERT) pre-training. The word vector trained by BERT pre-training is selected to supplant the word vector trained in the conventional manner, and the semantic vector is generated dynamically using the context of the word. Second, the Bi-directional Long Short-Term Memory (BiLSTM) model is employed to record semantic information by simultaneously integrating both positive and negative orientations to assess the emotional polarity information for every text in its entirety. The Multi-channel Convolutional Neural Networks (CNN) model is then utilized to derive textual local feature information of text. Lastly, the attention mechanism is used to cause all types of features to interact and to derive a deeper semantic association within the context. Experiments indicate that the suggested method has accuracy, F1, recall, and precision values of 0.915, 0.911, 0.915 and 0.908, correspondingly. The suggested approach performs significantly better in text emotion analysis than the comparison method.