TGAE: MOOC comment sentiment analysis based on TCN-BiGRU network model with multi-head attention mechanism

Mining emotional information plays an important role in improving MOOC quality. However, the word embedding method, which has been greatly improved, has insufficient ability to express emotional semantics, and it cannot extract more semantic information from MOOC comments. To solve these problems, this study proposes a TCN-BiGRU network model with attention embedding (TGAE) based on the mechanism of multi-head attention to improve the accuracy and robustness of MOOC comment sentiment analysis. In the word-embedding stage, the TGAE combines BERT embedding with improved GloVe embedding. The TGAE uses a TCN to extract local semantic features and a BiGRU to acquire contextual semantic information. The TCN enhances the computational efficiency and long-distance dependency extraction of the dilated convolution. The BiGRU processes forward and backward contexts simultaneously to enhance semantic understanding. The mechanism of multi-head attention is used to select some heads and then assign different weights to the features of the TCN and BiGRU features, which can focus on the key information of sentiment from multiple views. The experiment shows that TGAE achieves better performance in the sentiment classification of MOOC comments, and the accuracy and F1-score are improved.