Research on university email analysis based on SVM-RFE and Transformer-TBAM - LI Zhen; LI Zhichao; CHEN Lin (Tongxin xuebao)
By mining and analyzing email text data from universities, it can help faculty members better understand students’opinions and suggestions, and improve management efficiency. At present, deep learning methods are the main approach for text sentiment analysis, but existing methods have not fully utilized the features in Chinese text. To address this issue, a framework based on SVM-RFE and Transformer models was proposed for processing university emails. This architecture reconstructs a dual branch attention model and feature filtering mechanism to deeply extract effective feature information. The experiment shows that the algorithm achieves an accuracy of 94.67% in the classification of university email datasets, which is 1.2% higher than traditional algorithms.