The data visualization and intelligent text analysis for effective evaluation of English language teaching.

This study aims to explore and construct an evaluation system of English language teaching effect combining data visualization analysis and intelligent text analysis technology. The study is based on the limitations of traditional English teaching evaluation methods, that is, relying too much on teachers' subjective judgment and lacking real-time monitoring, which cannot meet the needs of personalized teaching. In this study, data visualization technology is used to transform students' learning data into intuitive charts, and intelligent text analysis technology is used to deeply explore students' writing and oral performance in order to achieve accurate teaching decisions. The research methods include emotion analysis with Bidirectional Encoder Representations from Transformers (BERT) model, theme modeling with Latent Dirichlet Allocation (LDA) model, semantic analysis with Word2Vec model, and model optimization with ensemble learning methods such as Bagging, XGBoost and Stacking. The experimental results show that the accuracy of Stacking model in training set and testing set is 95.0% and 94.3% respectively, which is significantly better than other single models. In addition, the optimization model combining BERT, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and self-attention mechanism also shows significant advantages in emotional analysis and teaching content analysis. This study not only provides a new idea for the development of educational evaluation methods, but also a powerful tool for realizing personalized and accurate teaching.