Emotional Analysis of English Text Based on Deep Learning and Application of Emotional Education

In order to improve the effect of sentiment analysis in English text and improve the efficiency of sentiment education, this paper captures sentiment information through sentiment analysis module and sentiment entity direction module, and purposefully identifies the direction information between sentiments. In order to obtain the sentiment features and directional features of sentiment entities in English text, this paper uses a pre-training model to map words in English text from low-dimensional space to multi-dimensional space one by one, and segments words in course reviews of data sets based on English phrase segmentation components in Python environment. Moreover, in order to support the segmentation operation of emoticons with special characters, the regular expressions and related codes matching keywords in the component are modified, and the process of sentiment analysis in English text based on deep learning is analyzed. Through comparative analysis, it can be seen that the method proposed in this paper has great advantages in accuracy, precision, recall and AUC compared with traditional methods, so it has certain effect in sentiment analysis of English texts.