Application of Thematic Context-Based Deep Learning in Foreign Language Teaching

Abstract

Foreign language teaching is not simply the transfer of knowledge, but rather the placement of students in contexts to explore and discover problems. The thematic contexts do not exist in isolation. Teachers should adopt certain teaching strategies based on thematic contexts, rely on relevant discourse, study the discourse text, and use rich learning and activities as the driving force to highlight students’ active experience and emotional experience. In this paper, we propose an ELT (English Language Teaching) affective analysis method based on contextual classification and genetic algorithms. The method first constructs ELT topic sets and ELT topic word sets using the LDA (latent Dirichlet allocation) model, then applies genetic algorithms to each ELT topic word set one by one using ELT label data to automatically iterate the sentiment values of words in the word sets, and finally calculates the sentiment polarity of ELT texts using the sentiment values of words in the word sets. The experimental results show that the accuracy of this method improves 3.12% compared with LDA, the recall rate reaches 87.32%, and F1 reaches 73.79%, which can obtain ELT sentiment information from contextual and nonfeatured sentiment words and effectively improve the accuracy of sentiment classification.