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.
Highlights
As society develops, education should keep pace with the time
Since the same word may show different sentiment polarity in different contexts, it is difficult to guarantee the accuracy of sentiment analysis of ELT texts without differentiating word contexts. e LDA extended model is one of the most important methods for text sentiment analysis, but the current research fails to consider the difference in sentiment polarity of the same word in different contexts and the influence of nonfeatured sentiment words on the sentiment polarity of ELT texts. erefore, this paper proposes an ELT sentiment analysis method based on contextual classification and genetic algorithms
To address the above problems, in order to further improve the accuracy of text sentiment polarity analysis using the LDA model extension method, this paper proposes an ELT sentiment analysis method based on context classification and genetic algorithm. e method first classifies ELT into contextual topics using the LDA model and divides ELT words into different contextual topics to form ELT topic sets and ELT topic word sets; for each topic of ELT and topic word sets, a genetic algorithm is used to calculate the sentiment values of all words, and the sentiment values of words are used to calculate ELT sentiment tendency
Summary
Education should keep pace with the time. Based on the fundamental task of establishing moral education, foreign languages curriculum standards have updated the content of the curriculum and emphasized the thematic contexts [1]. E theme context of “People and Society” is conducive to the formation of good social interaction, the establishment of good interpersonal relationships, the formation of good literacy among students, the cultivation of the innovative spirit of developing information technology, and the better integration of students into social life; the theme context of “People and Nature” advocates understanding nature, knowing nature, caring nature, cultivating students’. Since the same word may show different sentiment polarity in different contexts, it is difficult to guarantee the accuracy of sentiment analysis of ELT texts without differentiating word contexts. E LDA extended model is one of the most important methods for text sentiment analysis, but the current research fails to consider the difference in sentiment polarity of the same word in different contexts and the influence of nonfeatured sentiment words on the sentiment polarity of ELT texts. Since the same word may show different sentiment polarity in different contexts, it is difficult to guarantee the accuracy of sentiment analysis of ELT texts without differentiating word contexts. e LDA extended model is one of the most important methods for text sentiment analysis, but the current research fails to consider the difference in sentiment polarity of the same word in different contexts and the influence of nonfeatured sentiment words on the sentiment polarity of ELT texts. erefore, this paper proposes an ELT sentiment analysis method based on contextual classification and genetic algorithms
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