A Classification Method for English Texts Based on Hybrid Recurrent Neural Network and Graph Construction in Social Recommendation Systems

Abstract

Text classification (TC) methods are widely used in the field of recommendation systems. Text data has the characteristics of short length, large capacity, and variable data distribution, resulting in sparsity and concept drift issues, which pose challenges to text classification methods. In view of this, a recursive neural network and graph construction method has been proposed for classifying English texts in social recommendation systems. First, the English text document serves as the original dataset. Then, the data is preprocessed through tokenization, removal of stop words, stemming, and min-max normalization. Meanwhile, the principal component analysis is used to extract text features from English texts. Finally, feature selection is performed using information gain technology to improve text classification performance. The simulation results show that compared with the comparison method, the proposed algorithm has ideal results in accuracy, precision, recall, F1-score, true positive rate, and false positive rate. Meanwhile, based on experimental data, it was found that the proposed method outperforms state-of-the-art methods in text analysis of English text works.