Intelligent Text Sentiment Analysis of Emotional Beauty in the English Translation: Based on TB-LSTM Algorithm

Abstract

In the existing deep learning sentiment analysis methods, text is directly processed into a sequence of words, which often ignores the important components of the text and the topic of the text. To solve these problems, the topic of text is extracted through latent Dirichlet Allocation model, and then, a method for integrating topic information into neural networks is proposed. Furthermore, an emotion classification model that integrates the topic information is constructed. An attention mechanism is used at the lexical level and the sentence level, paying attention to the more and less important content separately when building the document presentation. The results show that the proposed model has excellent performance in all four kinds of data, among which the accuracy rate, recall rate, and F1-score of CCF-BDC are the best, reaching 0.63, 0.72, and 0.7, respectively. The translation accuracy also reaches 0.85. These results show that the model proposed in this study has better performance in sentiment analysis. These research results will help improve the user’s reading experience and make machine translation better meet the needs of human language expression.