<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper firstly researches English text emotion expression and information communication, classifies English text emotion expression and information communication according to the human emotion-value relationship, and summarizes the characteristics of English emotion expression and information communication. Secondly, using artificial intelligence technology, it is proposed to construct an analysis model for English text emotion and information communication using the BiLSTM neural network. To deal with the characteristics of English text quickly and efficiently, it is necessary to encode the emotional information of English text, and based on encoding, the BiLSTM neural network is applied to extract the emotional features of English text and solve the problem of the loss of emotional features through the loss function. Then, the crawler tool is used to obtain the dataset from the Chinese English module under the MOOC of Chinese universities, and the evaluation indexes are set according to the model’s performance, followed by the experimental analysis of the English text emotion expression and information conveyance. The results show that compared with the original CNN, LSTM, and T-LSTM, the BiLSTM-based neural network performs better in the task of text emotion expression and information conveyance, with the accuracy rate staying above 0.925, and the effect on the English dataset is a bit better than that on the Chinese dataset. This study aims to enhance English teaching and communication between Chinese and foreign cultures.</p>
</abstract>

This paper firstly researches English text emotion expression and information communication, classifies English text emotion expression and information communication according to the human emotion-value relationship, and summarizes the characteristics of English emotion expression and information communication. Secondly, using artificial intelligence technology, it is proposed to construct an analysis model for English text emotion and information communication using the BiLSTM neural network. To deal with the characteristics of English text quickly and efficiently, it is necessary to encode the emotional information of English text, and based on encoding, the BiLSTM neural network is applied to extract the emotional features of English text and solve the problem of the loss of emotional features through the loss function. Then, the crawler tool is used to obtain the dataset from the Chinese English module under the MOOC of Chinese universities, and the evaluation indexes are set according to the model’s performance, followed by the experimental analysis of the English text emotion expression and information conveyance. The results show that compared with the original CNN, LSTM, and T-LSTM, the BiLSTM-based neural network performs better in the task of text emotion expression and information conveyance, with the accuracy rate staying above 0.925, and the effect on the English dataset is a bit better than that on the Chinese dataset. This study aims to enhance English teaching and communication between Chinese and foreign cultures.

Emotional Expression and Information Communication in English Texts Based on Artificial Intelligence Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper firstly researches English text emotion expression and information communication, classifies English text emotion expression and information...

Emotional Expression and Information Communication in English Texts Based on Artificial Intelligence Technology

Publicado en línea: 08 nov 2023

Páginas: -

Recibido: 31 ene 2023

Aceptado: 19 may 2023

DOI: https://doi.org/10.2478/amns.2023.2.01047

Palabras clave
BiLSTM neural network, Loss function, English text, Emotional features, Message conveyance

© 2023 Jingbo Hao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Emotional Expression and Information Communication in English Texts Based on Artificial Intelligence Technology

Publicado en línea: 08 nov 2023

Páginas: -

Recibido: 31 ene 2023

Aceptado: 19 may 2023

DOI: https://doi.org/10.2478/amns.2023.2.01047

Palabras claveBiLSTM neural network, Loss function, English text, Emotional features, Message conveyance

© 2023 Jingbo Hao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
BiLSTM neural network, Loss function, English text, Emotional features, Message conveyance