Language Academy, Faculty of Social Sciences and Humanities, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
School of Foreign Languages, Yan'an University, Yan'an, Shaanxi, China.
PLoS One. 2023 Jan 25;18(1):e0280190. doi: 10.1371/journal.pone.0280190. eCollection 2023.
Recently, ecological damage and environmental pollution have become increasingly serious. Experts in various fields have started to study related issues from diverse points of view. To prevent the accelerated deterioration of the ecological environment, ecolinguistics emerged. Eco-critical discourse analysis is one of the important parts of ecolinguistics research, that is, it is a critical discourse analysis of the use of language from the perspective of the language's ecological environment. Firstly, an ecological tone and modality system are constructed from an ecological perspective. Under the guidance of the ecological philosophy of "equality, harmony, and symbiosis", this study conducts an ecological discourse analysis on the Sino-US trade friction reports, aiming to present the similarities and differences between the two newspapers' trade friction discourses and to reveal the ecological significance of international ecological factors in the discourse. Secondly, this method establishes a vector expression of abstract words based on emotion dictionary resources and introduces emotion polarity and part-of-speech features of words. Then the word vector is formed into the text feature matrix, which is used as the input of the Convolutional Neural Network (CNN) model, and the Back Propagation algorithm is adopted to train the model. Finally, in the light of the trained CNN model, the unlabeled news is predicted, and the experimental results are analyzed. The results reveal that during the training process of Chinese and English datasets, the accuracy of the training set can reach nearly 100%, and the loss rate can be reduced to 0. On the test set, the classification accuracy of Chinese text can reach 83%, while that of English text can reach 90%, and the experimental results are ideal. This study provides an explanatory approach for ecological discourse analysis on the news reports of Sino-US trade frictions and has certain guiding significance for the comparative research on political news reports under different ideologies between China and the United States.
最近，生态破坏和环境污染问题日益严重，各领域专家开始从不同角度研究相关问题。为了防止生态环境的加速恶化，生态语言学应运而生。生态批评话语分析是生态语言学研究的重要组成部分之一，即从语言的生态环境角度对语言的使用进行批评性话语分析。首先，从生态角度构建生态语气和情态系统。在“平等、和谐、共生”的生态哲学指导下，对中美贸易摩擦报道进行生态话语分析，旨在呈现两报贸易摩擦话语的异同，揭示国际生态因素在话语中的生态意义。其次，该方法基于情感词典资源建立了抽象词的向量表示，并引入了词的情感极性和词性特征。然后将词向量组合成文本特征矩阵，作为卷积神经网络（CNN）模型的输入，采用反向传播算法对模型进行训练。最后，根据训练好的 CNN 模型对未标记的新闻进行预测，并对实验结果进行分析。结果表明，在中文和英文数据集的训练过程中，训练集的准确率接近 100%，损失率可降低到 0。在测试集上，中文文本的分类准确率可达 83%，而英文文本的分类准确率可达 90%，实验结果理想。本研究为中美贸易摩擦新闻报道的生态话语分析提供了一种解释方法，对中美不同意识形态下政治新闻报道的比较研究具有一定的指导意义。
