School of Foreign Languages, Hunan University of Finance and Economics, Changsha, China.
School of Humanities, Nanyang Technological University, Singapore, Singapore.
Front Public Health. 2022 Oct 20;10:981182. doi: 10.3389/fpubh.2022.981182. eCollection 2022.
This paper conducts sentiment analysis on the evaluation discourse of business English translation based on language big data mining of public health environment, and aims to find a reasonable algorithm to conduct detailed research on all aspects of sentiment analysis.
This paper focuses on three areas of sentiment information, extraction, sentiment information retrieval, and sentiment information submission, using scale analysis and feedback analysis, combined with related algorithms of big data mining technology, such as decision trees and clustering algorithms, through the level of emotional words appearing in the corpus, phrase-level, text-level, etc., and combine the text model with the combined reliability to output the evaluation object and evaluation feature separately, and propose an evaluation method to calculate the sensitivity of the evaluation feature, so as to accurately improve the sensitivity of the evaluation feature. It is mainly divided into two categories for data analysis. One is to focus on the public health environment of the characteristics of business English translation itself, and the other is to conduct research on the application of big data mining in the evaluation of translation discourse.
The research data show that the smallest gap between the sentiment orientation of the discourse evaluation perspective is the output of the language discourse, and the smallest gap in the attributes of the evaluation object is at the phrase level, and the gap value is 3.5; for the evaluation object, the maximum difference is 3.4.
With the development of science and technology and the economy, the public health environment has become more and more complex, and business English translation has received more and more attention. The sentiment analysis of evaluation discourse in this field is a means of expressing language characteristics. In order to enrich research in this field, the study of this article is necessary.
This study has a deeper understanding of the affective analysis of evaluation discourse in public health environment business English translation. The clustering algorithm of big data mining technology applied can provide an important guarantee for the actual conclusion of this research and quantitative analysis of the positive evaluation and criticism of evaluation. To solve the various problems encountered in translation, so as to improve the translator's own translation level, and promote the research of translation methods in Chinese translation.
本文基于公共卫生环境的语言大数据挖掘，对商务英语翻译的评价语篇进行情感分析，旨在找到一种合理的算法，对情感分析的各个方面进行详细研究。
本文重点关注情感信息的三个领域，即情感信息提取、情感信息检索和情感信息提交，采用量表分析和反馈分析，结合大数据挖掘技术的相关算法，如决策树和聚类算法，通过语料库中情感词出现的水平、短语级、文本级等，结合文本模型与组合可靠性，分别输出评价对象和评价特征，并提出一种计算评价特征敏感性的评价方法，从而准确提高评价特征的敏感性。它主要分为两类进行数据分析。一是聚焦商务英语翻译自身的公共卫生环境特征，二是研究大数据挖掘在翻译话语评价中的应用。
研究数据表明，话语评价视角的情感倾向的最小差距是语言话语的输出，评价对象的属性的最小差距在短语级，差距值为 3.5；对于评价对象，最大差异为 3.4。
随着科学技术和经济的发展，公共卫生环境变得越来越复杂，商务英语翻译受到了越来越多的关注。该领域的评价语篇情感分析是一种表达语言特征的手段。为了丰富该领域的研究，本文的研究是必要的。
本研究对公共卫生环境商务英语翻译评价语篇的情感分析有了更深入的了解。大数据挖掘技术的聚类算法的应用，可以为本文研究的实际结论和评价的积极评价和批评的定量分析提供重要保障。解决翻译中遇到的各种问题，从而提高翻译人员自身的翻译水平，促进汉译的翻译方法研究。
