Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164779
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dc.contributor.authorLiu, Songen_US
dc.contributor.authorChen, Yukunen_US
dc.contributor.authorXu, Kunpeien_US
dc.contributor.authorLin, Jiaxinen_US
dc.date.accessioned2023-02-14T04:00:04Z-
dc.date.available2023-02-14T04:00:04Z-
dc.date.issued2022-
dc.identifier.citationLiu, S., Chen, Y., Xu, K. & Lin, J. (2022). Emotional analysis of evaluation discourse in business English translation based on language big data mining of public health environment. Frontiers in Public Health, 10, 981182-. https://dx.doi.org/10.3389/fpubh.2022.981182en_US
dc.identifier.issn2296-2565en_US
dc.identifier.urihttps://hdl.handle.net/10356/164779-
dc.description.abstractPurpose: 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. Methodology: 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. Research findings: 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. Research implications: 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. Practical implications: 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.en_US
dc.language.isoenen_US
dc.relation.ispartofFrontiers in Public Healthen_US
dc.rights© 2022 Liu, Chen, Xu and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.subjectHumanities::Languageen_US
dc.titleEmotional analysis of evaluation discourse in business English translation based on language big data mining of public health environmenten_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Humanitiesen_US
dc.identifier.doi10.3389/fpubh.2022.981182-
dc.description.versionPublished versionen_US
dc.identifier.pmid36339211-
dc.identifier.scopus2-s2.0-85141210104-
dc.identifier.volume10en_US
dc.identifier.spage981182en_US
dc.subject.keywordsLanguage Big Data Miningen_US
dc.subject.keywordsPublic Health Environmenten_US
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