评价系统视阈下中美企业致股东信情感话语对比分析——基于情感词典和机器学习的文本挖掘技术

A Comparative Study of the Discourse on Emotions of Chinese and American CEO's Letters from the Perspective of Appraisal System — Text Mining Technology Based on Sentiment Dictionary and Machine Learning

  • 摘要: 本文整合情感词典和机器学习的文本挖掘技术,运用语言学评价系统对中美企业英文版致股东信的情感话语进行了对比分析,阐明其异同及原因。研究发现:1)情感词典与机器学习算法相结合的准确率优于单独使用情感词典的准确率。2)中国企业致股东信情感倾向更加积极,美国企业则偏向中性。美国企业在信中既展示企业自身社会责任履行情况,也关注其外部供应商和社区情况。3)中美企业各自不同的经济制度、文化特点、思维模式导致信中句式结构、话语主题、话语分布存在明显差异。本研究能为中国企业撰写符合对方国家语言文化特征的致股东信提供借鉴,有助于促进企业信息披露的规范,塑造良好的中国企业形象,并为商务英语教学提供指导。

     

    Abstract: This paper integrates the text mining technology of sentiment dictionary and machine learning, and uses the linguistic appraisal system to compare and analyze the discourse on emotions of English version of Sino-US Enterprises' CEO's letters.The research finds that:1) the accuracy of combining sentiment dictionary with machine learning algorithms are better than that of using sentiment dictionary alone.2) Chinese companies have a more positive sentiment, while American companies have a more neutral sentiment.In the letters, American companies not only show their own social responsibility performances, but also focus on the situation of their external suppliers and communities.3) different economic systems, cultural features and thinking patterns of Chinese and American enterprises lead to obvious differences in sentence structure, discourse theme and discourse distribution in the letters.This study can provide reference for Chinese enterprises to write CEO's letters in accordance with the characteristics of the other party's native language and culture, thus promoting the norms of enterprise information disclosure, and shaping a good image of Chinese enterprises, and also provide guidance for business English teaching.

     

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