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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >CSR Image Construction of Chinese Construction Enterprises in Africa Based on Data Mining and Corpus Analysis
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CSR Image Construction of Chinese Construction Enterprises in Africa Based on Data Mining and Corpus Analysis

机译：基于数据挖掘和语料库分析的非洲中国建筑企业CSR图像建设

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摘要

Since there is negative coverage of some western media on the business activities of Chinese overseas enterprises, which has adverse impact on the image of Chinese enterprises and even the national image of China, this study aims to detect the corporate social responsibility image (hereafter CSR image) of Chinese construction enterprises in Africa (hereafter CCEA) through analyzing the coverage of Financial Times (hereafter FT) from the UK and The Wall Street Journal (hereafter WSJ) from the US and dig up the motives behind their coverage. Octopus is first applied to mine and collect the reports data on CCEA from 2011 to 2019 by the two media. Two small corpora including the reports are then built. NVivo is next used to do the statistical analysis and clustering analysis of the keywords in two corpora as a whole and AntConc is finally utilized to do the statistics of high-frequency evaluative adjectives and nouns modified by evaluative adjectives as well as the concordance of the low-frequency words but closely relevant to corporate social responsibility (hereafter CSR) in two corpora, respectively. The results of the detailed analyses of the keywords are combined to unveil the CSR image of CCEA, which is followed by a discussion about the motives behind the coverage and finally some suggestions are put forward to improve the CSR image of CCEA. Theoretically, the present study promotes the interaction among data science, management, communications, and linguistics; practically it offers some advice to CCEA to elevate their CSR image.
机译：由于一些西方媒体对中国海外企业的业务活动存在负面认证，这对中国企业的形象甚至中国的国家形象产生了不利影响，旨在检测企业社会责任形象（以下，所述CSR图像）在非洲的中国建筑企业（以后CCEA）通过分析来自英国和华尔街日记（以下简称WSJ）的金融时报（以下，WSJ）并挖掘其覆盖后的动机。章鱼首先应用于我的矿山，从2011年到2019年通过两种媒体收集CCEA的报告数据。然后建立两个包括报告的小型公司。 NVivo接下来是为了完成两种Corpara的关键字的统计分析和聚类分析，整个，最终利用ANTCONC来做评价形容词修饰的高频评估形容词和名词的统计数据以及低的低频率 - 分别与企业社会责任（以下基层）密切相关的频繁词汇。关键字详细分析的结果组合以推出CCEA的CSR图像，然后讨论覆盖后面的动机，最后提出了一些建议，以改善CCEA的CSR图像。从理论上讲，本研究促进了数据科学，管理，通信和语言学之间的互动;实际上它为CCEA提供了一些建议，以提升其CSR图像。

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