The Keywords in Corporate Social Responsibility: A Dictionary Construction Method Based on MNIR

Corporate social responsibility (CSR) and environmental, social, and governance (ESG) disclosures are critical for sustainable value creation. However, traditional evaluation methods struggle to quantify authentic performance and detect disclosure biases. In response, this study proposes an automated CSR polarity dictionary construction method that innovatively combines natural-language-processing technology and the multinomial inverse regression (MNIR) method. This method analyzes the correlations between corporate CSR reports and CSR ratings and constructs a dictionary that best reflects the CSR level of listed companies. We also used the CSR dictionary to construct a CSR disclosure level index for listed companies’ annual reports. This study reveals that CSR disclosure levels in annual reports expose manipulative disclosure practices and image management. However, this behavior has been proven to fail in generating excess returns for the company in the stock market. This phenomenon provides novel insights into corporate stock market performance management. In addition, the CSR disclosure level index is shown to effectively reflect the CSR level of enterprises in different industries and provides a theoretical reference for the social responsibility management of companies with different pollution levels. These findings facilitate efficient information release and strengthen ESG assessment frameworks through data-driven standardization.