Using sentiment analysis to study the relationship between subjective expression in financial reports and company performance.

In recent years, with the development and progress of text information research, the disclosure of non-financial and qualitative information has often be found to have an incremental function. Financial reports, including financial statements and other relevant information, provide important insights on an enterprise's financial status, operating results, and cash flow. Faced with a large number of financial reports, readers often do not know where to start, and as financial statements are prepared based on past transactions, they cannot fully reflect the past, present, and future economic conditions of the company. Information asymmetry and uncertainty make the text mining of financial reports of great significance to enterprise stakeholders. Accordingly, this paper takes financial reports as the research object and builds a research framework on the relationship between subjective expression in financial reports and company performance. Through natural language processing, sentiment analysis, and other text-mining technologies, the paper quantifies the subjective expression in financial reports and introduces intermediaries. Variables, moderating variables, and control variables are used to construct a multiple regression model. The empirical results show that the underlying emotional tendencies in subjective expressions substantially impact on the future development of listed companies. This paper enriches understanding of the multi-dimensional relationship between financial report text and company performance, and provides ideas for further exploring this relationship. It is of great practical significance to help them make rational decisions and ensure the normal operation of the company and the preservation and appreciation of capital.

Analysis of Enterprise Social Responsibility to Employee Psychological Satisfaction Based on Discriminant Least Square Regression.

Employee psychological satisfaction is the satisfaction of perception of environmental factors at the psychological and physiological levels, that is, the employees' subjective response to the work situation. How to enhance employee loyalty and psychological satisfaction has always been a hot issue in theoretical and practical research. With the development of artificial intelligence (AI), many AI methods are widely used to find important factors which have significant influences on the psychological satisfaction of employees. Feature selection methods as one kind of AI models can select discriminant features which have high correlation with the outcome. In this study, we first construct 19 factors from enterprise social responsibility. Then we use a discriminant least square regression model to select most relative factors associating with employee psychological satisfaction. Our experimental results show that the psychological satisfaction of employees is very related to salary, security, welfare, occupational health, and fairness. In addition, we find that discriminant least square regression performs better than the comparison feature selection methods we select, and the selected factors are more in line with our perceptions and expectations.