## 6 Conclusions  
Taking Chinese A-share listed companies as the sample and selecting data from 2012 to 2021, this paper uses a web crawler to obtain the MD&A section in annual reports of listed companies and uses textual analysis technology to quantify this section. Based on the emotional dictionary, this paper calculates the tone of the MD&A and calculates the emotional tone of the financial news by using structured data from the database of the Chinese Research Data Services Platform. The emotional tone of MD&A and the emotional tone of financial news are internal and external texts for listed companies, respectively. This paper further combines them with traditional financial indicators for comparing the early warning effect of financial crises in listed companies, and finally draws the following four conclusions: (1) The introduction of the emotional tone indicators of MD&A text has no significant effect on the improvement of the early warning effect financial crisis in listed companies and even brings some noise, which has a negative influence on the prediction effect of some models. (2) The introduction of the emotional tone indicators of financial news text can improve the early warning effect of financial crises of listed companies. It can be seen that the external text contains incremental information and can objectively reflect the operation and the future development trend of listed companies. (3) The emotional tone indicator of financial news text is not easily influenced by listed companies. However, the emotional tone indicators of MD&A are easily modified and manipulated by the management of listed companies. Adopting the tone of financial news text can exclude the interference of some modification information to the research results, and then improve the accuracy of financial crisis early warning. (4) Under three different sets of input feature variables, DBGT, AdaBoost, random forest and Bagging models still maintain stable and accurate sample recognition ability. The above four models can be used as relatively optimal classifiers for financial crisis early warning for listed companies. There are still some limitations in this study, which are as follows: (1) There is no research on the early warning effect of linguistic features of texts other than emotional tone. (2) The establishment of a special comprehensive emotional dictionary of financial texts can be a research direction in the future. (3) There is a lack of research on other external texts, such as commentary texts, which represent the emotions of investors, and they may have an enhancing effect on the early warning effect of financial crises. Owing to low credibility and financial falsification, traditional financial indicators may fail to truly reflect the development of listed companies. This paper focuses on non-financial indicators such as textual data, to quantify the internal and external texts of companies, finding that the emotional tone of financial news texts has an enhancing effect on the early warning effect of models based on traditional financial indicators. This finding will provide a useful supplement to the methods of crisis prediction relying solely on traditional financial indicators. It will also bring important theoretical and practical implications for the financial risk identification of listed companies. Conceptualization, H.N., Z.Z. ; Methodology, H.N., Z.Z. ; Writing--original draft preparation, Z.Z., M.L., Z.H. ; Writing--review and editing, Z.Z., M.L., Z.H., H.N. All authors have read and agreed to the published version of the manuscript. This research was supported by Beijing Foreign Studies University Double First Class Major Landmark Project (No. 2022 SYLZD001) in China, the Fundamental Research Funds for the Central Universities (No. 2022 JX031) in China, and Beijing Foreign Studies University G20 Research Center Project (No. G20 ZX20223003) in China. Not applicable.