## 1 Introduction  
The financial situation of listed companies has attracted the attention of government departments, shareholders, business operators, creditors and other stakeholders in pace with the development of the capital market. Academics have also been committed to exploring effective financial crisis recognition indicators and constructing a more accurate financial crisis warning model to improve the predictive capability of listed companies' financial crises. In terms of the selection of crisis recognition indicators, most scholars focus more on standardized financial data and less on non-standardized textual information. Textual information, as a newer data element, contains richer emotions. Thus, the sentiment analysis of textual information turns out to be an effective supplement to financial indicators. This paper quantifies the sentiment and tone of MD&A sections in the annual financial reports of listed companies and financial news texts, combining them with traditional financial indicators, respectively, to form new input feature variables. Furthermore, it constructs different financial crisis early-warning models based on thirteen representative machine learning methods. The study puts textual information and traditional financial indicators together for financial crisis identification, which has a significant positive effect on the sustainable growth of Chinese listed companies and the capital market. Based on the emotional tone of texts and machine learning models, this paper mainly focuses on four questions as follows:  
RQ1: Whether the combination of the emotional tone of MD&A texts and traditional financial indicators can improve the identification of financial crises of listed companies. RQ2: Whether the combination of the emotional tone of financial news texts and traditional financial indicators can improve the identification of financial crises of listed companies. RQ3: A comparative study of the effect of the emotional tone of internal texts (MD&A) and external texts (financial news) on early warning of financial crises in listed companies. RQ4: A comparative study of the early warning effects of thirteen machine learning models. Based on the above research questions, this paper selects 1082 Chinese A-share (RMB-denominated common shares) listed companies from 2012 to 2021 as the sample. This paper takes the year of the sample as T and selects the traditional financial indicators in T-3 years as the benchmark. This study compares the financial crisis early warning effect incorporated with the emotional tone of MD&A texts and financial news texts, respectively, and then compares the recognition performance of thirteen machine learning models. Finally, in this paper, we find the emotional tone of the text that can improve the financial crisis recognition performance of listed companies, along with the financial crisis early warning model with greater accuracy. In summary, the contributions of this paper are mainly: (1) This paper quantifies textual information and uses the information as a new prediction indicator to measure the financial crisis of listed companies, which expands the choice of financial crisis prediction indicators. (2) This paper covers universal machine learning models. By comparing the effects of different models based on different combinations of prediction indicators, this paper finds models with better and more stable early warning effects, which provide references for model users. (3) This paper investigates the effectiveness of the emotional tone indicator of financial news texts on the early warning models for financial crises and finds that this indicator helps enhance the accuracy of these early-warning financial crisis models for listed companies. (4) This study expands on the differences between the emotional tone indicator of MD&A and that of financial news in enhancing financial crisis warning capability, from the perspective of the internal and external texts of companies.