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Article analysis:

The article is generally reliable and trustworthy in its claims and evidence presented. The authors provide a comprehensive overview of the current state of corporate financial distress prediction models and how introducing textual features into these models can improve their accuracy. The authors also provide empirical evidence from listed companies in China to support their claims, which adds credibility to their argument.

However, there are some points that could be improved upon in terms of trustworthiness and reliability. For example, the authors do not explore any counterarguments or present any opposing views on their claims. Additionally, they do not discuss any potential risks associated with using textual features for predicting corporate financial distress or note any possible biases in their data or methodology. Furthermore, they do not provide any evidence for the claim that deep belief networks have potential to perform better predictions than other methods such as logistic regression or BP neural networks.

In conclusion, while this article is generally reliable and trustworthy in its claims and evidence presented, it could benefit from further exploration of counterarguments and potential risks associated with using textual features for predicting corporate financial distress as well as providing more evidence for its claims regarding deep belief networks’ potential for better predictions than other methods.