過去違約預警模型之相關研究大多以總體經濟變數、財務比率變數、市場變數等量化資訊進行分析，然而公開之新聞資訊中往往隱含許多有價值的訊息，因此，本研究嘗試爬取美國著名報社之新聞進行情感分析(sentiment analysis) ，剖析公開新聞資訊中與樣本公司相關之新聞內涵，建立基於情感傾向和新聞報導量的媒體情緒量化指標，並採用 logit 模型建構財務預警模型，將媒體情緒量化指標與過去文獻常用之指標進行效能驗證與比較，觀察公開新聞資訊是否能夠提升預測公司違約之準確率。
實證結果顯示，納入基於公開新聞資訊之媒體情緒量化指標，有助於提升違約預警模型之效能，也就是說，能夠提升模型預測違約發生之準確度;本研究亦進行兩種違約臨界值之比較，Begley, Ming, and Watts (1996) 建議之最適臨界值在模型之效能上，較一般文獻使用之 0.5 臨界值能夠明顯降低型一誤差，因此擁有較好的分類效果。

As most of the relevant studies of default prediction models are based on quantitative information such as financial ratio variables and market variables, public news information actually contains lots of valuable information. This paper uses Sentiment Analysis technology to extract the information related to sample companies from open news, quantifying the media sentiment indicator, and use Logit model and Probit model to conduct default prediction models. The open news is from The New York Times website and the US Newsstream database which contains Los Angeles Times, Chicago Tribune and The Wall Street Journal, I use VADER (Hutto, 2014) as the main tool of sentiment analysis, and create media sentiment indicator according to the amount of news and sentiment tendency.
The empirical results show that the media sentiment indicator does improve the effectiveness of the prediction of the corporate default probability. I also compare the effectiveness of the models with two different cut-off points. The optimal cut-off point suggested by Begley, Ming, and Watts (1996) create significantly lower Type I error than the general 0.5 cut-off point, so it has a better classification effect.

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