為改進過去語言分析模型無法辨認語言一字多義以及訓練域與預測域不一致之問題，本研究嘗試以BERT(Bidirectional Encoder Representations from Transformers)模型針對金融領域文本進行領域遷移(Domain Adaption)，比較有無經過遷移對模型效能之改進，接著以遷移過之模型分析RavenPack資料庫內所含的美國企業相關新聞，並以此建構信用評等變動預警模型。

本研究實證結果顯示，經過遷移之模型預測財金文本情緒的預測準確率比未經遷移之模型高出30.47%，且領域遷移後辨認的新聞情緒提升對未來企業信用評等變動的預測。另外，本研究建構四個隨機森林模型，用以證明企業金融財務面的媒體情緒隱含對企業未來評級可能變動的有效資訊。

To improve the inability of the language analysis model to recognize the polysemy of the language and the inconsistency between the training domain and the prediction domain, this study uses the BERT (Bidirectional Encoder Representations from Transformers) model to perform Domain Adaption for the financial corpus. The adaption improves the performance of the model, and we further use the adapted model to analyze the news related to US companies contained in the RavenPack database and construct an early warning model for credit rating changes.

The empirical results show that the prediction accuracy of the adapted model in predicting the sentiment of financial texts is 30.47% higher than that of the non-adapted one, which shows that adaption learning indeed improves the prediction of the corporate credit rating changes. Also, we developed four different random forest models to prove that the media sentiment on the company's financial news contains effective information on the possible changes in the company's future rating.

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