  The emergence and rapid progress of the Internet have brought ever-increasing
impact on financial domain. How to rapidly and accurately mine the key
information from the massive negative financial texts has become one of the key
issues for investors and decision makers. Aiming at the issue, we propose a
sentiment analysis and key entity detection approach based on BERT, which is
applied in online financial text mining and public opinion analysis in social
media. By using pre-train model, we first study sentiment analysis, and then we
consider key entity detection as a sentence matching or Machine Reading
Comprehension (MRC) task in different granularity. Among them, we mainly focus
on negative sentimental information. We detect the specific entity by using our
approach, which is different from traditional Named Entity Recognition (NER).
In addition, we also use ensemble learning to improve the performance of
proposed approach. Experimental results show that the performance of our
approach is generally higher than SVM, LR, NBM, and BERT for two financial
sentiment analysis and key entity detection datasets.
