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

A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts