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

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 Rakers. 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.

I. Introduction

With the rapid development of the Internet, great changes have taken place in financial domain. The development of Internet finance has brought subversion to the traditional finance, and also changed people's work and life style. Compared with traditional finance, Internet finance has the characteristics of low cost, high efficiency, wide coverage and high risk. The emerging social media, represented by Micro-blog, provides relevant financial information. Sentiment analysis and key entities of online financial texts help to understand the public's sentiment state, timely access to public opinions and attitudes, and rapidly get to the subject of information. Thus it is of great practical significance for risk control, public opinion analysis and government regulation [1]