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

TL;DR

This paper presents a BERT-based method for sentiment analysis and key entity detection in online financial texts, focusing on negative sentiments and employing ensemble learning to enhance accuracy.

Contribution

It introduces a novel BERT-based approach for joint sentiment analysis and key entity detection tailored for financial texts, outperforming traditional models.

Findings

Higher performance than SVM, LR, NBM, and BERT on financial datasets.

Effective detection of negative sentiment and key entities in social media texts.

Improved accuracy through ensemble learning techniques.

Abstract

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.