论文标题
基于BERT的情感分析和在线财务文本的关键实体检测方法
A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts
论文作者
论文摘要
互联网的出现和快速进步对金融领域产生了不断增加的影响。如何快速，准确地从大规模的负财务经文中挖掘关键信息已成为投资者和决策者的关键问题之一。针对这个问题，我们提出了基于BERT的情感分析和关键实体检测方法，该方法在社交媒体中的在线财务文本挖掘和公众舆论分析中应用。通过使用预训练模型，我们首先研究情感分析，然后我们将关键实体检测视为不同粒度的句子匹配或机器阅读理解（MRC）任务。其中，我们主要关注负面情感信息。我们通过使用我们的方法来检测特定实体，这与传统命名实体识别（NER）不同。此外，我们还使用合奏学习来提高拟议方法的性能。实验结果表明，我们的方法的性能通常高于SVM，LR，NBM和BERT，用于两个财务情感分析和关键实体检测数据集。
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.
