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.

中文翻译：

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基于 BERT 的在线金融文本情感分析和关键实体检测方法

互联网的出现和飞速发展，给金融领域带来了越来越大的影响。如何快速准确地从海量负面财经文本中挖掘关键信息，成为投资者和决策者关注的关键问题之一。针对这个问题，我们提出了一种基于BERT的情感分析和关键实体检测方法，应用于在线金融文本挖掘和社交媒体舆情分析。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。我们使用我们的方法检测特定实体，这与传统的命名实体识别（NER）不同。此外，我们还使用集成学习来提高所提出方法的性能。实验结果表明，对于两个金融情绪分析和关键实体检测数据集，我们的方法的性能普遍高于 SVM、LR、NBM 和 BERT。