• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Data Science and Advanced Analytics >Towards Bankruptcy Prediction: Deep Sentiment Mining to Detect Financial Distress from Business Management Reports
【24h】

Towards Bankruptcy Prediction: Deep Sentiment Mining to Detect Financial Distress from Business Management Reports

机译：破产预测：深度情绪挖掘，从企业管理报告中检测财务困境

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Due to their disclosure required by law, business management reports have become publicly available for a large number of companies, and these reports offer the opportunity to assess the financial health or distress of a company, both quantitatively from the balance sheets and qualitatively from the text. In this paper, we analyze the potential of deep sentiment mining from the textual parts of business management reports and aim to detect signals for financial distress. We (1) created the largest corpus of business reports analyzed qualitatively to date, (2) defined a non-trivial target variable based on the so-called Altman Z-score, (3) developed a filtering of sentences based on class-correlated pattern mining to reduce the complexity of these long and complex texts, and (4) employed one of the best-performing machine learning methods for this type of task, Dependency Sensitive Convolutional Neural Networks (DSCNNs). Experimental results show that strong prediction performance can be achieved by a suitable bundle of methods, with an F1-score of more than 0.86 and a Kappa score of more than 65%. To better understand the parts of management reports that indicate financial distress, the prediction engine is complemented by a visualization tool that highlights critical text passages.
机译：由于法律规定的披露，企业管理报告已经成为公开提供了大量的公司，而这些报告提供了机会，来评估一个公司的财务状况或痛苦，无论是数量上，从资产负债表和定性从文本。在本文中，我们从企业管理报告文字部分分析深层感悟挖掘的潜力，旨在检测为金融求救信号。我们（1）创建的业务报告最大的语料库定性分析到今天为止，（2）定义的基础上，所谓的Z-Score模型一个不平凡的目标变量，（3）开发句子的过滤基于类的相关模式挖掘，以减少这些复杂的文本的复杂性，以及（4）对于这种类型的任务，相关敏感卷积神经网络（DSCNNs）的表现最好的机器学习方法中采用的一种。实验结果表明，强预测性能可以通过方法合适的束来实现，具有大于0.86的F1-分数和卡伯得分的大于65 ％。为了更好地理解的管理报告，指示财务困境的部分，预测发动机通过可视化工具，亮点至关重要的文字段落补充。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号