Automatic recognition of adverse events in news: A text mining approach

Xuan Zhang, Mi Zhou, Weiguo Fan, G. Alan Wang

Research output: Contribution to conferencePaperpeer-review

Abstract

Corporate adverse events are important resources for investors to analyze business stability and predict future performance. However, adverse events are often scattered across different news media and difficult to recognize. In this paper, we introduce a classification framework to recognize adverse events. A novel under-sampling method based on majority instances clustering is also proposed to deal with the imbalanced data issue. The framework and the under-sampling method are tested using a sample of manually labelled news articles collected for S&P 500 companies. Our experimental results show that both the framework and the under-sampling method are effective in classifying the imbalanced data, and produce better performance than three baseline methods. The proposed framework can be conveniently applied to other text classification areas as well.

Original languageEnglish (US)
StatePublished - Jan 1 2014
Externally publishedYes
Event24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014 - Auckland, New Zealand
Duration: Dec 17 2014Dec 19 2014

Conference

Conference24th Annual Workshop on Information Technologies and Systems: Value Creation from Innovative Technologies, WITS 2014
CountryNew Zealand
CityAuckland
Period12/17/1412/19/14

Keywords

  • Adverse events
  • Classification
  • Imbalanced data
  • Text mining
  • Under-sampling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Computer Science Applications

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