Mining Economic Sentiment Using Argumentation Structures

  • Alexander Hogenboom
  • Frederik Hogenboom
  • Uzay Kaymak
  • Paul Wouters
  • Franciska de Jong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6413)


The recent turmoil in the financial markets has demonstrated the growing need for automated information monitoring tools that can help to identify the issues and patterns that matter and that can track and predict emerging events in business and economic processes. One of the techniques that can address this need is sentiment mining. Existing approaches enable the analysis of a large number of text documents, mainly based on their statistical properties and possibly combined with numeric data. Most approaches are limited to simple word counts and largely ignore semantic and structural aspects of content. Yet, argumentation plays an important role in expressing and promoting an opinion. Therefore, we propose a framework that allows the incorporation of information on argumentation structure in the models for economic sentiment discovery in text.


Text Mining Sentiment Analysis Argumentation Structure Text Segment Argumentation Ontology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Hogenboom
    • 1
  • Frederik Hogenboom
    • 1
  • Uzay Kaymak
    • 1
  • Paul Wouters
    • 1
  • Franciska de Jong
    • 1
  1. 1.Erasmus University RotterdamRotterdamThe Netherlands

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