IDEAS home Printed from
   My bibliography  Save this paper

Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts


  • Pekka Malo
  • Ankur Sinha
  • Pyry Takala
  • Pekka Korhonen
  • Jyrki Wallenius


The use of robo-readers to analyze news texts is an emerging technology trend in computational finance. In recent research, a substantial effort has been invested to develop sophisticated financial polarity-lexicons that can be used to investigate how financial sentiments relate to future company performance. However, based on experience from other fields, where sentiment analysis is commonly applied, it is well-known that the overall semantic orientation of a sentence may differ from the prior polarity of individual words. The objective of this article is to investigate how semantic orientations can be better detected in financial and economic news by accommodating the overall phrase-structure information and domain-specific use of language. Our three main contributions are: (1) establishment of a human-annotated finance phrase-bank, which can be used as benchmark for training and evaluating alternative models; (2) presentation of a technique to enhance financial lexicons with attributes that help to identify expected direction of events that affect overall sentiment; (3) development of a linearized phrase-structure model for detecting contextual semantic orientations in financial and economic news texts. The relevance of the newly added lexicon features and the benefit of using the proposed learning-algorithm are demonstrated in a comparative study against previously used general sentiment models as well as the popular word frequency models used in recent financial studies. The proposed framework is parsimonious and avoids the explosion in feature-space caused by the use of conventional n-gram features.

Suggested Citation

  • Pekka Malo & Ankur Sinha & Pyry Takala & Pekka Korhonen & Jyrki Wallenius, 2013. "Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts," Papers 1307.5336,, revised Jul 2013.
  • Handle: RePEc:arx:papers:1307.5336

    Download full text from publisher

    File URL:
    File Function: Latest version
    Download Restriction: no

    Other versions of this item:


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Paola Cerchiello & Giancarlo Nicola, 2017. "Assessing News Contagion in Finance," DEM Working Papers Series 139, University of Pavia, Department of Economics and Management.
    2. Paola Cerchiello & Giancarlo Nicola & Samuel Rönnqvist & Peter Sarlin, 2017. "Deep Learning Bank Distress from News and Numerical Financial Data," DEM Working Papers Series 140, University of Pavia, Department of Economics and Management.
    3. Samuel Ronnqvist & Peter Sarlin, 2016. "Bank distress in the news: Describing events through deep learning," Papers 1603.05670,, revised Dec 2016.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1307.5336. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (arXiv administrators). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.