IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-030-43412-0_12.html
   My bibliography  Save this book chapter

Obtaining More Specific Topics and Detecting Weak Signals by Topic Word Selection

In: Reliability and Statistical Computing

Author

Listed:
  • Laura Kölbl

    (Friedrich-Alexander-Universität Erlangen-Nürnberg)

  • Michael Grottke

    (Friedrich-Alexander-Universität Erlangen-Nürnberg
    GfK SE, Global Data Science)

Abstract

With topic modeling methods, such as Latent Dirichlet Allocation (LDA), we can find topics in large text collections. To efficiently employ this information, there is a need for a method that automatically analyzes the topics with respect to their usefulness for applications like the detection of new innovations. This paper presents a novel method to automatically evaluate topics produced by LDA. The new approach puts the focus on finding topics with topic words that are not only coherent, but also specific. By using the documents associated with each word to calculate background topics, a baseline can be set for each topic word that helps assess whether its context fits the topic well. Experiments indicate that the resulting topics are more manageable in terms of their interpretability. Moreover, we show that the approach can be used to detect weak signals.

Suggested Citation

  • Laura Kölbl & Michael Grottke, 2020. "Obtaining More Specific Topics and Detecting Weak Signals by Topic Word Selection," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Reliability and Statistical Computing, pages 193-206, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-030-43412-0_12
    DOI: 10.1007/978-3-030-43412-0_12
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    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:spr:ssrchp:978-3-030-43412-0_12. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.