IDEAS home Printed from
MyIDEAS: Log in (now much improved!) to save this article

Non parametric statistical models for on-line text classification

Listed author(s):
  • Paola Cerchiello


  • Paolo Giudici


Social media, such as blogs and on-line forums, contain a huge amount of information that is typically unorganized and fragmented. An important issue, that has been raising importance so far, is to classify on-line texts in order to detect possible anomalies. For example on-line texts representing consumer opinions can be, not only very precious and profitable for companies, but can also represent a serious damage if they are negative or faked. In this contribution we present a novel statistical methodology rooted in the context of classical text classification, in order to address such issues. In the literature, several classifiers have been proposed, among them support vector machine and naive Bayes classifiers. These approaches are not effective when coping with the problem of classifying texts belonging to an unknown author. To this aim, we propose to employ a new method, based on the combination of classification trees with non parametric approaches, such as Kruskal–Wallis and Brunner–Dette–Munk test. The main application of what we propose is the capability to classify an author as a new one, that is potentially trustable, or as an old one, that is potentially faked. Copyright Springer-Verlag Berlin Heidelberg 2012

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: Access to full text is restricted to subscribers.

As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

Article provided by Springer & German Classification Society - Gesellschaft für Klassifikation (GfKl) & Japanese Classification Society (JCS) & Classification and Data Analysis Group of the Italian Statistical Society (CLADAG) & International Federation of Classification Societies (IFCS) in its journal Advances in Data Analysis and Classification.

Volume (Year): 6 (2012)
Issue (Month): 4 (December)
Pages: 277-288

in new window

Handle: RePEc:spr:advdac:v:6:y:2012:i:4:p:277-288
DOI: 10.1007/s11634-012-0122-2
Contact details of provider: Web page:

Web page:

Web page:

Web page:

Web page:

Order Information: Web:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

in new window

  1. Hoai Le Thi & Hoai Le & Van Nguyen & Tao Pham Dinh, 2008. "A DC programming approach for feature selection in support vector machines learning," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 2(3), pages 259-278, December.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:spr:advdac:v:6:y:2012:i:4:p:277-288. 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: (Sonal Shukla)

or (Rebekah McClure)

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.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.