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Non parametric statistical models for on-line text classification

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  • Paola Cerchiello

    ()

  • Paolo Giudici

    ()

Abstract

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

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File URL: http://hdl.handle.net/10.1007/s11634-012-0122-2
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Bibliographic Info

Article provided by Springer in its journal Advances in Data Analysis and Classification.

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

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Handle: RePEc:spr:advdac:v:6:y:2012:i:4:p:277-288

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Web page: http://www.springer.com/statistics/statistical+theory+and+methods/journal/11634

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Related research

Keywords: Non parametric statistical models; Kruskal–Wallis test; Brunner–Dette–Munk test; Text analysis; Opinion spam detection; 62G10; 62H30;

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  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, vol. 2(3), pages 259-278, December.
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