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Selecting Characteristic Patterns of Text Contributions to Social Networks Using Instance-Based Learning Algorithm IBL-2

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F17%3A00108749" target="_blank" >RIV/00216224:14560/17:00108749 - isvavai.cz</a>

  • Alternative codes found

    RIV/62156489:43110/17:43911358

  • Result on the web

    <a href="https://ece.pefka.mendelu.cz/sites/default/files/imce/ece_2015_final.pdf" target="_blank" >https://ece.pefka.mendelu.cz/sites/default/files/imce/ece_2015_final.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Selecting Characteristic Patterns of Text Contributions to Social Networks Using Instance-Based Learning Algorithm IBL-2

  • Original language description

    The presented research focuses on selecting typical patterns of textual entries written using a natural language (English) in a social network booking.com, which publishes sentiment of customers that used an accommodation service. This work deals with the possibility to find the patterns via text mining based on a machine-learning tool known as Instance-Based Learning (IBL). To reduce high computational demands of the basic algorithm IBL-1 (k-nearest neighbors), IBL-2 does not store sample candidates the function of which is successfully carried out by the already stored samples. The textual data are represented as bag-of-words with sparse vectors. Because the non-linearly increasing computational complexity depends on the number of samples as well as on their vocabulary, the potential candidates are firstly freed of common insignificant terms and then the vector sparsity is strongly decreased by removing words having a low frequency in relation to the number of samples. Then, IBL-2 rejects to store samples that duplicate the functionality of the already stored ones. As a result, the database contains only (or mainly) significant samples that represent characteristic patterns, which may be used for classification or another type of a following social network analysis.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10102 - Applied mathematics

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2017

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    ENTERPRISE AND COMPETITIVE ENVIRONMENT

  • ISBN

    9788075094995

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    971-980

  • Publisher name

    Mendel University in Brno, Faculty of Business and Economics

  • Place of publication

    Brno

  • Event location

    Mendel University in Brno

  • Event date

    Mar 9, 2017

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article

    000427306200100