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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/62156489:43110/17:43911358

Výsledek na webu

<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

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

ENTERPRISE AND COMPETITIVE ENVIRONMENT

ISBN

9788075094995

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

971-980

Název nakladatele

Mendel University in Brno, Faculty of Business and Economics

Místo vydání

Brno

Místo konání akce

Mendel University in Brno

Datum konání akce

9. 3. 2017

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000427306200100