Selecting Characteristic Patterns of Text Contributions to Social Networks Using Instance-Based Learning Algorithm IBL-2
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43911358" target="_blank" >RIV/62156489:43110/17:43911358 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/00216224:14560/17:00108749
Výsledek na webu
<a href="https://ece.pefka.mendelu.cz/sites/default/files/imce/ECE2017_fin.pdf" target="_blank" >https://ece.pefka.mendelu.cz/sites/default/files/imce/ECE2017_fin.pdf</a>
DOI - Digital Object Identifier
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Alternativní jazyky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/GA16-26353S" target="_blank" >GA16-26353S: Sentiment a jeho vliv na akciové trhy</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Enterprise and Competitive Environment: Conference Proceedings
ISBN
978-80-7509-499-5
ISSN
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e-ISSN
neuvedeno
Počet stran výsledku
10
Strana od-do
971-980
Název nakladatele
Mendelova univerzita v Brně
Místo vydání
Brno
Místo konání akce
Brno
Datum konání akce
9. 3. 2017
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000427306200100