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

Result code in 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>

Alternative codes found

RIV/00216224:14560/17:00108749

Result on the web

<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

—

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

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA16-26353S" target="_blank" >GA16-26353S: Sentiment and its impact on stock markets</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

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

Article name in the collection

Enterprise and Competitive Environment: Conference Proceedings

ISBN

978-80-7509-499-5

ISSN

—

e-ISSN

neuvedeno

Number of pages

10

Pages from-to

971-980

Publisher name

Mendelova univerzita v Brně

Place of publication

Brno

Event location

Brno

Event date

Mar 9, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000427306200100