Ensembles of Classifiers for Parallel Categorization of Large Number of Text Documents Expressing Opinions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F17%3A43911118" target="_blank" >RIV/62156489:43110/17:43911118 - isvavai.cz</a>

Result on the web

<a href="http://cesmaa.eu/journals/jaes/files/JAES%20Spring%20XII%201(47)_2017_online.pdf" target="_blank" >http://cesmaa.eu/journals/jaes/files/JAES%20Spring%20XII%201(47)_2017_online.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Ensembles of Classifiers for Parallel Categorization of Large Number of Text Documents Expressing Opinions

Original language description

Opinions provided by people that used some services or purchased some goods are a rich source of knowledge. The opinion classification, applying mostly supervised classifiers, is one of the essential tasks. Computer&apos;s technological capabilities are still a major obstacle, especially when processing huge volumes of data. This study proposes and evaluates experimentally a parallelism application to the classification of a very large number of contrary opinions expressed as freely written text reviews. Instead of training a single classifier on the entire data set, an ensemble of classifiers is trained on disjunctive subsets of data and a group decision is used for the classification of unlabelled items. The main assessment criteria are computational efficiency and error rates, combined into a single measure to be able to compare ensembles of different sizes. Support vector machines, artificial neural networks, and decision trees, belonging to frequently used classification methods, were examined. The paper demonstrates the suggested method viability when the number of text reviews leads to computational complexity, which is beyond the contemporary common PC&apos;s capabilities. Classification accuracy and the values of other classification performance measures (Precision, Recall, F-measure) did not decrease, which is a positive finding.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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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

Name of the periodical

Journal of Applied Economic Sciences

ISSN

1843-6110

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

RO - ROMANIA

Number of pages

11

Pages from-to

25-35

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85018818886