Open Access
Lecture Notes in Computer Science, pages 242-253
Selecting an Optimal Feature Set for Stance Detection
Publication type: Book Chapter
Publication date: 2019-12-14
Journal:
Lecture Notes in Computer Science
Quartile SCImago
Q3
Quartile WOS
—
Impact factor: —
ISSN: 03029743, 16113349, 18612075, 18612083
Abstract
Stance detection is an automatic recognition of author’s view point in relation to a given object. An important stage of the solution process is determining the most appropriate way to represent texts. The paper proposes a new method of selecting an optimal feature set. The method is based on a homogenous ensemble of feature selection methods and a procedure of determining the optimal number of features. In this procedure the dependence of task performance on the number of features is approximated and the optimal number of features is determined by analyzing the growth rate of the function. There have been conducted experiments with text corpora consisting of “for” and “against” stances towards vaccinations of children, the Unified State Examination at school, and human cloning. The results demonstrate that the proposed method allows to achieve better performance in comparison with individual methods and even an overall feature set with a considerably fewer number of features.
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IEEE Access
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IEEE Access
1 publication, 50%
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Neural Computing and Applications
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Neural Computing and Applications
1 publication, 50%
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Citations by publishers
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IEEE
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IEEE
1 publication, 50%
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Springer Nature
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Springer Nature
1 publication, 50%
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Vychegzhanin S. et al. Selecting an Optimal Feature Set for Stance Detection // Lecture Notes in Computer Science. 2019. pp. 242-253.
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Vychegzhanin S., Razova E., Kotelnikov E., Milov V. Selecting an Optimal Feature Set for Stance Detection // Lecture Notes in Computer Science. 2019. pp. 242-253.
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TY - GENERIC
DO - 10.1007/978-3-030-37334-4_22
UR - https://doi.org/10.1007%2F978-3-030-37334-4_22
TI - Selecting an Optimal Feature Set for Stance Detection
T2 - Lecture Notes in Computer Science
AU - Vychegzhanin, Sergey
AU - Razova, Elena
AU - Kotelnikov, Evgeny
AU - Milov, Vladimir
PY - 2019
DA - 2019/12/14 00:00:00
PB - Springer Nature
SP - 242-253
SN - 0302-9743
SN - 1611-3349
SN - 1861-2075
SN - 1861-2083
ER -
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@incollection{2019_Vychegzhanin,
author = {Sergey Vychegzhanin and Elena Razova and Evgeny Kotelnikov and Vladimir Milov},
title = {Selecting an Optimal Feature Set for Stance Detection},
publisher = {Springer Nature},
year = {2019},
pages = {242--253},
month = {dec}
}