Towards a polynomial approximation of support vector machine accuracy applied to Arabic tweet sentiment analysis

Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text. Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when t...

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Published inMathematical Modeling and Computing Vol. 10; no. 2; pp. 511 - 517
Main Authors Banou, Z., Elfilali, S., Benlahmar, H.
Format Journal Article
LanguageEnglish
Published 2023
Online AccessGet full text
ISSN2312-9794
2415-3788
DOI10.23939/mmc2023.02.511

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Abstract Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text. Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score. However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming. In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange polynomial.
AbstractList Machine learning algorithms have become very frequently used in natural language processing, notably sentiment analysis, which helps determine the general feeling carried within a text. Among these algorithms, Support Vector Machines have proven powerful classifiers especially in such a task, when their performance is assessed through accuracy score and f1-score. However, they remain slow in terms of training, thus making exhaustive grid-search experimentations very time-consuming. In this paper, we present an observed pattern in SVM's accuracy, and f1-score approximated with a Lagrange polynomial.
Author Banou, Z.
Elfilali, S.
Benlahmar, H.
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