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Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for c...

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Main Authors: Nurcahyawati, Vivine, Mustaffa, Zuriani
Format: Article
Language:English
Published: IAES 2023
Subjects:
QA76 Computer software
T Technology (General)
Online Access:http://umpir.ump.edu.my/id/eprint/36817/1/Improving%20sentiment%20reviews%20classification%20performance.pdf
http://umpir.ump.edu.my/id/eprint/36817/
https://doi.org/10.11591/eei.v12i3.4830
https://doi.org/10.11591/eei.v12i3.4830
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spelling my.ump.umpir.368172023-01-26T00:33:25Z http://umpir.ump.edu.my/id/eprint/36817/ Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm Nurcahyawati, Vivine Mustaffa, Zuriani QA76 Computer software T Technology (General) High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for classification. Various challenges were encountered, including how to determine the optimal combination of pre-processing techniques, how to clean the dataset, and determine the best classification algorithm. This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. The proposed model was realized using 3 different datasets, namely Amazon product review, movie review, and airline review from Twitter. This study provides better findings than the previous results. Improved performance is generated by SVM combined with FM, resulting in 96% accuracy. So that the SVM-FM combination can be said to be the best combination for sentiment analysis on the given data set. IAES 2023 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/36817/1/Improving%20sentiment%20reviews%20classification%20performance.pdf Nurcahyawati, Vivine and Mustaffa, Zuriani (2023) Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm. Bulletin of Electrical Engineering and Informatics, 12 (3). pp. 1817-1824. ISSN 2302-9285 https://doi.org/10.11591/eei.v12i3.4830 https://doi.org/10.11591/eei.v12i3.4830
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA76 Computer software
T Technology (General)
spellingShingle QA76 Computer software
T Technology (General)
Nurcahyawati, Vivine
Mustaffa, Zuriani
Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
description High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for classification. Various challenges were encountered, including how to determine the optimal combination of pre-processing techniques, how to clean the dataset, and determine the best classification algorithm. This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. The proposed model was realized using 3 different datasets, namely Amazon product review, movie review, and airline review from Twitter. This study provides better findings than the previous results. Improved performance is generated by SVM combined with FM, resulting in 96% accuracy. So that the SVM-FM combination can be said to be the best combination for sentiment analysis on the given data set.
format Article
author Nurcahyawati, Vivine
Mustaffa, Zuriani
author_facet Nurcahyawati, Vivine
Mustaffa, Zuriani
author_sort Nurcahyawati, Vivine
title Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
title_short Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
title_full Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
title_fullStr Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
title_full_unstemmed Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
title_sort improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm
publisher IAES
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/36817/1/Improving%20sentiment%20reviews%20classification%20performance.pdf
http://umpir.ump.edu.my/id/eprint/36817/
https://doi.org/10.11591/eei.v12i3.4830
https://doi.org/10.11591/eei.v12i3.4830
_version_ 1756060205920026624
score 13.211869

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