SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION

Jurnal Riset Informatika

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Field Value
 
Title SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION
 
Creator Alfianti, Zulia Imami
Gunawan, Deni
Amin, Ahmad Fikri
 
Description Sentiment analysis is an area of ​​approach that solves problems by using reviews from various relevant scientific perspectives. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. The number of reviews is certainly very much making consumers reluctant to read reviews. Eventually, the reviews become useless. For this reason, the authors classify based on positive and negative classes, so consumers can find product comparisons quickly and precisely. The implementation of Particle Swarm Optimization (PSO) optimization can improve the accuracy of the Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm can improve accuracy and provide solutions to the review classification problem to be more accurate and optimal. Comparison of accuracy resulting from testing this data is an SVM algorithm of 89.20% and AUC of 0.973, then compared to SVM based on PSO with an accuracy of 94.60% and AUC of 0.985. The results of testing the data for the NB algorithm are 88.50% accuracy and AUC is 0.536, then the accuracy is compared with the PSO based NB for 0.692. In these calculations prove that the application of PSO optimization can improve accuracy and provide more accurate and optimal solutions
 
Publisher Kresnamedia Publisher
 
Date 2020-06-22
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ejournal.kresnamediapublisher.com/index.php/jri/article/view/149
10.34288/jri.v2i3.149
 
Source Jurnal Riset Informatika; Vol 2 No 3 (2020): Period of June 2020; 169-178
2656-1735
2656-1743
10.34288/jri.v2i3
 
Language eng
 
Relation http://ejournal.kresnamediapublisher.com/index.php/jri/article/view/149/57
 
Rights Copyright (c) 2020 Zulia Imami Alfianti, Deni Gunawan, Ahmad Fikri Amin
http://creativecommons.org/licenses/by-nc/4.0
 

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