Comparison of Naive Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application

Comparison of Naive Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application

• By Ernawati, Siti; Wati, Risa; Nuris, Nuzuliarini; Marita, Sari , Lita; Yulia, Rini , Eka;
• Seminar Internasional Bereputasi
• 589
• 1-6

The problem examined in this study is about the user’s trust in using digital learning applications that are downloaded on playstore. Many reviews are given by the public
about the application that has been downloaded on playstore. This review is very influential on their trust in using the application. The purpose of this study is to classify data according to labels and find out the best choice between the  classification method and the proposed selection feature as a consideration in determining the use of digital learning applications.This study compares the classification method, the Na¨ıve Bayes algorithm and the genetic algorithm
(GA) as feature selection with the Na¨ıve Bayes algorithm classification method and the particle swarm optimization (PSO) as feature selection to categorize the reviews in the playstore. The experimental results show that the Na¨ıve Bayes algorithm and PSO as feature selection is the best model between the two models proposed in this study. Reviews can be classified into positive and negative labels well. The accuracy is 98.00%. The results of the classification are expected to help in making decisions when going to use digital learning application.

• Katakunci
• URL http://
• Penerbit ,

REFERENSI

[1] Koncz P and Paralic J, 2011 An approach to feature selection for sentiment analysis 2011 15th IEEE Int. Conf. Intell. Eng. Syst. p. 357{362.
[2] Zhang W and Gao F, 2011 An Improvement to Naive Bayes for Text Classification Elsevier 15 p. 2160{2164.
[3] Vijayarani S Ilamathi M J and Nithya M, 2015 Preprocessing Techniques for Text Mining - An Overview Int. J. Comput. Sci. Commun. Networks 5, 1 p. 7{16.
[4] Ernawati S Yulia E Frieyadie and Samudi, 2018 Implementation of The Na¨ıve Bayes Algorithm with Feature Selection using Genetic Algorithm for Sentiment Review Analysis of Fashion Online Companies 2018 6th Int. Conf. Cyber IT Serv. Manag. Citsm p. 1{5.

[5] Ghareb A S Bakar A A and Hamdan A R, 2015 Hybrid Feature Selection Based On Enhanced Genetic Algorithm For Text Categorization Expert Syst. Appl. 49 p. 31{47.
[6] Syarif I, 2016 Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization Emit. Int. J. Eng. Technol. 4, 2 p. 277{290.
[7] Ahmed B Cha S and Tappert C, 2004 Language Identification from Text Using N-gram Based Cumulative Frequency Addition Proc. Student/Faculty Res. Day, CSIS, Pace Univ. p. 12.1-12.8.
[8] Liu Y Wang G Chen H Dong H Zhu X and Wang S, 2011 An Improved Particle Swarm Optimization for Feature Selection J. Bionic Eng. 8, 2 p. 191{200.
[9] Shi Y and Eberhart R C, 1945 Empirical Study of Particle Swarm Optimization IEEE p. 1945{1950.
[10] G¨unal S, 2012 Hybrid feature selection for text classification Turkish J. Electr. Eng. Comput. Sci. 20, SUPPL.2 p. 1296{1311.
[11] Yonghe L Minghui L Zeyuan Y and Lichao C, 2015 Improved particle swarm optimization algorithm and its application in text feature selection Appl. Soft Comput. J. 35 p. 629{636.