This study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%.


    Access

    Download


    Export, share and cite



    Title :

    Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm


    Contributors:

    Publication date :

    2021-10-30


    Remarks:

    International Journal of Advances in Data and Information Systems; Vol. 2 No. 2 (2021): International Journal of Advances in Data and Information Systems; 96-104 ; 2721-3056


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629




    New Feature Selection Process to Enhance Naïve Bayes Classification

    Tejaswini, Oruganti / Aswath, Shakthi.K.P / Geethika, Khadavilli Ramya et al. | IEEE | 2018


    AIR COMBAT WITH PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM

    Egemen Berki Çimen | DOAJ | 2014

    Free access


    A Comparison of Particle Swarm Optimization and the Genetic Algorithm

    Hassan, Rania / Cohanim, Babak / de Weck, Olivier et al. | AIAA | 2005