Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

PIKSEL (Penelitian Ilmu Komputer Sistem Embedded dan Logic)

View Publication Info
Field Value
Title Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine
Creator Azhar, Muhamad
Hafidz, Noor
Rudianto, Biktra
Gata, Windu
Description Abstract
Technology implementation in the marketplace world has attracted the attention of researchers to analyze the reviews from customers. The Klik Indomaret application page on GooglePlay is one application that can be used to get information on review data collection. However, getting information on consumer’s opinion or review is not an easy task and need a specific method in categorizing or grouping these reviews into certain groups, i.e. positive or negative reviews. The sentiment analysis study of a review application in GooglePlay is still rare. Therefore, this paper analysis the customer’s sentiment from klikindomaret app using Naive Bayes Classifier (NB) algorithm that is compared to Support Vector Machine (SVM) as well as optimizing the Feature Selection (FS) using the Particle Swarm Optimization method. The results for NB without using FS optimization were 69.74% for accuracy and 0.518 for Area Under Curve (AUC) and for SVM without using FS optimization were 81.21% for accuracy and 0.896 for AUC. While the results of cross-validation NB with FS are 75.21% for accuracy and 0.598 for AUC and cross-validation of SVM with FS is 81.84% for accuracy and 0.898 for AUC, while there is an increase when using the Feature Selection (FS) Particle Swarm Optimization and also the modeling algorithm SVM has a higher value compared to NB for the dataset used in this study.
Keywords: Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.
Publisher LPPM Universitas Islam 45 Bekasi
Date 2020-09-30
Type info:eu-repo/semantics/article
Format application/pdf
Source PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic; Vol 8 No 2 (2020): September 2020; 91 - 100
Language eng
Rights Copyright (c) 2020 PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us


Copyright © 2015-2018 Simon Fraser University Library