Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

Marketplace Sentiment Analysis Using Naive Bayes And Support Vector Machine

• By Azhar, Muhamad; Hafidz, Noor; Rudianto, Biktra; Gata, Windu;
• Jurnal Nasional Terakreditasi
• 861
• 91 - 100

Technology implementation in the marketplace world has attracted the attention of researchers to analyze the reviews from customers. The klikindomaret application page on GooglePlay is one application that can be used to get information on review data collection. However, getting an 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. 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.

• Katakunci Naive Bayes, Particle Swarm Optimization, Support Vector Machine, Feature Selection, Consumer Review.
• URL http://jurnal.unismabekasi.ac.id/index.php/piksel
• Penerbit the Center for Research and Community Service of the State Polytechnic of Batam, Batam Riau

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