Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia

Laurenzius Julio Anreaja, Norma Nobuala Harefa, Julius Galih Prima Negara, Venantius Nathan Hermanu Pribyantara, Agung Budi Prasetyo

Abstract


Opensea is an NFT buying and selling application-based platform that is booming in the community. One way to find out the public's perception of the Opensea application is by sentiment analysis, as done in this study. Data that is used is user review data for the Opensea application in the Indonesian play store. The sentiment analysis technique used is the Naïve Bayes Classifier and the Support Vector Machine (SVM) method. Both are used to compare public responses from sentiment analysis of reviewed data labeled as positive, negative, and neutral. Based on this study, it was found that the Naive Bayes algorithm gives the results that class precision is 87.31%, class recall is 71.02%, and accuracy is 89.81%. While the SVM algorithm gives the results that class precision is 94.23%, class recall 71.96%, and Accuracy 90.78%. It is concluded that the SVM algorithm has a better performance than the Naive Bayes algorithm.

 

 


Keywords


Opensea, NFT, Sentiment Analysis, Google play store, SVM, Naive Bayes

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References


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DOI: https://doi.org/10.31326/jisa.v5i1.1267

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Copyright (c) 2022 Laurenzius Julio Anreaja, Norma Nobuala Harefa, Julius Galih Prima Negara, Venantius Nathan Hermanu Pribyantara, Agung Budi Prasetyo

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