Implementation of Naive Bayes Classifier (NBC) for Sentiment Analysis on Twitter in Mobile Legends

Deden Kusnanda; Angga Permana

doi:10.46729/ijstm.v4i5.935

Deden Kusnanda Universitas Muhammadiyah Tangerang
Angga Permana Universitas Multimedia Nusantara

DOI: https://doi.org/10.46729/ijstm.v4i5.935

Keywords: Mobile Legends, Analysis Sentiment, Confusion Matrix and Naïve Bayes Classifier.

Abstract

On July 11, 2016, the mobile legends game was first released on the Indonesian server, and became one of the first MOBA games to enter the e-sports branch in Indonesia. The popularity of this game is growing rapidly, reaching more than 500 million downloads in the play store, and reaping a lot of controversy from mobile legends players. So that people create content and express their concerns through social media Twitter in the form of uploads and tweets. This makes the writer want to know how the public sentiment towards mobile legends online games on Twitter social media. The purpose of this research was to analyze how people's opinion about the Mobile Legends Online Game uses the Naïve Bayes Classifier method and to find out the level of accuracy that is obtained automatically by the system. As well as doing manual testing of the Confusion matrix from the results obtained. in classifying tweets. The results of community tweets taken using the data scraping method totaled 217 tweets and became a dataset, after the preprocessing process the tweets totaled 199 and became data testing, after being labeled it showed 100 positive tweets, 25 negative and 74 neutral. The level of accuracy obtained using the Naïve Bayes Classifier method is 80% with 104 positive tweets, 7 negative tweets and 88 neutral tweets. With a precision value of 83% positive, 100% negative and 76% neutral. The recall values obtained were 86% positive, 28% negative and 91% neutral. As for the F1-Score values obtained 84% positive, 44% negative and 83% neutral.

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