Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis

Styawati, Styawati; Isnain, Auliya Rahman; Hendrastuty, Nirwana; Andraini, Lili

doi:10.30591/jpit.v6i1.3245

Cited by 2 publications

(1 citation statement)

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“…Naive Bayes (NB) is a classification method that can predict the probability of a class to make decisions based on learning data. The advantages of NB include being easy to use, fast and very accurate when applied to large data [15]. The use of Naïve Bayes can be seen in equation (4).…”

Section: Naïve Bayesmentioning

confidence: 99%

Comparison of the performance of naive bayes and support vector machine in sirekap sentiment analysis with the lexicon-based approach

Setiyawan,

Mustofa

2024

J. Soft Comput. Explor.

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The general public often uses the SiRekap application to see the progress of the election and to provide critical statements. Policies made by the government have good and bad outcomes, and users end up leaving their reviews and ratings on the Google Play Store, where the app can be downloaded. These reviews can be collected and processed into useful information such as sentiment analysis using Naïve Bayes and Support Vector Machine methods. Both methods have differences during training and during evaluation. The difference in results from the various scenarios tested was not much different. When training the Support Vector Machine model is able to process comment data labeled with a lexicon 10% better than the Naïve Bayes model by looking at the results of the accuracy of the two models. During the accuracy evaluation process, the two models produce the same accuracy of 72%. Although both models get the same accuracy during the evaluation process, there are differences in precision, recall, and f1 score. The difference is that the Support Vector Machine model is 5% better for precision, 8% for recall, and 3% for f1-score compared to the Naïve Bayes model. This research is limited to only knowing the performance of two machine learning models, namely the use of naive bayes and svm by using a label lexicon. The results obtained can be improved for the better. Improving the evaluation results can be done by adding data or using text data augmentation and there is creation from experts related to language sentiment.

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Section: Naïve Bayesmentioning

confidence: 99%