Public Sentiment Analysis on the Issue of Stopping Tax Payments on Twitter Using the Naive Bayes Method and Support Vector Machine
DOI:
https://doi.org/10.55606/jeei.v2i1.203Keywords:
Naïve Bayes, Sentiment Analysis, Stop Paying Taxes, Support Vector Machine, TwitterAbstract
This research was conducted to find out public opinion on the Stop Paying Tax Issue on Twitter social media. In this study the author aims to use the Naïve Bayes Algorithm and Support Vector Machine in analyzing positive and negative sentiment labels and knowing the results of the accuracy of the Naïve Bayes algorithm and Support Vector Machine in posts by Twitter social media users related to Stop Paying Taxes. The data collection process in this study will using public data sets. The public data set is obtained from 2000 tweets. The final result of this comparison with the two test methods uses the naïve byes algorithm and Support Vector and Machine, namely the prediction results of Public Sentiment on Stop Paying Tax Issues based on data obtained from Twitter and implemented with the SVM (Support Vector Machine) method showing an accuracy value of 84.77 % Of the test data, it is predicted that 1,192 data are Negative Sentiment and 174 data are Positive Sentiment. Of the 1367 test data, 883 data were predicted as Negative Sentiment and 483 data as Positive Sentiment For the prediction results from Negative Sentiment, there were 1367 data predicted Negative and 1 data predicted Positive.
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