Implementation of Naive Bayes Algorithm and Support Vector Machine for Public Sentiment Analysis towards Imported Clothing Ban
DOI:
https://doi.org/10.55606/jeei.v2i3.313Keywords:
Imported Used Clothing, Naïve Bayes, Sentiment Analysis, Support Vector Machine, TwitterAbstract
This research was conducted to find out the public's opinion on the Issue of Imported Clothing on Twitter social media. One of the algorithms that can be used to carry out sentiment analysis is Naïve Bayes and Support VectorMachine. In this research the author aims to use the Naïve Bayes Algorithm and Support Vector Machine in analyzing positive and negative sentiment labels. The final result of the comparison with these two test methods, namely the prediction of public sentiment on the issue of imported clothing based on data obtained from Twitter and implemented using the SVM (Support Vector Machine) method, shows an accuracy value of 87.89%. Of the 603 test data, it is predicted that 194 data are Positive Sentiment and 409 data are Negative Sentiment. For prediction results from Negative Sentiment, there are 603 data predicted Negative and 2 data predicted Positive. and the Naive Bayes method shows an accuracy value of 97.01%. Of the 603 test data, it is predicted that 409 data are Negative Sentiment and 194 data are Positive Sentiment.
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