DOI of the published article https://doi.org/10.21070/pels.v4i0.1395
Sentiment Analysis Before Presidential Election 2024 Using Naïve Bayes Classifier Based on Public Opinion in Twitter
Analisa Sentimen Jelang Pilpres 2024 Menggunakan Naïve Bayes Classifier Berdasarkan Opini Publik di Twitter
DOI:
https://doi.org/10.21070/ups.2248Keywords:
Naive Bayes, Public Opinion, Sentiment Analysis, TwitterAbstract
This study aims to determine the performance of the Naïve Bayes Classifier algorithm and sentiment analysis tested on a dataset obtained from Twitter social media scrapping with the topic of 2024 presidential candidates. Three candidates frequently discussed in public spaces were used as keyword parameters in data mining: #anis, #ganjar, and #pilpres2024, which were successfully converted to ".xlsx" format documents. The results of the study showed that the best Naïve Bayes model was obtained in the first experiment with a 10% testing data and 90% training data composition, resulting in 71% accuracy, 93% precision, 66% recall, and an fmeasure score of 77%. The conclusion of the study is that the electability of the 2024 presidential candidates shapes public opinion and generates public sentiment in the form of positive and negative tweets. Positive tweets had a higher percentage of 71.5% (1543), while negative sentiment tweets accounted for 28.5% (614).
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