Sentiment Analysis, Vaccine COVID-19, TF-IDF, RNN, Naïve Bayes

Sentiment Analysis, Vaccine COVID-19, TF-IDF, RNN, Naïve Bayes

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtainedfrom 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.

Sentiment Analysis, Vaccine COVID-19, TF-IDF, RNN, Naïve Bayes

Merinda Lestandy1, Abdurrahim Abdurrahim2, Lailis Syafa’ah3

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/24

Universitas Muhammadiyah Malang

26 agustus 2021

Fajar bagus W

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Indonesia

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