SENTIMENT ANALYSIS OF THE COVID VACCINATION ON  SOCIAL MEDIA TWITTER USING NAÏVE BAYES  ALGORITHM

BRYANO SANDY RIZKY

Informasi Umum

Kode

23.04.3215

Klasifikasi

000 - General Works

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Cybernetics

Dilihat

203 kali

Informasi Lainnya

Abstraksi

ABSTRACT The Twitter social network has provided a new space for the public to share their opinions widely. In its journey, Twitter is no longer just a network of friends in the virtual world, but Twitter itself can be used as a forum for its users to freely express opinions. During the Covid-19 pandemic, vaccination was the best solution to overcome the pandemic. Vaccination raises pros and cons among Indonesian people. Responses to these concerns are usually expressed on social media, the majority of the public responds and opinions on concerns related to vaccination through social media, one of the social media that is used as an option to convey these responses and opinions is Twitter. This study aims to analyze public sentiment towards the dissemination of information on the Covid-19 vaccination; identify the most frequently occurring words; and categorizing every opinion that appears into categories of positive and negative sentiments regarding the Covid-19 vaccination program. The methods used are Wordcloud analysis, Sentiment Analysis, and the Naïve Bayes algorithm. The result of this study based on the results of data processing and using the Naïve Bayes algorithm method shows that correct prediction is 113 data (51.36%) which it’s higher than wrong prediction with result 107 (48.64%) data from total 220 test data and accuracy with result is 51.36%. According to impression, the result for positive labeling is 1.126 data (51.36%) and 1.066 data (48.64%) or negative from total 2.192 data.

Keywords: covid vaccination, sentiment analysis, twitter, wordcloud, naïve bayes