Open AccessNaives Bayes Algorithm for Twitter Sentiment Analysis
Author(s) -
Samsir,
Deci Irmayani,
Edi Firman,
Junaidi Mustapa Harahap,
Jupriaman,
Rizki Kurniawan Rangkuti,
Basyarul Ulya,
Ronal Watrianthos
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1933/1/012019
Subject(s) - indonesian , sadness , government (linguistics) , indonesian government , naive bayes classifier , anger , public opinion , sentiment analysis , social media , anticipation (artificial intelligence) , political science , covid-19 , advertising , psychology , business , computer science , law , artificial intelligence , social psychology , support vector machine , philosophy , linguistics , politics , medicine , disease , pathology , infectious disease (medical specialty)
On 2 March 2020, the Indonesian government, through President Joko ‘Jokowi’ Widodo, announced the first two cases of COVID-19 in Indonesia. This is the first case of COVID-19 officially confirmed in that country. Several cases have continued to increase since then. President Jokowi began issuing policies on the spread of this virus. This is different from other countries, such as Malaysia and Singapore, which responded from the previous month when the Indonesian government still stated that coronavirus does not exist in Indonesia. Our case study is to find a public opinion through social network analysis of Indonesian public policy during the beginning of the Indonesian COVID-19 pandemic in March 2020. This research implements text mining and document-based sentiments on Twitter data that is reprocessed through machine learning techniques using the Naïve Bayes method. We found negative opinions in the period more dominant by 46%, while that was 35% positive sentiment and 20% neutral. This research shows that anticipation, sadness, and anger are very dominant in the emotional analysis.