SENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING
Covid-19 vaccination program exist as one of the available solutions for covid- 19 pandemic in Indonesia. However, this program has become a matter of debate among several party in Indonesia society. This study aims to analyze covid-19 vaccine sentiment so that it can be a form of feedback for th...
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id-itb.:577092021-08-26T07:18:43ZSENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING Muyassar, Hafizh Indonesia Final Project Covid-19 vaccine, Indonesian people, machine learning, Naive Bayes Classifier, Support Vector Machine. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/57709 Covid-19 vaccination program exist as one of the available solutions for covid- 19 pandemic in Indonesia. However, this program has become a matter of debate among several party in Indonesia society. This study aims to analyze covid-19 vaccine sentiment so that it can be a form of feedback for the upcoming vaccination program. Twitter text data related to covid-19 vaccine starting from April to July 2021 was used as data source for sentiment analysis. To classify the data to negative, neutral, and positive sentiment group, two model of machine learning was build, namely Naive Bayes Classifier dan Support Vector Machine. In this study both of the model was successfully built and it is obtained that Support Vector Machine have a more accurate result in classifying sentiment by reaching 91% accuracy level. Sentiment analysis result showed in this model was in general neutral sentiment, followed by positive and negative sentiment. text |
| institution |
Institut Teknologi Bandung |
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Institut Teknologi Bandung Library |
| continent |
Asia |
| country |
Indonesia Indonesia |
| content_provider |
Institut Teknologi Bandung |
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Digital ITB |
| language |
Indonesia |
| description |
Covid-19 vaccination program exist as one of the available solutions for covid-
19 pandemic in Indonesia. However, this program has become a matter of debate
among several party in Indonesia society. This study aims to analyze covid-19
vaccine sentiment so that it can be a form of feedback for the upcoming vaccination
program. Twitter text data related to covid-19 vaccine starting from April to July
2021 was used as data source for sentiment analysis. To classify the data to negative,
neutral, and positive sentiment group, two model of machine learning was build,
namely Naive Bayes Classifier dan Support Vector Machine. In this study both
of the model was successfully built and it is obtained that Support Vector Machine
have a more accurate result in classifying sentiment by reaching 91% accuracy level.
Sentiment analysis result showed in this model was in general neutral sentiment,
followed by positive and negative sentiment. |
| format |
Final Project |
| author |
Muyassar, Hafizh |
| spellingShingle |
Muyassar, Hafizh SENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING |
| author_facet |
Muyassar, Hafizh |
| author_sort |
Muyassar, Hafizh |
| title |
SENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING |
| title_short |
SENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING |
| title_full |
SENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING |
| title_fullStr |
SENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING |
| title_full_unstemmed |
SENTIMENT ANALYSIS OF COVID-19 VACCINES USING MACHINE LEARNING |
| title_sort |
sentiment analysis of covid-19 vaccines using machine learning |
| url |
https://digilib.itb.ac.id/gdl/view/57709 |
| _version_ |
1822002729689546752 |