Setiment Analysis of Public Opinion on The Go-Jek Indonesia Through Twitter Using Algorithm Support Vector Machine

The development of technology and information, especially in Indonesia is very
rapid so that social media is the most popular communication tool by the people of Indonesia
today. One of these social media is Twitter. This also causes the public to tend to give opinions
and assessments in the form of tweets to service companies, one of which is Go-Jek Indonesia.
Public opinion and judgment on Twitter can be classified into 3 classes: negative, neutral, and
positive. The purpose of this study is to analyze the sentiment of public opinion on Go-jek
Indonesia on twitter using the Support Vector Machine (SVM) algorithm. The approach used
were Multiclass One Vs Rest SVM with Univariate Chi Square feature selection to classify
community tweets on Go-Jek Indonesia's services. Using testing data of 170 tweets, 31.2% of
people with negative opinions were obtained, 24.1% were neutral and 36.5% were positive
opinions and 5.9% failed to be classified. The results of sentiment analysis testing conducted
provide a classification accuracy of 91.8%.