A Analysis of YouTube User Sentiment About Rohingya Using the SVM (Support Vector Machine) Algorithm
DOI:
https://doi.org/10.33379/gtech.v8i3.4497Keywords:
Sentiment analysis, SVM, YouTube, RohingyaAbstract
YouTube is a widely used platform in Indonesian society, largely due to its constantly updated content reflecting current events in Indonesia, such as the Rohingya refugee crisis. This content attracts numerous netizens to comment on the events, both positively and negatively. Text mining techniques are branch of science that utilizes comment data for study and analysis. From diverse comment data, they are grouped using sentiment analysis. Among YouTube videos discussing the Rohingya case, there are many unstructured comments, requiring steps and methods to process the data into structured and directed data. In this study, sentiment analysis will utilize the SVM method. SVM is a commonly used method in sentiment analysis and has a high accuracy rate. From the classification results obtained, an accuracy score of 77%, recall of 100%, and precision of 77% were obtained. From these results, it can be concluded that this method is suitable for sentiment analysis.
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