<![CDATA[Virtual YouTuber (VTuber) has a feature that allows fan and viewer interaction through live streaming chat that contains textual data based on emotions and opinions. The previous study examined sentiment analysis in various domains. However, live-streaming chat has short, informal, and unstructured text characteristics, making it challenging to analyze its sentiment. Decision Trees (DT) have advantages in interpretability and training speed, while Support Vector Machines (SVM) can handle high-dimensional data and avoid overfitting. Still, few studies examine DT and SVM in live streaming chat. This study aims to propose a sentiment analysis model in VTuber live streaming chat by comparing the performance of DT and SVM. VTuber Lives streaming chat was collected and preprocessed through cleaning, expansion-contraction, case folding, tokenization, stopword removal, and lemmatization. VADER and AFINN Lexicon labeled positive, neutral, or negative sentiments. Later, TF-IDF is used for feature extraction, and K-Fold cross-validation is used to evaluate the sentiment analysis model based on DT and SVM. A confusion matrix measures the model’s performance by knowing the accuracy, precision, recall, and F1 score values. The results of 10-fold cross-validation show that the proposed model with a combination of DT+AFINN with hyperparameter optimization achieves an accuracy of 96.26%. The combination of DT+AFINN shows its superiority in sentiment analysis of VTuber live chat data compared to DT+VADER, SVM+AFINN, and SVM+VADER.]]>
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