Comparison of SVM, Naïve Bayes, and Logistic Regression Algorithms for Sentiment Analysis of Fraud and Bots in Purcashing Concert Ticket

Abstract

Music concerts are highly anticipated entertainment events, but they are often subject to fraud and the use of bots in online ticket purchases, to the detriment of fans and organizers. Fans may lose confidence in the ticket system and reduce interest in the event. For organizers, it can reduce the event's reputation and finances. This research aims to analyze public sentiment regarding this issue by comparing three classification algorithms: Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression. Data taken from Twitter which contains comments related to fraud and bots. The methods used include data crawling, preprocessing, sentiment labeling, and model evaluation. Preprocessing includes data cleaning, case folding, tokenizing, stopwords, and stemming. Sentiment labeling is done manually or by human annotators. The results showed that SVM had the best accuracy of 91.27%, followed by Logistic Regression (90.03%) and Naïve Bayes (77.70%). Applying SMOTE to overcome class imbalance and improve the performance of negative sentiment models. This research emphasizes the importance of choosing the right algorithm and using SMOTE to improve the accuracy of sentiment analysis regarding fraud and bots in concert ticket purchases. The research results can be applied to improve bot usage detection systems and provide insight for organizers.