<![CDATA[Football fans are individuals who promote, motivate, and inspire football. Players of football clubs have both positive and negative fanaticism in both the real world and social media, especially on Twitter. Twitter is one of the communication media. Attracting people worldwide, Twitter saw a record increase in global users, with 313 million monthly active users in 2016 alone; the majority accessed Twitter through mobile devices, accounting for 82 percent of users. Due to the multitude of users tweeting, the latest news and comments become significant worldwide. What happens becomes the main topic, and comments received from many users trigger trending topics on Twitter. This research aims to develop a classification model to predict whether tweets from stadium events are positive or negative from fan perspectives. The classification model is based on a Twitter dataset, and sentiment analysis of tweets was conducted using the Support Vector Machine (SVM) algorithm. The next step involved preprocessing, including case-folding, cleansing, translation to English, and sentiment labeling using VADER. Subsequently, in the preprocessing step 2, tokenization, stopwords, and stemming were applied. For modeling, classic algorithms such as Naïve Bayes and Support Vector Machine were used. The highest accuracy, 87.77%, was achieved using the Support Vector Machine (SVM) algorithm.]]>
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