<![CDATA[The number of users of the online game Apex Legends continues to increase along with the always active community, which also leads to an increase in the number of user reviews. In this condition, conducting manual review analysis becomes ineffective, especially due to the numerous reviews written in informal English, containing negation words, and also showing an imbalanced sentiment class distribution. In this study, the aim is to classify reviews from Apex Legends users on the Steam platform into positive and negative sentiments using the Multinomial Naive Bayes algorithm with TF-IDF weighting based on N-Gram features with a combination of Unigram and Bigram. The dataset was obtained through web scraping from the Steam platform with a total of 9,000 reviews, followed by preprocessing which resulted in 8,981 valid reviews. However, the data still showed class imbalance. The random undersampling process was then applied to obtain 5,512 balanced data points. The test results show that the model can achieve an accuracy of 0.8132 or 81.32%. For the negative class, the model obtained a precision of 0.79, recall of 0.85, and f1-score of 0.82, while the positive class obtained a precision of 0.83, recall of 0.78, and f1-score of 0.81. The trained model is also applied to a Streamlit based dashboard to support the visualization and prediction of new review sentiments. The contributions of this study are the application of combined N-Gram features (unigram and bigram) to Multinomial Naive Bayes for handling negation context and informal language, the use of random undersampling to address class imbalance, and the deployment of the trained model into a Streamlit-based dashboard that enables direct visualization and sentiment prediction of new reviews.]]>
