Word Embedding Features to Improve Machine Learning Performance in Sentiment Analysis of the Honor of Kings Game - Abdul Harris; Agus Nugroho; Yudi Novianto; Jasmir Jasmir; Dhea Fatma (SISTEMASI, 2026)
The rapid growth of social media has encouraged an increasing number of studies on sentiment analysis to better understand public perceptions and opinions. This study aims to evaluate the performance of three machine learning algorithms—Naïve Bayes, K-Nearest Neighbor (KNN), and Random Forest—in classifying user review sentiments toward the game Honor of Kings. The dataset was collected from the Google Play Store, consisting of 900 reviews. The data then underwent preprocessing steps including cleaning, case folding, tokenization, stopword removal, stemming, and sentiment labeling into positive and negative classes. Furthermore, three word embedding techniques were applied, namely Word2Vec, GloVe, and FastText, each of which was tested across the three machine learning algorithms. The experimental results indicate that the use of word embedding features significantly improves classification accuracy compared to models without embedding features. KNN combined with FastText achieved the best performance, reaching an accuracy of 87.55%, while Random Forest combined with FastText produced the lowest accuracy. FastText demonstrated superior performance due to its ability to represent words through subword information, making it more effective in handling rare vocabulary and large-scale datasets. This study confirms that combining machine learning classification methods with word embedding features plays a crucial role in improving sentiment analysis performance. Future research may focus on hyperparameter optimization, the application of more advanced preprocessing techniques, and dataset expansion to develop more robust models with better generalization capability.