Text Data Classification Using the SVM Model on the LMDB Minecraft Dataset

Text classification is a fundamental task in Natural Language Processing (NLP) aimed at categorizing text data into predefined classes. This study implements a Support Vector Machine (SVM) model to classify text data from the LMDB Minecraft Dataset, which contains user reviews of the Minecraft movie. The research involves text preprocessing, TF-IDF feature extraction, and SVM model training. The classification results are evaluated using accuracy, precision, recall, f1-score, and confusion matrix metrics. The comment data is also analyzed based on the timing of their appearance in the movie. All processes are visualized in diagrams; the final results are saved in Excel format. The SVM model performs adequately on informal and domain-specific language data, providing a foundation for future research in similar text classification contexts.