An Approach for Web Content Classification with FastText

Nowadays, with the Internet infrastructure and nearly global access, the amount and diversity of data are increasing rapidly. Many tasks require information retrieval and data collection for machine learning, research, and survey reports in various fields such as meteorology, science, geography, literature, and more. However, manual data collection and classification can be time-consuming and prone to errors. Additionally, AI assistants used for drafting or writing can sometimes be corrected regarding writing style and inappropriate language for the given context. Faced with these needs, In this article, Vietnamese documents are classified using the TF-IDF method, TF-IDF combined with SVD, and FastText at three levels: word level, n-gram level, and character level. For this approach, 15 categories were gathered from various online news sources. The dataset was preprocessed and trained using machine learning models such as SVM, Naive Bayes, Neural Network, and Random Forest to find the most effective method. The Random Forest combined with the FastText method was highly evaluated, achieving a success rate of 82% when measured against essential evaluation criteria of accuracy, precision, and F1 score.