A Review of Machine Learning Algorithms for Text Classification
Ruiguang Li, Ming Liu, Dawei Xu, Jiaqi Gao, Fudong Wu, Liehuang Zhu
Abstract
Abstract Text classification is a basic task in the field of natural language processing, and it is a basic technology for information retrieval, questioning and answering system, emotion analysis and other advanced tasks. It is one of the earliest application of machine learning algorithm, and has achieved good results. In this paper, we made a review of the traditional and state-of-the-art machine learning algorithms for text classification, such as Naive Bayes, Supporting Vector Machine, Decision Tree, K Nearest Neighbor, Random Forest and neural networks. Then, we discussed the advantages and disadvantages of all kinds of machine learning algorithms in depth. Finally, we made a summary that neural networks and deep learning will become the main research topic in the future.