Accurate text classification via maximum entropy model

Abstract

Text classification and the research of classification algorithms or models play an important part in the research area of big data, which is among the hottest in our daily life contemporarily. The final target of task of text classification is to choose which is the correct class label that a given text input should belong to. In this paper, we try to propose a more accurate text classification approach by making full use of the principle of maximum entropy model. We conduct a series of experiments of our approach based on a real-world text dataset, which can be downloaded for public research use. The experimental results demonstrate that our proposed approach is very efficient for the task of text classification.