Enhancing Classification Effectiveness of Chinese News Based on Term Frequency

For the daily news published on the web, in general, they can be classified into various categories, such as social, politics, entertainment, and so on. These classifications motivate users to watch the desired information. If the classification is wrong, user cannot catch accurately context. How to accurately classify the daily news is becoming an important issue. In this paper, we will propose a method to enhance the effectiveness of news classification. We will utilize the term frequency appeared in variety of classified historical news to training the weighting of each category of each term. And then classify the test news based on the weighting. We propose a framework and an algorithm to training the weighting of each term. The training data, which are over 3500 Chinese news, are collected from UDN and LTN, which are two major electrical news portals in Taiwan. Based on the weighting mechanism, we conduct some experiments to evaluate the effectiveness of the algorithm. The test data are 170 Chinese news, which are collected from Google. The result shows that the traditional manually classification method has up to 13% error classification.