Abstract:
The classification of articles has become an important research area due to the increase in unorganized text articles that are available in digital format. One of the main ways to organize digital data is to automatically assign a number of articles to predefined categories based on their content. Article classification is a process that consists of a phases. Each phase can be carried out using different techniques. Choosing the right technique for each phase affects the efficiency of article classification performance. The goal of this project is to provide a classification model which supports both general and efficiency.
