| Due to rapid increase in volume of data on the Internet every day, it is becoming very difficult to classify text, index documents, and search on World Wide Web for useful and resourceful information. Classifying text by hand requires intense labor, time, and resources. Hence, in this work we are proposing an automated method to classify a given input text into predefined types or classes. Different feature extraction methods are investigated first then we explored different machine learning algorithms for classification and pattern matching. Computational results are presented in this work regarding the different machine learning algorithms and correspond to different areas of applications. In this work, we employ machine learning algorithms including SVM, Decision trees, Random forest and maximum entropy for text classification. We applied these algorithms in different application areas including cyber security, business forecasting, news topic classification, and spam filtering. Finally, we demonstrate the usefulness and applicability of these methods. |
