Text Classification: process and Algorithms

چکیده مقاله

As the volume of information available on the Internet and corporate increases, there is growing interest in developing tools to help people better find, filter, and manage these electronic resources. The aim of text classification is to build systems which are able to automatically classify documents into categories. Text is cheap but information in the form of knowing what classes a text belongs to is expensive. Automatic classification of text can provide this information at low cost. Proper classification of e-documents, online news, emails and digital libraries needs text mining, machine learning and natural language processing techniques to get meaningful knowledge. This paper provided a review of text classification process including documents collection, pre-processing, indexing, feature selection and classification. Moreover, it studied the main algorithms in text classification such as Bayesian classifier, Decision Tree, Decision Rule, K-nearest neighbor(KNN), Support Vector Machines(SVMs), Neural Networks, Rocchio’s Algorithm, Fuzzy Correlation and Genetic Algorithms.

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