A Comparative Study of Machine Learning Approaches for Text Classification
نویسندگان
چکیده
Perhaps the single largest data source in the world is the world wide web. Heterogeneous and unstructured nature of the data on web has challenged mining the web. Practical needs to extract textual information and unseen patterns continue to drive the research interest in text mining. Faultless categorization of texts can be better performed by machine learning techniques. In this paper we present a review of various text classification approaches under machine learning paradigm. Existing classification algorithms including Decision Tree, Naive Bayes, Support Vector Machine and k-Nearest Neighbors are compared based on speed, accuracy, interpretability and multi-class support.
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