Automatic Classification of Unstructured Blog Text (original) (raw)

Automatic classification of blog entries is generally treated as a semi-supervised machine learning task, in which the blog entries are automatically assigned to one of a set of pre-defined classes based on the features extracted from their textual content. This paper attempts automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming; statistical techniques for feature set extraction, and feature set enhancement using semantic resources followed by modeling using two alternative machine learning models-the naïve Bayesian model and the artificial neural network model. Empirical evaluations indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with both machine learning model alternatives. However, the naïve Bayesian classification model clearly out-performs the ANN based classification model when a smaller feature-set is available which is usually the case when a blog topic is recent and the number of training datasets available is restricted.