Automatic Classification of Unstructured Blog Text
- 1 Information Technology Department, Sarvajanik College of Engineering & Technology, Surat, India
- 2 Computer Engineering Department, S. V. National Institute of Technology, Surat, India
Abstract
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 na ? ve 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 na ? ve 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.
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