Automatic Classification of Unstructured Blog Text - Journal of Intelligent Learning Systems and Applications

JILSA > Vol.5 No.2, May 2013

Automatic Classification of Unstructured Blog Text ()

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DOI: 10.4236/jilsa.2013.52012 5,730 Downloads 9,145 Views Citations

Author(s)

Mita K. Dalal, Mukesh A. Zaveri

Affiliation(s)

Computer Engineering Department, S. V. National Institute of Technology, Surat, India.
Information Technology Department, Sarvajanik College of Engineering & 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.

KEYWORDS

Automatic Blog Text Classification; Feature Extraction; Machine Learning Models; Semi-Supervised Learning

Share and Cite:

M. Dalal and M. Zaveri, "Automatic Classification of Unstructured Blog Text," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 2, 2013, pp. 108-114. doi: 10.4236/jilsa.2013.52012.

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