Article citationsMore>>

S. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas and R. Harshman, “Indexing by Latent Semantic Analysis,” Journal of American Society of Information Science, Vol. 41, No. 6, 1990, pp. 391-407. doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

has been cited by the following article:

• TITLE: Automatic Classification of Unstructured Blog Text

  AUTHORS: Mita K. Dalal, Mukesh A. Zaveri

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

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.2, May 22, 2013

  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.