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Paper Details

Paper Title
Using Machine Learning for Automatic Text Classification of Unstructured Blog Data
Opportunities for integrating applications of machine intelligence into the daily lives of people are growing with the increasing popularity of computing systems, the widening diversity of web services, the growing popularity of portable devices that contain general-purpose operating systems, and ongoing inventions in human-computer interaction— including the cases of speech recognition, handwriting, and sketch-understanding interfaces. Much of machine learning (ML) research is inspired by problems and its solutions from biology, medicine, finance, astronomy, etc.This paper presents automatic classification of unstructured blog entries by following pre-processing steps like tokenization, stop-word elimination and stemming. It uses Machine Learning techniques for feature set extraction, and feature set enhancement by semantic resources followed by modeling using a alternative machine learning model—the naïve Bayesian model.Empirical evaluations and calculations done in this paper indicate that this multi-step classification approach has resulted in good overall classification accuracy over unstructured blog text datasets with machine learning model alternative.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. The naïve Bayesian classification model clearly out-performs the other 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.
Automatic Blog Text Classification; Feature Extraction; Machine Learning Models; Semi-Supervised Learning; Polysemy ; Prior Probability.
Others Details
Paper Id : 67809
Author Name : Dr. Nitin Rajvanshi
Volume/Issue No : Volume 05 Issue 03
Page No : 95-99
DOI Number : DOI:10.21090/IJAERD.67809
Publication Date : 2018-03-07
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website :
Impact Factor : 4.72, SJIF-2016
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406