Opinion mining framework using proposed RB-bayes model for text classication

Rajni Bhalla, Amandeep Bagga


Information mining is a capable idea with incredible potential to anticipate future patterns and conduct. It alludes to the extraction of concealed information from vast data sets by utilizing procedures like factual examination, machine learning, grouping, neural systems and genetic algorithms. In naive baye’s, there exists a problem of zero likelihood. This paper proposed RB-Bayes method based on baye’s theorem for prediction to remove problem of zero likelihood. We also compare our method with few existing methods i.e. naive baye’s and SVM. We demonstrate that this technique is better than some current techniques and specifically can analyze data sets in better way. At the point when the proposed approach is tried on genuine data-sets, the outcomes got improved accuracy in most cases. RB-Bayes calculation having precision 83.333.


naive bayes, RB-bayes, SVM hotencoder, confusion matrix

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DOI: http://doi.org/10.11591/ijece.v9i1.pp477-484
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ISSN 2088-8708, e-ISSN 2722-2578