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Information Technology Journal
Year: 2008  |  Volume: 7  |  Issue: 7  |  Page No.: 1016 - 1022

Automatic Classifications of Malay Proverbs Using Naive Bayesian Algorithm

S.A. Noah and F. Ismail    

Abstract: This research presented an experimental study on automatic classification of Malay proverbs using Naïve Bayesian algorithm. The automatic classification tasks were implemented using two Bayesian models, multinomial and multivariate Bernoulli model. Both models were calibrated using one thousand training and testing dataset which were classified into five categories: family, life, destiny, social and knowledge. Two types of testing have been conducted; testing on dataset with stop words and dataset with no stop words by using three cases of Malay proverbs, i.e., proverb alone, proverb with meaning and proverb with the meaning and example sentences. The intuition was that, since proverbs were commonly short statement, the inclusion of its meaning and associated used in sentences could improve the accuracy of classification. The results showed that a maximum of 72.2 and 68.2% of accuracy have been achieved respectively by the Multinomial model and the Multivariate Bernoulli for the dataset with no stop words using proverb with the meaning and example sentences. This experiment has indicated the capability of the Naïve Bayesian algorithm in performing proverbs classification particularly with the inclusion of meaning and example usage of such proverbs.

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