Learning Sentiment Dependent Bayesian Network Classifier for Online Product Reviews

Sylvester Olubolu Orimaye, Zi Yang Pang, Alvino Mandala Putra Setiawan


Analyzing sentiments for polarity classification has recently gained attention in the literature with different machine learning techniques performing moderately. However, the challenges that sentiment classification constitutes require a more effective approach for better results. In this study, we propose a logical approach that augments the popular Bayesian Network for a more effective sentiment classification task. We emphasize on creating dependency networks with quality variables by using a sentiment-dependent scoring technique that penalizes the existing Bayesian Network scoring functions such as K2, BDeu, Entropy, AIC and MDL. The outcome of this technique is called Sentiment Dependent Bayesian Network. Empirical results on eight product review datasets from different domains, suggest that a sentimentdependent
scoring mechanism for Bayesian Network classifier could improve sentiment classification.

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