Sentiment augmented Bayesian network
Document Type
Conference Proceeding
Publication Title
Conferences in Research and Practice in Information Technology Series
Abstract
Sentiment 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 and MDL. The outcome of this technique is called Sentiment Augmented Bayesian Network. Empirical results on three product review datasets from different domains, suggest that a sentiment-augmented scoring mechanism for Bayesian Network classifier, has comparable performance, and in some cases outperform state-of-the-art sentiment classifiers.
First Page
89
Last Page
98
Publication Date
1-1-2013
Recommended Citation
Orimaye, Sylvester Olubolu, "Sentiment augmented Bayesian network" (2013). Global Population Health Faculty Publications. 25.
https://collections.uhsp.edu/global-population-health_pubs/25