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While online product reviews are valuable sources of information to facilitate consumers’ purchase decisions, it is deemed meaningful and important to distinguish helpful reviews from unhelpful ones for consumers facing huge amounts of reviews nowadays. Thus, in light of review classification, this paper proposes a novel approach to identifying review helpfulness. In doing so, a Bayesian inference is introduced to estimate the probabilities of the reviews belonging to respective classes, which differs from the traditional approach that only assigns class labels in a binary manner. Furthermore, an extended fuzzy associative classifier, namely GARCfp, is developed to train review helpfulness classification models based on review class probabilities and fuzzily partitioned review feature values. Finally, data experiments conducted on the reviews from amazon.com reveal the effectiveness of the proposed approach.

Extending associative classifier to detect helpful online reviews with uncertain classes