Extending associative classifier to detect helpful online reviews with uncertain classes
- https://doi.org/10.2991/ifsa-eusflat-15.2015.160How to use a DOI?
- Review helpfulness, binary classification, class probability, associative classifier
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 fac-ing huge amounts of reviews nowadays. Thus, in light of review classification, this paper proposes a novel ap-proach to identifying review helpfulness. In doing so, a Bayesian inference is introduced to estimate the proba-bilities 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 experi-ments conducted on the reviews from amazon.com re-veal the effectiveness of the proposed approach.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zunqiang Zhang AU - Yue Ma AU - Guoqing Chen AU - Qiang Wei PY - 2015/06 DA - 2015/06 TI - Extending associative classifier to detect helpful online reviews with uncertain classes BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 1134 EP - 1139 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.160 DO - https://doi.org/10.2991/ifsa-eusflat-15.2015.160 ID - Zhang2015/06 ER -