Abstract
Product reviews and blogs play a vital role in giving an insight to end user for making purchasing decision. Studies show a direct link between product reviews/rating and revenue of product. So, review hosting sites are often targeted to promote or demote products by writing fake reviews. These fictitious opinions which are written to sound authentic known as deceptive opinion spam. To build an automatic classifier for opinion spam detection, feature engineering plays an important role. Deceptive cues are needed to be transformed into features. We have extracted various psychological, linguistic, and other textual features from text reviews. We have used mMulti-view Ensemble Learning (MEL) to build the classifier. Rough Set Based Optimal Feature Set Partitioning (RS-OFSP) algorithm is proposed to construct views for MEL. Proposed algorithm shows promising results when compared to random feature set partitioning (Bryll Pattern Recognit 36(6):1291–1302, 2003) [1] and optimal feature set partitioning (Kumar and Minz Knowl Inf Syst, 2016) [2].
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Saini, M., Verma, S., Sharan, A. (2019). Multi-view Ensemble Learning Using Rough Set Based Feature Ranking for Opinion Spam Detection. In: Bhatia, S., Tiwari, S., Mishra, K., Trivedi, M. (eds) Advances in Computer Communication and Computational Sciences. Advances in Intelligent Systems and Computing, vol 759. Springer, Singapore. https://doi.org/10.1007/978-981-13-0341-8_1
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