Abstract
The importance of consumer reviews has evolved significantly with increasing inclination towards e-Commerce. Potential consumers exhibit sincere intents in seeking opinions of other consumers. These consumers have had a usage experience of the products they are intending to make a purchase decision on. The underlying businesses also deem it fit to ascertain common public opinions regarding the quality of their products as well as services. However, the consumer reviews have bulked over time to such an extent that it has become a highly challenging task to read all the reviews and detect their genuineness. Hence, it is crucial to manage reviews since spammers can manipulate the reviews to demote or promote wrong product. The paper proposes an algorithm for detecting the fake reviews. Since the proposed work concentrates only on text. So, n-gram (unigram + bigram) features are used. Supervised learning technique is used for reviews filtering. The proposed algorithm considers the combination of multiple learning algorithms for better predictive performance. The obtained results clearly indicate that using only simple features like n-gram, Ensemble can boost efficiency of algorithm at significant level.
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Mani, S., Kumari, S., Jain, A., Kumar, P. (2018). Spam Review Detection Using Ensemble Machine Learning. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_15
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