Abstract
The Social web contains enormous content, in which higher range of online users believe the Online Reviews for their decision-making before any online purchase or using services. The online reviews written are all not true and some of them are Spam. Data Mining Classification helps in finding the Review as Spam or Ham. Many Text Classification Algorithms are existing and it has been proved that these classifiers can be improved when Hybrization of classifiers is performed. This research work focuses on developing hybrid classifiers for spam review detections. Hybridization of classification is performed as Classification-Classification process by using NaiveBayes, K Nearest Neighbor(KNN) as base classifiers in stage 1 and Support Vector Machine (SVM) Classifier in stage 2. Amazon-based and Yelp Review Datasets are used for implemenation and the Accuracy of the proposed Hybrid classifier improves the performance of the classifier from 89.04 to 93.50% of Accuracy.
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Krishnaveni, N., Radha, V. (2022). A Hybrid Classifier for Detection of Online Spam Reviews. In: Chandramohan, S., Venkatesh, B., Sekhar Dash, S., Das, S., Sharmeela, C. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1361. Springer, Singapore. https://doi.org/10.1007/978-981-16-2674-6_25
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DOI: https://doi.org/10.1007/978-981-16-2674-6_25
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