Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection

Abstract

Customers nowadays rely heavily on online reviews when making buying decisions. Various internet websites, including Amazon, Yelp, Google Plus, BookMyShow, Facebook, Twitter, and others, allow users to generate massive amounts of data. The information is gathered through feedback/reviews, comments, and tweets. Companies can leverage this information to improve the quality of their products. Spam reviews are created pretentiously by some businesses and people to promote or degrade the popularity of any product, organization, or person due to their reliance on these online reviews. Thus, identifying spam or nonspam review by the naked eye is nearly impossible. Classifying the reviews manually is also highly speculative. Hence, to overcome this issue, a fitness-based Grey Wolf Optimizer (FGWOK) clustering method is proposed in this paper to identify spam reviews. The fitness-based grey wolf optimization (GWO) is used to obtain the optimal cluster heads in the proposed method. In the fitness-based GWO, the position of the grey wolves is updated in two phases. In the first phase, all the search agents update their position using the contemporary GWO, and in the second phase, the fitness is evaluated to update the position. To prove that the proposed strategy is effective, three spam data sets, namely synthetic spam reviews, movie reviews, and Yelp hotel and restaurant reviews, have been used in our work. The reported results are compared with the existing state-of-art metaheuristic clustering methods like a genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), k-Means, and artificial bee colony clustering (ABCK) method. The experimental and statistical analysis results show that the proposed FGWOK algorithm outperforms current methodologies.