American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/794 2018 2018 Mechatronics Department, Faculty of Engineering, Horus University in Egypt (HUE), Egypt Mohammed K. Hassan Accounting Department, Faculty of Commerce, Kafr El Sheikh University, Egypt Dina K. Hassan Accounting Department, Faculty of Commerce, Mansoura University, Egypt Ahmed K. Metawee Dassault Systemes Deutschland GmbH, Meitnerstraße 8, 70563 Stuttgart, Germany Bassem Hassan Sentimental Analysis (SA) becomes a familiar topic among business people, which is commonly applied for the classification of sentiments from online reviews. It is generally treated as a sentiment classification (SC) problem where the online reviews are categorized into positive or negative polarities using the words that exist in the online reviews. With this motivation, this paper presents a new K-means clustering with hybrid metaheuristic algorithm (KMC-HMA) for SA and classification. The proposed KMC-HMA technique initially performs data preprocessing to remove the unwanted words from the product reviews. In addition, K-means clustering technique is used for the clustering of the massive quantity of the applied product reviews. Moreover, the clustered data are fed into the classification model based on hybrid ant colony optimization (ACO) with dragonfly algorithm (DFA).  The ACO algorithm is used for the classification of product reviews and the performance of the ACO algorithm can be optimally tuned by the use of DFA. The performance validation of the KMC-HMA technique is validated using two datasets such as Canon and ipod. The experimental values pointed out the superior performance of the KMC-HMA technique over the recent state of art techniques. 2021 2021 49 56 10.54216/AJBOR.040201 https://www.americaspg.com/articleinfo/1/show/794