Consumer Behavior Analysis in Social Networking Big Data Using Correlated Extreme Learning

Abstract

Scrutiny of consumer tweets posted on social media is found to be indispensable for numerous business applications. In this manner, the model of big data analytics is applied in processing data and analyzes it to predict consumer behavioral patterns on social media. Different machine learning algorithms have gathered consumer data to analysis consumer behavior. Conventional methods are unable to discover extreme hidden patterns and require to be enhanced to produce more accurate behavioral patterns. In this work a hybrid method called, proposed Bouldin Correlation Clustering and Gradient Extreme Learning Machine (BCC-GELM) method to perform the consumer behavior analysis in social network with big data. The BCC-GELM method in hybrid model split into two modules. At first, Davis-Bouldin Index-based Correlation Clustering selects clusters with most edges within clusters as positive (i.e., similar information) while most edges between clusters as negative (i.e., dissimilar information), therefore minimizing the error rate. Consumer previous behavioral characteristics and twitter messages are analyzed by means of focal points (i.e., cluster center) via Davis-Bouldin Index. Subsequently, Stochastic Gradient Descent Extreme Learning Machine yields good results by considering distribution of tweets, therefore paving way for predicting consumer behavioral patterns in an optimal manner. The performance of BCC-GELM method is evaluated using experimental analysis and comparison is also made with traditional consumer behavioral pattern methods. The findings demonstrate that BCC-GELM method performs well than the traditional consumer behavioral pattern methods in terms of 9% of clustering accuracy, 45 and 54% of clustering time using without and with preprocessing (percent), 23% of clustering overhead and 46% of error rate.