Piecewise linear convolutional deep belief classifier for consumer Behavior Analysis in Social Network in Gaussian-embedded clustering

Abstract

ABSTRACT A novel technique Gaussian-Embedded Clustering-based Piecewise Linear Convolutional Deep Belief Classifier (GEC-PLCDBC) is introduced for enhancing the accuracy of consumer behaviors pattern prediction with minimum time consumption. The proposed GEC-PLCDBC technique performs three major processes, namely, preprocessing, clustering, and behavior pattern analysis. First, preprocessing is carried out using Gaussian distributive neighbor embedding technique to remove the repeated, retweets, and duplicate tweets from the dataset. After preprocessing, the clustering process is said to be performed using Tversky qualitative Hartigan–Wong clustering for grouping similar tweets to minimize the time complexity of the behavior pattern analysis. Finally, the Piecewise Linear Regressive Convolutional Deep Belief Neural Classifier (PLRCDBNC) is used for behavioral pattern prediction such as positive, negative, or neutral based on the extracted words from the tweets. In this way, the GEC-PLCDBC technique improves behavioral pattern prediction while maintaining a low error rate. GEC-PLCDBC technique’s experimental assessment is carried out with respect to clustering accuracy, behavior pattern prediction accuracy, behavior pattern prediction time, and error rate, with respect to different number of tweets. The quantitative results show that GEC-PLCDBC outperforms existing methods by 8% in clustering accuracy and 8% in prediction accuracy of behavior pattern prediction with 15% less time and 26% mean absolute error than the conventional methods.