A Sentimental Classification Approach to Predict the User Behavior Using an Optimized Centered Convolutional Restricted Boltzmann Machine

Abstract

Sentiment analysis uses different tools and techniques to extract informative data such as users' opinions or emotions from their textual feedback. The state-of-art sentiment analysis techniques offered lower performance due to the inability to handle both small and larger datasets. To overcome this problem this paper presents a deep learning technique known as Centered Convolutional Restricted Boltzmann Machines (CCRBM) for user behavioral sentimental analysis. However, this deep learning model's performance solely depends upon the parameter selection process. To overcome this problem and increase the classification accuracy a Hybrid Atom Search Arithmetic Optimization (HASAO) algorithm is used in this paper to select the parameters of the CCRBM architecture and offer optimal performance. The initial population quality and exploitation capacity of the Atom Search Optimization (ASO) algorithm is enhanced by hybridizing it with the Arithmetic Optimization(AO) algorithm. To investigate the effectiveness of the proposed HASAO optimized CCRBM architecture it is evaluated using four different datasets namely Reddit, Twitter, IMDB movie review, and Yelp dataset. The performance of the proposed model is analyzed by comparing it with four baseline models and the accuracy value above 90% for the four datasets proves the efficiency of the proposed technique.