A Hybrid Fuzzy Deep Belief Network Extreme Learning Machine Framework With Hyperbolic Secant Activation Function for Robust Semi‐Supervised Sentiment Classification

Abstract

Sentiment classification deals with extracting and classifying the text sentiment. Fuzzy Deep Belief Network (DBN) has proved its efficiency in dealing with sentiment analysis and suitability for classifying unlabeled or semi‐labeled data. Previous structures of deep belief networks are mostly made of traditional activation functions such as sigmoid. In this paper, a new activation function, which is referred to as hyperbolic secant function, is proposed. The new activation function not only solves gradient zeroing problem but also increases the accuracy and efficiency. Besides, extreme learning machine (ELM) is proposed as the decision layer to increase the accuracy and improve the generalizability through solving gradient‐based learning problem. The efficiency of the proposed method has been experimented on “IMDB” movie critic dataset, 20‐newspaper dataset and Sentiment Analysis dataset. The results of the proposed method are more accurate and precise as compared with the previous approaches.