Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

Abstract

Sentiment analysis (SA) is a growing field at the intersection of computer science and computational linguistics that endeavors to automatically identify the sentiment presented in text. Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language. Sentiment is classified as a negative or positive assessment articulated through language. SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online (of a movie) can be negative or positive toward the thing that has been reviewed. Deep learning (DL) is becoming a powerful machine learning (ML) method for dealing with the increasing demand for precise SA. With this motivation, this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification (MSCADBN-MVC) technique. The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data. Primarily, the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process. For the classification of sentiments that exist in the movie reviews, the ADBN model is utilized in this work. At last, the hyperparameter tuning of the ADBN model is carried out using the MSCA technique, which integrates the Levy flight concepts into the standard sine cosine algorithm (SCA). In order to demonstrate the significant performance of the MSCADBN-MVC model, a wide-ranging experimental analysis is performed on three different datasets. The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.