Integrated deep learning paradigm for document-based sentiment analysis
Peter Atandoh, Peter Atandoh, Fengli Zhang, Daniel Adu-Gyamfi, Paul H. Atandoh, Paul H. Atandoh, Raphael Elimeli Nuhoho
Abstract
An integrated deep learning paradigm for the analysis of document-based sentiments is presented in this article. Generally, sentiment analysis has enormous applications in the real world, particularly in e-commerce and/or cloud computing-oriented businesses. Integrated deep learning paradigms for document-based sentiment analysis seek to efficiently categorize the polarity of contextual sentiments into positive, negative, and neutral to aid organizations in making informed decisions. Nonetheless, the sparsity of text and disambiguation of natural languages make it relatively difficult for existing methods to provide precise identification and extraction when subjected to document-based data. As a result, this study introduces BERT-MultiLayered Convolutional Neural Network (B-MLCNN) as a computationally viable integrated deep learning paradigm. The B-MLCNN considers the overall textual review as a single document and classifies the available sentiments. First, the BERT pre-trained language model handles the feature vector representation and captures any global features. Further, the multi-layered convolutional neural network (MLCNN) with different kernel dimensions handles feature extraction. A softmax function produces classification results. The experimental setup with B-MLCNN based on IMDB movie reviews, 2002 movie reviews, 2004 movie reviews, and Amazon review datasets achieved accuracies of 95%, 88%, 95%, and 95% respectively, which promises to be efficient to deploy in practical applications.