Future Institute of Engineering and Management (FIEM), Makaut University, India
E-mail: mghoshnit2019@gmail.com (M.G.); chakraborty.anirban@rediffmail.com (A.C.); imindramc@gmail.com (I.P.)
*Corresponding author
Manuscript received March 3, 2023; revised April 20, 2023; accepted July 10, 2023; published May 23, 2024
Abstract—Sentiment Analysis (SA) has recently been considered as the most active research field in the Natural Language Processing (NLP) domain. Deep Learning (DL) is a subset of the large family of Machine Learning (ML) and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based machine learning framework has been attempted by merging the traditional machine learning method with deep learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors were augmented with an optimized feature set generated from the Binary Particle Swarm Optimisation (BPSO) method. Finally, a traditional Support Vector Machine (SVM) classifier is trained with this extended feature set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets: IMDb, SST-2. Experimental results with comparative studies based on performance accuracy and F-Score value are reported to highlight the benefits of the developed frameworks.
Keywords—deep learning framework, sentiment analysis, Binary Particle Swarm Optimisation (BPSO), Convolutional Neural Network (CNN), Support Vector Machine (SVM)
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Cite: Monalisha Ghosh, Anirban Chakraborty, and Indrajit Pal, "An Ensemble Approach to Enhance the Efficacy of Sentiment Prediction," International Journal of Computer Theory and Engineering, vol. 16, no. 2, pp. 55-65, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
