An Ensemble Approach to Enhance the Efficacy of Sentiment Prediction
Monalisha Ghosh, Anirban Chakraborty, Indrajit Pal · International Journal of Computer Theory and Engineering · 2024
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.