Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier.

Abstract

Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the Hybrid LXGB (Long short-term memory Extreme Gradient Boosting) Model, a novel approach for multimodal sentiment analysis that merges the strengths of long short-term memory (LSTM) and XGBoost classifiers. The primary objective is to address the intricate task of understanding emotions across diverse data sources, such as textual data, images, and audio cues. By leveraging the capabilities of deep learning and gradient boosting, the Hybrid LXGB Model achieves an exceptional accuracy of 97.18% on the CMU-MOSEI dataset, surpassing alternative classifiers, including LSTM, CNN, DNN, and XGBoost. This study not only introduces an innovative model but also contributes to the field by showcasing its effectiveness and balance in capturing the nuanced spectrum of sentiments within multimodal datasets. The comparison with equivalent studies highlights the model's remarkable success, emphasizing its potential for practical applications in real-world scenarios. The Hybrid LXGB Model offers a unique and promising perspective in the realm of multimodal sentiment analysis, demonstrating the significance of integrating LSTM and XGBoost for enhanced performance.