Optimized Queuing Modeling in Multimodal Sentiment Analysis: An Examination and Application

Abstract

This paper introduces an innovative queuing model tailored for multimodal sentiment analysis, designed to swiftly generate a preliminary score by prioritizing influential factors from the most valuable modalities. In a bid to yield reliable results in a compressed timeframe, this model meticulously processes data from the modality possessing the paramount effect factor, concurrently trimming down processing time by omitting data from lesser impactful modalities. This discerning approach allows for a dynamic adjustment of the weights assigned to various modalities based on their respective impact variables, culminating in precise and credible sentiment analysis outcomes. In an era where expediency and accuracy are at a premium, the proposed model stands out by ensuring that the sentiment analysis is not just rapid, but also robust and reliable, reflecting the true emotional tonality with a high degree of reliability. The model’s adaptability to fluctuating modalities, ensures its utility across diverse platforms and applications, making it a valuable tool for contemporary sentiment analysis tasks. Amidst a digital landscape that continually evolves, this queuing model for sentiment analysis exemplifies a significant leap forward, proffering enhanced efficiency, accuracy, and reliability in sentiment analysis endeavors.