Abstract

In the intricate tapestry of e-commerce, where human-generated content unveils a burst of sentiments within visual expressions, our research propels the exploration of sentiment analysis methodologies. Focused on deciphering the nuanced emotional undertones within user-generated visual content, our approach integrates deep learning, semantic text analysis, visual sentiment analysis, and human-robot interaction. The interplay of these methodologies resonates with the explosion inherent in human expression, acknowledging the multifaceted nature of sentiments encapsulated within the pixels. Our methodology begins with deep learning assisted semantic text analysis (DLSTA), a robust framework designed for human emotion detection using big data. By harnessing word embeddings and natural language processing, our model delves into the semantic and syntactic intricacies of textual expressions, achieving an expressively superior human emotion detection rate of 98.76% and a classification accuracy rate of 98.67%. Expanding beyond textual nuances, our approach extends to visual sentiment analysis, adapting the developed framework to the dynamic landscape of e-commerce. User-generated product images become focal points, and the adaptability of our methodology is showcased through precision, recall, and F1 score metrics across ten samples. The explosion within visual expressions is acknowledged, with each image presenting a unique burst of sentiment that our model navigates with interpretative finesse. Human-robot interaction emerges as a pivotal layer within our methodology, injecting a layer of complexity and depth to sentiment analysis. The dynamic interplay between human intuition and computational precision mirrors the explosion within visual content, capturing not only the static nature of images but the evolving stream of sentiments encountered in the digital marketplace.