Enhanced audience sentiment analysis in IoT-integrated metaverse media communication
Hongtao Wang, Shan Wang, Yijun Lu, Nikolai Ivanovich Vatin, Jiandong Huang, Hung Thanh Bui, Hung Thanh Bui, Hung Thanh Bui
TL;DR
This paper introduces a deep learning framework for real-time sentiment analysis in IoT-integrated Metaverse media, achieving high accuracy and scalability.
Contribution
A novel BG-Hybrid model combining BERT and GPT for context-aware emotion detection in heterogeneous media flows.
Findings
The BG-Hybrid model achieved 94.5% accuracy and 91.5% F1-score on benchmark datasets.
The system demonstrated an average response latency of 250 ms and scalability exceeding 91.5%.
Predictive thresholding and anomaly detection improved temporal sentiment analysis and data trustworthiness.
Abstract
The convergence of Metaverse technologies, Internet of Things (IoT), and consumer electronics has given rise to an imperative need for scalable, real-time sentiment analysis that can process heterogeneous, high-velocity media flows. The traditional approaches tend to fail in preserving the contextual, emotional, and temporal dynamism that pervades cross-platform settings. For these shortcomings, this work proposes a deep learning-based framework for sentiment analysis that integrates IoT-enabled consumer devices and Metaverse media interactions seamlessly. The overall BG-Hybrid model, fundamentally, blends BERT-led bidirectional encoding and GPT-based generative modeling to attain subtle emotion detection and context-aware comprehending. The five interconnected modules constituting the architecture include (i) multi-source data collection using RESTful APIs; (ii) weighted preprocessing…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Emotion and Mood Recognition · Stock Market Forecasting Methods