S1 File - Enhanced audience sentiment analysis in IoT-integrated metaverse media communication

<p>The Sentiment140 Twitter sentiment dataset analyzed in this study is publicly available and can be downloaded directly from: (<a href="https://nyc3.digitaloceanspaces.com/ml-files-distro/v1/investigating-sentiment-analysis/data/training.1600000.processed.noemoticon.csv.zip" tar...

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Bibliographic Details
Main Author:	Hongtao Wang (570131) (author)
Other Authors:	Shan Wang (120085) (author), Yijun Lu (5154233) (author), Nikolai Ivanovich Vatin (12005927) (author), Jiandong Huang (1462300) (author)
Published:	2025
Subjects:	Biological Sciences not elsewhere classified Information Systems not elsewhere classified velocity media flows traditional approaches tend led bidirectional encoding entropy loss minimization average response latency enabled consumer devices based generative modeling time sentiment analysis optimized model training sentiment analysis based training hybrid model consumer electronics based framework xlink "> work proposes twitter sentiment140 temporal dynamism process heterogeneous predictive thresholding platform settings overall bg obtaining 94 metaverse technologies imperative need given rise feedback mechanism experimental analyses deep learning blends bert aware comprehending architecture include adam algorithm 250 ms
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S1 File - Enhanced audience sentiment analysis in IoT-integrated metaverse media communication

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