Experiment Results on the Weibo dataset.

<div><p>The rapid development of social media has significantly impacted sentiment analysis, essential for understanding public opinion and predicting social trends. However, modality fusion in sentiment analysis can introduce a lot of noise because of the differences in semantic represe...

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Bibliographic Details
Main Author:	Guangyu Mu (762845) (author)
Other Authors:	Ying Chen (9697) (author), Xiurong Li (812565) (author), Li Dai (159381) (author), Jiaxiu Dai (21192737) (author)
Published:	2025
Subjects:	Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified publicly available datasets kl divergence losses understanding public opinion public opinion events ten baseline models modal interaction fusion 19 %, 82 experimental results show text feature representations predicting social trends secif model improves multimodal sentiment analysis proposed secif model opinion trends sentiment analysis multimodal models social media modality fusion 27 %, results demonstrate classification results xlink "> visual features ultimately impacting text features sina weibo semantic enhancement rapid development profound integration paper presents icn module gmha mechanism f1 score dataset collected considered primary conducted using 56 %. &# 160
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Experiment Results on the Weibo dataset.

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