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Keywords = polynomial tensor pooling

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26 pages, 15132 KB  
Article
Tree-Based Mix-Order Polynomial Fusion Network for Multimodal Sentiment Analysis
by Jiajia Tang, Ming Hou, Xuanyu Jin, Jianhai Zhang, Qibin Zhao and Wanzeng Kong
Systems 2023, 11(1), 44; https://doi.org/10.3390/systems11010044 - 12 Jan 2023
Cited by 1 | Viewed by 3432
Abstract
Multimodal sentiment analysis is an actively growing field of research, where tensor-based techniques have demonstrated great expressive efficiency in previous research. However, existing sequential sentiment analysis methods only focus on a single fixed-order representation space with a specific order, which results in the [...] Read more.
Multimodal sentiment analysis is an actively growing field of research, where tensor-based techniques have demonstrated great expressive efficiency in previous research. However, existing sequential sentiment analysis methods only focus on a single fixed-order representation space with a specific order, which results in the local optimal performance of the sentiment analysis model. Furthermore, existing methods could only employ a single sentiment analysis strategy at each layer, which indeed limits the capability of exploring comprehensive sentiment properties. In this work, the mixed-order polynomial tensor pooling (MOPTP) block is first proposed to adaptively activate the much more discriminative sentiment properties among mixed-order representation subspaces with varying orders, leading to relatively global optimal performance. Using MOPTP as a basic component, we further establish a tree-based mixed-order polynomial fusion network (TMOPFN) to explore multi-level sentiment properties via the parallel procedure. Indeed, TMOPFN allows using multiple sentiment analysis strategies at the same network layer simultaneously, resulting in the improvement of expressive power and the great flexibility of the model. We verified TMOPFN on three multimodal datasets with various experiments, and find it can obtain state-of-the-art or competitive performance. Full article
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