Open Access

ARTICLE

STP-BTDM: Semi-Tensor Product-Based Block Term Decomposition of Multilinear Pooling Method for Multi-Modal Information Fusion in Sentiment Analysis

Fen Liu^1,*, Jinghua Zhang², Weijie Tan³

1 School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China
2 School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, China
3 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China

* Corresponding Author: Fen Liu. Email:

Computers, Materials & Continua 2026, 89(2), 55 https://doi.org/10.32604/cmc.2026.086571

Received 02 June 2026; Accepted 09 July 2026; Issue published 15 September 2026

Abstract

Multi-modal information fusion integrates data from various sensors, distinct sources, or different modalities, such as audio, images, and text, to achieve a more comprehensive and accurate understanding and analysis. This paper proposes a Semi-Tensor Product-based Block Term Decomposition of Multilinear (STP-BTDM) pooling method and applies it to sentiment analysis and emotion recognition. Unlike prior factorized multilinear approaches, STP-BTDM introduces block-term decomposition with a block-diagonal core tensor, yielding a globally sparse yet locally dense structure and enabling modality-specific independent subspace learning. The technique first introduces the Semi-Tensor Product-based Block Term Decomposition (STP-BTD) model to obtain globally sparse and locally dense weight tensors. Subsequently, by combining the multilinear pooling model, the STP-BTDM method is presented. This approach allows each modality to be controlled by only one block of the block-diagonal core, with different blocks being independent during training. The model can represent full multilinear interactions in a computationally efficient manner. Moreover, the introduction of sparsity constraints in the core tensor of STP-BTDM enhances the generalization performance of multilinear pooling. The resulting locally dense yet globally sparse characteristics make the model highly flexible. Finally, our experiments with the STP-BTDM method on the CMU-MOSI dataset for sentiment analysis and the IEMOCAP dataset for emotion recognition demonstrate its superior performance and effectiveness across both tasks.

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Keywords

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