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

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086571 - 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… More >

Weijun Gao, Ziyang Zhang^*, Maotang Su

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085382 - 13 August 2026

Abstract Multimodal sentiment analysis (MSA) has made significant progress in integrating heterogeneous information from text, speech, and vision. However, real-world multimodal data often suffer from modality noise, semantic inconsistency, and incomplete modality information, which can weaken cross-modal fusion and reduce the reliability of sentiment prediction. To address these challenges, this paper proposes RUAL, a robust uncertainty-aware learning framework for multimodal sentiment analysis. Specifically, RUAL first employs a Gathered Multi-Head Attention Pooling (GMHA) module to aggregate intra-modal features and estimate modality uncertainty based on attention entropy. Then, an Uncertainty-Aware Cross-Modal Coupled Layer (UACCL) is introduced to dynamically More >

Mahander Kumar¹, Lal Khan^2,*, Mohammad Zubair Khan^3,*, Ibrahim Aljubayri⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.081699 - 27 July 2026

Abstract Aspect-Based Sentiment Analysis (ABSA) is a fundamental Natural Language Processing (NLP) task that aims to determine fine-grained sentiment polarity toward specific aspects mentioned in text. With the emergence of Large Language Models (LLMs) and transformer-based architectures, significant improvements have been achieved in contextual representation learning for sentiment analysis. However, existing LLM-inspired and transformer-based ABSA frameworks often suffer from inadequate aspect-context alignment, redundant feature integration, limited interpretability, and insufficient coordination between contextual and sequential modeling components. To address these challenges, this paper proposes two hybrid architectures, namely HABSA and AAC-HABSA, centered on a novel Adaptive Aspect… More > Graphic Abstract

Mubariz Khan¹, Hafeez Ur Rehman Siddiqui², Adil Ali Saleem², Muhammad Amjad Raza^2,3, Lázaro Javier Hernández Rodríguez^4,5,6,7, Pablo Herrero García^4,8,9, Isabel de la Torre Díez^10,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084269 - 23 July 2026

Abstract Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear, sentiment-driven dynamics of these markets. This study compares three hybrid deep learning architectures—VAR-LSTM, XGBoost-LSTM, and CNN-LSTM—to determine which best forecasts Bitcoin (BTC), Ethereum (ETH), and Dogecoin (DOGE) closing prices, and to quantify the marginal predictive value of Twitter sentiment integration. Six years of hourly OHLCV data (2017–2023) are augmented with VADER-scored Twitter sentiment polarity. Each model is formulated mathematically, implemented with documented hyperparameters (epochs, dropout, units;), and trained for one-step-ahead next-hour price prediction. Performance is measured by RMSE,… More >

Ying Cao¹, Penghui Zhao¹, Xinyu Qiao¹, Ningfan Zhan¹, Xiaomei Zou^2,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084057 - 23 July 2026

Abstract Multimodal Sentiment Analysis (MSA) integrates diverse modalities to identify emotional states, yet performance often suffers in scenarios with missing data. In this situation, despite the promising results of recent methods, the failure of part methods to fully exploit the latent valid information contained in incomplete modalities may degrade predictive performance. Besides, to address the oversight of varying contributions across modalities to sentiment understanding, the score-based weighting schemes in the exhibited methods remain overly sensitive to data fluctuations, leading to unstable and unreliable predictions. To this end, we propose a novel method, Data Mining and Uncertainty-Aware… More >

Jinjin Liu^1,2,3,*, Changchang Fan^1,2, Qiulu Guo^1,2, Yihao Xu^1,2, Penghui Ma^1,2

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083112 - 23 July 2026

Abstract Incomplete multimodal sentiment analysis has attracted increasing research interest in recent years. Existing methods attempt to recover missing modalities through generative reconstruction and text-enhanced fusion, but these approaches may be limited in preserving sentiment-relevant information and fully leveraging complementary and hierarchical cross-modal interactions, particularly under noisy or incomplete conditions. To address these challenges, we propose TC-DSC, a text-centric hierarchical dual-stream interaction framework for incomplete multimodal sentiment analysis. Rather than reconstructing raw signals, TC-DSC performs semantic alignment and consistency modeling in the feature space through structured interactions between a text-centric stream and auxiliary audio-visual streams. A More >

Naimul Hasan Shadesh^*, Zannatul Ferdous, Bipasha Iasmin

Journal on Artificial Intelligence, Vol.8, pp. 335-357, 2026, DOI:10.32604/jai.2026.078589 - 22 July 2026

Abstract Sentiment analysis is a core task in Natural Language Processing (NLP) that aims to identify opinions and sentiment polarity expressed in textual data. This study presents a systematic empirical evaluation of classical machine learning–based sentiment analysis methods using a unified experimental framework. Several supervised classifiers, including Decision Trees, Logistic Regression, Support Vector Machines (SVM), Random Forests, Naïve Bayes, and K-Nearest Neighbors (KNN), are evaluated on labeled text datasets collected from multiple domains such as product reviews, customer feedback, hotel reviews, and social media content. The experimental pipeline includes standard NLP preprocessing steps—text normalization, tokenization, stopword More >

Anitya Kumar Gupta^*, Pankaj Vaidya, Anurag Rana

Journal on Artificial Intelligence, Vol.8, pp. 377-402, 2026, DOI:10.32604/jai.2026.074171 - 22 July 2026

Abstract Sentiment analysis aims at determining the stance or point of view of a topic, author, or speaker about a certain topic, document, or event. The given paper proposes a hybrid quantum-classical model of sentiment classification, which is known as Complex-Valued Quantum-Enhanced Recurrent Neural Network (CQRNN). The model combines Quantum Long Short-Term Memory (QLSTM) and Quantum Gated Recurrent Units (QGRU) with the use of Variational Quantum Circuits (VQCs) and complex-valued embeddings designed at the level of semantic information, both in the real and imaginary domains. Experiments on benchmark sentiment datasets show that CQRNN can be fine-tuned More >

Xinshan Huang¹, Zirui Pei¹, Chaohong Tan², Zuqiang Meng^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081626 - 15 June 2026

Abstract Multimodal Sentiment Analysis (MSA) plays an important role in understanding social media content; however, existing methods often struggle with the heterogeneity and complex interactions between images and text. These challenges include inter-modal information asymmetry, insufficient feature fusion, and noise interference, which collectively limit robustness and accuracy. To address these issues, we propose a multimodal sentiment classification model termed Multi-Branch Cross-Modal Cross-Attention Gating (MB-CMCAG). The model first incorporates a Transformer-based image caption generation module to convert raw images into semantically rich auxiliary textual descriptions, which complement the original text and form paired textual inputs with enhanced… More >

Hongbin Wang^1,2, Liusong Li^1,2, Di Jiang^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.079330 - 15 June 2026

Abstract Multimodal sentiment analysis aims to fuse emotional information from data across different modalities to predict human emotional states. Although existing multimodal sentiment analysis methods have made significant progress, the heterogeneity between modalities still leads to an imbalance in feature space distribution, thereby hindering the effective learning and fusion of multimodal representations. In addition, the presence of emotion-irrelevant information in auxiliary modalities is another major factor contributing to differences in feature space distributions. To address this issue, we propose a Hierarchical Contrastive Representation Learning framework with Multimodal Feature Decoupling (HCRL-MFD). To reduce emotion-irrelevant information and optimize… More >