Soumalya De¹, Rahil Akhtar², Saiyed Umer², Ranjeet Kumar Rout³, G. G. Md. Nawaz Ali^4,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.079340 - 08 May 2026

Abstract This work presents a comprehensive sustainable sentiment analysis system utilizing textual data, designed within a structured client-server architecture for real-time deployment. The system integrates dual feature representations Bag-of-Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF) whose prediction scores are combined through a parameter-free score-level fusion strategy. The implementation of the proposed system consists of five major components. The first component involves the acquisition of textual data from various sources, followed by rigorous text preprocessing to eliminate noise and enhance data quality. The second component focuses on feature extraction, ensuring that the extracted features not only… More >

Choongwon Kang^1,2, Haein Lee^3,4, Jang Hyun Kim^1,2,*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.078997 - 08 May 2026

Abstract With the rapid growth of social media and frequent anonymous interactions, hate speech has become widespread. As users express diverse opinions in digital spaces, the need for effective detection remains crucial. To address this, we propose a framework applicable to diverse hate speech types, combining sentence-level semantic representation vectors from the pre-trained Bidirectional Encoder Representations from Transformers (BERT) with sentiment score vectors from the Linguistic Inquiry and Word Count (LIWC) dictionary and the Valence Aware Dictionary for sEntiment Reasoning (VADER). This semantic-sentiment fusion integrates three deep learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), More >

Shuqiu Tan, Chunsheng Tan, Yahui Liu^*

CMC-Computers, Materials & Continua, Vol.88, No.1, 2026, DOI:10.32604/cmc.2026.077982 - 08 May 2026

Abstract Multimodal sentiment analysis aims to accurately identify emotional states by comprehensively utilizing information from multiple sources such as text, audio, and visual data. However, semantic heterogeneity and temporal differences exist between different modalities, limiting the effectiveness of feature fusion. To address this issue, this paper proposes a hierarchical joint cross-modal attention and gating mechanism (HJCAG) for multimodal sentiment analysis. This method introduces a hierarchical structure, dividing modal interactions into bimodal and trimodal layers to progressively model the semantic relevance between modalities. First, deep features are extracted from text, audio, and visual modalities using pre-trained models… More >

Zilin Zhang¹, Yan Liu^1,*, Jia Liu², Senbao Hou³, Yuping Zhang¹, Chenyuan Wang¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-14, 2026, DOI:10.32604/cmc.2025.072286 - 09 December 2025

Abstract With the growing demand for more comprehensive and nuanced sentiment understanding, Multimodal Sentiment Analysis (MSA) has gained significant traction in recent years and continues to attract widespread attention in the academic community. Despite notable advances, existing approaches still face critical challenges in both information modeling and modality fusion. On one hand, many current methods rely heavily on encoders to extract global features from each modality, which limits their ability to capture latent fine-grained emotional cues within modalities. On the other hand, prevailing fusion strategies often lack mechanisms to model semantic discrepancies across modalities and to… More >

Palak Bari, Gurnur Bedi, Khushi Joshi, Anupama Jawale^*

Journal on Artificial Intelligence, Vol.7, pp. 499-508, 2025, DOI:10.32604/jai.2025.072531 - 17 November 2025

Abstract This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories, balanced across sarcastic and non-sarcastic classes. A sequential baseline model (LSTM) is compared with transformer-based models (RoBERTa and XLNet), integrated with attention mechanisms. Transformers were chosen for their proven ability to capture long-range contextual dependencies, whereas LSTM serves as a traditional benchmark for sequential modeling. Experimental results show that RoBERTa achieves 0.87 accuracy, XLNet 0.83, and LSTM 0.52. These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection. Future work will incorporate multimodal More >

Dan Wang¹, Zhoubin Li¹, Yuze Xia^1,2,*, Zhenhua Yu^1,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5823-5845, 2025, DOI:10.32604/cmc.2025.071656 - 23 October 2025

Abstract Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs. However, existing models suffer from weak aspect-image alignment, modality imbalance dominated by textual signals, and limited reasoning for implicit or ambiguous sentiments requiring external knowledge. To address these issues, we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network (GLAMSNet). First of all, an input encoding module is employed to construct modality-specific and aspect-aware representations. Subsequently, we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment. Building upon these components, More >

Chenquan Gan^1,2,*, Xu Liu¹, Yu Tang², Xianrong Yu³, Qingyi Zhu¹, Deepak Kumar Jain⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5399-5421, 2025, DOI:10.32604/cmc.2025.068126 - 23 October 2025

Abstract Multimodal sentiment analysis aims to understand emotions from text, speech, and video data. However, current methods often overlook the dominant role of text and suffer from feature loss during integration. Given the varying importance of each modality across different contexts, a central and pressing challenge in multimodal sentiment analysis lies in maximizing the use of rich intra-modal features while minimizing information loss during the fusion process. In response to these critical limitations, we propose a novel framework that integrates spatial position encoding and fusion embedding modules to address these issues. In our model, text is… More >

Xiaofang Jin, Yiran Li^*, Yuying Yang

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5309-5326, 2025, DOI:10.32604/cmc.2025.067812 - 23 October 2025

Abstract Given the importance of sentiment analysis in diverse environments, various methods are used for image sentiment analysis, including contextual sentiment analysis that utilizes character and scene relationships. However, most existing works employ character faces in conjunction with context, yet lack the capacity to analyze the emotions of characters in unconstrained environments, such as when their faces are obscured or blurred. Accordingly, this article presents the Adaptive Multi-Channel Sentiment Analysis Network (AMSA), a contextual image sentiment analysis framework, which consists of three channels: body, face, and context. AMSA employs Multi-task Cascaded Convolutional Networks (MTCNN) to detect More >

Luyu Ma^1,*, Xiu Cheng^1,*, Zongyan Xing¹, Yue Wu¹, Weiwei Jiang²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3921-3943, 2025, DOI:10.32604/cmc.2025.067786 - 23 September 2025

Abstract Green consumption (GC) are crucial for achieving the Sustainable Development Goals (SDGs). However, few studies have explored public attitudes toward GC using social media data, missing potential public concerns captured through big data. To address this gap, this study collects and analyzes public attention toward GC using web crawler technology. Based on the data from Sina Weibo, we applied RoBERTa, an advanced NLP model based on transformer architecture, to conduct fine-grained sentiment analysis of the public’s attention, attitudes and hot topics on GC, demonstrating the potential of deep learning methods in capturing dynamic and contextual… More >

Erlin Tian¹, Shuai Zhao^2,*, Min Huang², Yushan Pan^3,4, Yihong Wang^3,4, Zuhe Li¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1427-1456, 2025, DOI:10.32604/cmc.2025.066476 - 29 August 2025

Abstract With the increasing importance of multimodal data in emotional expression on social media, mainstream methods for sentiment analysis have shifted from unimodal to multimodal approaches. However, the challenges of extracting high-quality emotional features and achieving effective interaction between different modalities remain two major obstacles in multimodal sentiment analysis. To address these challenges, this paper proposes a Text-Gated Interaction Network with Inter-Sample Commonality Perception (TGICP). Specifically, we utilize a Inter-sample Commonality Perception (ICP) module to extract common features from similar samples within the same modality, and use these common features to enhance the original features of… More >