Article: A deep learning framework for sentiment-aware visual communication in digital media systems Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.95 pp.1 - 24 Abstract: The fast-changing digital media systems have enhanced the necessity to have smart visual communication systems that comprehend and react to human sentiments. This paper proposes a new deep sentiment-aware visual communication framework (DSVCF), which combines deep learning, affective computing, and adaptive communication control to improve human-machine communication. The proposed framework works in three interdependent modules: 1) a multimodal sentiment perception network (MSPN), which combines visual, textual and auditory information with the help of a dual-channel transformer convolutional neural network (DT-CNN) to extract emotion-rich representations; 2) a adaptive emotion modulation layer (AEML), which uses a dynamic attention generative mechanism (DA-GM) to control visual content presentation depending on the detected affective states; 3) an optimised communication control module. The DSVCF fills the gap between the understanding of sentiment and visual content optimisation, enabling digital systems to produce emotionally resonant messages and communications that relate to the context. Not only does this framework promote the affective deep learning to applications within digital communication systems, but it also forms a basis of sentiment-based control architecture in multimedia environments. The suggested model is both theoretically and practically valuable as it promotes the responsiveness of sentiment, interpretability, and effectiveness of communication within the intricate digital ecosystem. Inderscience Publishers - linking academia, business and industry through research

Title: A deep learning framework for sentiment-aware visual communication in digital media systems

Authors: HaiHong Xie; Jun Ma

Addresses: Department of Visual Communication, Zhengzhou Academy of Fine Arts, Zhengzhou, 450451, China ' Department of Visual Communication, Zhengzhou Academy of Fine Arts, Zhengzhou, 450451, China

Abstract: The fast-changing digital media systems have enhanced the necessity to have smart visual communication systems that comprehend and react to human sentiments. This paper proposes a new deep sentiment-aware visual communication framework (DSVCF), which combines deep learning, affective computing, and adaptive communication control to improve human-machine communication. The proposed framework works in three interdependent modules: 1) a multimodal sentiment perception network (MSPN), which combines visual, textual and auditory information with the help of a dual-channel transformer convolutional neural network (DT-CNN) to extract emotion-rich representations; 2) a adaptive emotion modulation layer (AEML), which uses a dynamic attention generative mechanism (DA-GM) to control visual content presentation depending on the detected affective states; 3) an optimised communication control module. The DSVCF fills the gap between the understanding of sentiment and visual content optimisation, enabling digital systems to produce emotionally resonant messages and communications that relate to the context. Not only does this framework promote the affective deep learning to applications within digital communication systems, but it also forms a basis of sentiment-based control architecture in multimedia environments. The suggested model is both theoretically and practically valuable as it promotes the responsiveness of sentiment, interpretability, and effectiveness of communication within the intricate digital ecosystem.

Keywords: deep learning; visual communication; sentiment analysis; affective computing; reinforcement learning; digital media systems.

DOI: 10.1504/IJICT.2026.156129

International Journal of Information and Communication Technology, 2026 Vol.27 No.95, pp.1 - 24

Received: 03 Apr 2026
Accepted: 28 Apr 2026
Published online: 04 Sep 2026 *

Title: A deep learning framework for sentiment-aware visual communication in digital media systems

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