Article: Digital media operations prediction based on user sentiment analysis and deep neural networks Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.46 pp.20 - 36 Abstract: Against the backdrop of increasingly fierce competition in the digital media industry, how to accurately predict operational effects has become the key to enhancing the competitiveness of media. Aiming at the problem of fusion redundancy caused by the existing research ignoring the mutual influence among cross-modalities, this paper first uses BERT and the improved visual transformer model to extract text and image features respectively. Then, cross-modal shared computing is utilised to enhance the complementarity among the features of each modal. Introduce text gating enhancement and use text information as prior knowledge to guide and improve the representation of image characteristics. Eventually, the fused characteristics are input into the classification layer for prediction. Experimental outcome indicates that the prediction accuracy rate of the suggested approach is 95.3%, which is at least 2.2% higher, significantly improving the accuracy of predicting the operation effect of digital media. Inderscience Publishers - linking academia, business and industry through research

Title: Digital media operations prediction based on user sentiment analysis and deep neural networks

Authors: Xinyu Chen; Zhenbin Huang

Addresses: School of Art, Design and Media, Sanda University, Shanghai, 201209, China ' School of Art, Design and Media, Sanda University, Shanghai, 201209, China

Abstract: Against the backdrop of increasingly fierce competition in the digital media industry, how to accurately predict operational effects has become the key to enhancing the competitiveness of media. Aiming at the problem of fusion redundancy caused by the existing research ignoring the mutual influence among cross-modalities, this paper first uses BERT and the improved visual transformer model to extract text and image features respectively. Then, cross-modal shared computing is utilised to enhance the complementarity among the features of each modal. Introduce text gating enhancement and use text information as prior knowledge to guide and improve the representation of image characteristics. Eventually, the fused characteristics are input into the classification layer for prediction. Experimental outcome indicates that the prediction accuracy rate of the suggested approach is 95.3%, which is at least 2.2% higher, significantly improving the accuracy of predicting the operation effect of digital media.

Keywords: digital media; operation effect prediction; sentiment analysis; convolutional neural network; vision transformer.

DOI: 10.1504/IJICT.2025.150597

International Journal of Information and Communication Technology, 2025 Vol.26 No.46, pp.20 - 36

Received: 30 Jun 2025
Accepted: 23 Jul 2025
Published online: 17 Dec 2025 *

Title: Digital media operations prediction based on user sentiment analysis and deep neural networks

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