Title: Two-way sentiment analysis method of multimedia information based on deep learning algorithm

Authors: Yingjie Liu; Baopeng Kan

Addresses: School of Communication, Soochow University, Suzhou, 215000, China ' School of Information Engineering, Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, 215123, China

Abstract: Emotional analysis can better understand the public's emotions and needs, and make better decisions based on the analysis results. However, there is still a lack of effective analysis methods in practical applications. Therefore, the study utilises a bidirectional emotion classification mechanism based on deep learning, and uses time series algorithms to predict the development trend of users' bidirectional emotions. A bidirectional emotion analysis method for multimedia information based on deep learning algorithms is proposed. The results show that the accuracy of emotional judgment in the analysis model is 82.5%, which is 11.1% higher than the machine learning model. At the same time, the prediction accuracy of the prediction model is around 84%, which is significantly better than the comparison method. This indicates that the bidirectional emotion model constructed through research can accurately analyse user emotions and provide reference for making development decisions in the multimedia field.

Keywords: deep learning; two-way sentiment analysis; attention mechanism; time series; sentiment prediction.

DOI: 10.1504/IJCSYSE.2026.151333

International Journal of Computational Systems Engineering, 2026 Vol.10 No.1/2/3/4, pp.58 - 68

Received: 01 Aug 2023
Accepted: 08 Sep 2023
Published online: 26 Jan 2026 *

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