Article: Integrating multi-modal emotion recognition with strategy generation: a transformer approach to sustainable marketing Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2026 Vol.52 No.5 pp.36 - 47 Abstract: This paper proposes the multi-modal emotion perception and strategy optimisation transformer (MEPSO-Transformer), a dual-task architecture designed to jointly perform emotion classification and green marketing strategy recommendation based on heterogeneous user data. The model incorporates modality-specific encoders, hierarchical gated cross-attention fusion, and two parallel decoders, and is trained under a multi-task learning scheme. To evaluate its effectiveness, experiments are conducted on three datasets: CMU-MOSEI, MELD, and a constructed domain-specific dataset, GreenPromo-Emotion, which contains 6,200 multi-modal samples annotated with seven emotional categories and five predefined marketing strategies. Results show that MEPSO-Transformer achieves an average accuracy of 84.3% and a macro-F1 score of 81.2% on CMU-MOSEI, outperforming the best baseline by +2.3% and +2.6% respectively. On MELD, the model attains a 75.0% macro-F1 and a Hit@1 score of 65.7%. These findings demonstrate the model's applicability to sustainable marketing tasks requiring real-time affective understanding and strategy selection. Inderscience Publishers - linking academia, business and industry through research

Title: Integrating multi-modal emotion recognition with strategy generation: a transformer approach to sustainable marketing

Authors: Anqi Wu; Jing Wang; Jie Zhang; Wei Qiu

Addresses: Faculty of Applied Sciences, Macao Polytechnic University, Macau, 999078, China ' School of Business, Shandong Xiehe University, Jinan, 250109, Shandong, China ' School of Business, Shandong Xiehe University, Jinan, 250109, Shandong, China ' School of Computer Science, Shandong Xiehe University, Jinan, 250109, Shandong, China

Abstract: This paper proposes the multi-modal emotion perception and strategy optimisation transformer (MEPSO-Transformer), a dual-task architecture designed to jointly perform emotion classification and green marketing strategy recommendation based on heterogeneous user data. The model incorporates modality-specific encoders, hierarchical gated cross-attention fusion, and two parallel decoders, and is trained under a multi-task learning scheme. To evaluate its effectiveness, experiments are conducted on three datasets: CMU-MOSEI, MELD, and a constructed domain-specific dataset, GreenPromo-Emotion, which contains 6,200 multi-modal samples annotated with seven emotional categories and five predefined marketing strategies. Results show that MEPSO-Transformer achieves an average accuracy of 84.3% and a macro-F1 score of 81.2% on CMU-MOSEI, outperforming the best baseline by +2.3% and +2.6% respectively. On MELD, the model attains a 75.0% macro-F1 and a Hit@1 score of 65.7%. These findings demonstrate the model's applicability to sustainable marketing tasks requiring real-time affective understanding and strategy selection.

Keywords: multi-modal emotion recognition; green marketing; strategy optimisation; transformer; sustainable AI; affective computing; multi-task learning.

DOI: 10.1504/IJAHUC.2026.154096

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.5, pp.36 - 47

Received: 01 Aug 2025
Accepted: 22 Dec 2025
Published online: 12 Jun 2026 *

Title: Integrating multi-modal emotion recognition with strategy generation: a transformer approach to sustainable marketing

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