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TEDT: Transformer-Based Encoding–Decoding Translation Network for Multimodal Sentiment Analysis

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Abstract

Multimodal sentiment analysis is a popular and challenging research topic in natural language processing, but the impact of individual modal data in videos on sentiment analysis results can be different. In the temporal dimension, natural language sentiment is influenced by nonnatural language sentiment, which may enhance or weaken the original sentiment of the current natural language. In addition, there is a general problem of poor quality of nonnatural language features, which essentially hinders the effect of multimodal fusion. To address the above issues, we proposed a multimodal encoding–decoding translation network with a transformer and adopted a joint encoding–decoding method with text as the primary information and sound and image as the secondary information. To reduce the negative impact of nonnatural language data on natural language data, we propose a modality reinforcement cross-attention module to convert nonnatural language features into natural language features to improve their quality and better integrate multimodal features. Moreover, the dynamic filtering mechanism filters out the error information generated in the cross-modal interaction to further improve the final output. We evaluated the proposed method on two multimodal sentiment analysis benchmark datasets (MOSI and MOSEI), and the accuracy of the method was 89.3% and 85.9%, respectively. In addition, our method outperformed the current state-of-the-art methods. Our model can greatly improve the effect of multimodal fusion and more accurately analyze human sentiment.

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Data Availability

Data is available in a public repository. The data that support the findings of this study are available in the following: MOSI–https://www.jianguoyun.com/p/DV1YEgUQvP35Bhim9d8D and MOSEI–https://www.jianguoyun.com/p/DVw4E7EQvP35Bhir9d8D.

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Funding

This work was supported by the National Natural Science Foundation of China 61962057, Autonomous Region Key R&D Project 2021B01002, and the National Natural Science Foundation of China under grant U2003208.

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Authors and Affiliations

  1. School of Software, University of Xinjiang, Ürümqi, Xinjiang, China

    Fan Wang, Shengwei Tian, Jing Liu, Junwen Wang, Kun Li & Yongtao Wang

  2. Network and Information Center, University of Xinjiang, Ürümqi, Xinjiang, China

    Long Yu

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Correspondence to Shengwei Tian.

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Wang, F., Tian, S., Yu, L. et al. TEDT: Transformer-Based Encoding–Decoding Translation Network for Multimodal Sentiment Analysis. Cogn Comput 15, 289–303 (2023). https://doi.org/10.1007/s12559-022-10073-9

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