A soft voting ensemble learning-based approach for multimodal sentiment analysis
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07451-7.pdf
Reference42 articles.
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2. Yang X, Feng S, Wang D, Zhang Y (2020) Image-text multimodal emotion classification via multi-view attentional network. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2020.3035277
3. Soleymani M, Garcia D, Jou B, Schuller B, Chang SF, Pantic M (2017) A survey of multimodal sentiment analysis. Image Vis Comput 65:3–14. https://doi.org/10.1016/j.imavis.2017.08.003
4. Xu N, Mao W (2017) A residual merged neutral network for multimodal sentiment analysis. In: 2017 IEEE 2nd ınternational conference on big data analysis, ICBDA 2017, pp 6–10. https://doi.org/10.1109/ICBDA.2017.8078794
5. Poria S, Majumder N, Hazarika D, Cambria E, Gelbukh A, Hussain A (2018) Multimodal sentiment analysis: addressing key issues and setting up the baselines. IEEE Intell Syst 33(6):17–25. https://doi.org/10.1109/MIS.2018.2882362
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