A Sentiment Analysis Method for Big Social Online Multimodal Comments Based on Pre-trained Models
Author:
Funder
Key Project of Science and Technology Research of Chongqing Education Commission
project from the Rector of the Silesian University of Technology
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11036-024-02303-1.pdf
Reference24 articles.
1. Liu H, Xiang MA, Zhang L, Rujin HE (2023) Aspect-based sentiment analysis model integrating match-lstm network and grammatical distance. J Comput Appl 43(1):45–50
2. Alahmary R, Al-Dossari H (2023) A semiautomatic annotation approach for sentiment analysis. J Inf Sci 49(2):398–410
3. Kota VR, Munisamy SD (2022) High accuracy offering attention mechanisms based deep learning approach using cnn/bi-lstm for sentiment analysis. Int J Intell Comput Cybern 15(1):61–74
4. Mewada A, Dewang RK (2022) Sa-asba: a hybrid model for aspect-based sentiment analysis using synthetic attention in pre-trained language bert model with extreme gradient boosting. J Supercomput 79(5):5516–5551
5. Pradhan A, Senapati MR, Sahu PK (2023) A multichannel embedding and arithmetic optimized stacked bi-gru model with semantic attention to detect emotion over text data. Appl Intell 53(7):7647–7664
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