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Emotional analysis and semantic understanding of multimodal network language data

Chen Weimiao

Chen Weimiao

Guangdong Baiyun UniversityGuangzhou Guangdong, China
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Weimiao, Chen
  
31 mars 2025
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Publié en ligne: 31 mars 2025
Reçu: 05 nov. 2024
Accepté: 13 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0818
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© 2025 Chen Weimiao, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Overall framework of model
Overall framework of model

Figure 2.

Extracted MFCC features
Extracted MFCC features

Figure 3.

Transformer model structure
Transformer model structure

Figure 4.

Schematic diagram of ResNet residual block
Schematic diagram of ResNet residual block

Figure 5.

Multi-head attention feature fusion mechanism
Multi-head attention feature fusion mechanism

Figure 6.

Performance comparison of different models in emotional analysis tasks
Performance comparison of different models in emotional analysis tasks

Figure 7.

Confusion matrix of model on data set
Confusion matrix of model on data set

Figure 8.

Semantic tags identified by the model on the data set
Semantic tags identified by the model on the data set

Performance of the model in semantic tag recognition

index value
precision 79.3
Recall rate 76.8
F1 value 78.0

Overall performance of different model in affective analysis task

types of models Accuracy Recall F1 values
Text only 78.6% 77.8% 78.2%
Image only 80.5% 83.2% 81.8%
Audio only 82.1% 81.4% 81.7%
Multimodal fusion 85.2% 86.7% 85.0%
Langue:
Anglais
Périodicité:
1 fois par an
Sujets de la revue:
Sciences de la vie, Sciences de la vie, autres, Mathématiques, Mathématiques appliquées, Mathématiques générales, Physique, Physique, autres
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