Fig 1.
Public opinion sentiment analysis.
Table 1.
Analysis of cross-lingual sentiment detection literature.
Fig 2.
XLM-RoBERTa: Aligning semantic spaces across languages.
Fig 3.
BiLSTM-attention algorithm.
Table 2.
Cross-lingual sentiment analysis algorithm parameter configuration table.
Table 3.
Attention mechanism case analysis: Sentiment expression patterns.
Fig 4.
Model sentiment detection ACC performance comparison in single language.
Fig 5.
Model sentiment detection loss comparison in single language.
Fig 6.
Model sentiment detection confusion matrix heatmap in single language.
Table 4.
Statistical analysis of single language sentiment classification: Ablation study.
Table 5.
Comprehensive performance metrics for single language tasks.
Fig 7.
Model sentiment detection ACC performance comparison in multilingual setting.
Fig 8.
Model sentiment detection loss comparison in multilingual setting.
Fig 9.
Model sentiment detection confusion matrix heatmap in multilingual setting.
Table 6.
Statistical analysis of multilingual sentiment classification performance.
Table 7.
Comprehensive performance metrics for multilingual tasks.
Table 8.
Computational efficiency comparison of different model configurations.
Table 9.
Recent cross-lingual sentiment analysis models performance improvement comparison.