A multi-label text sentiment analysis model based on sentiment correlation modeling
2024
ECO-SAM: A New Model for Sentiment Analysis
Sample size: 32445
publication
Evidence: high
Author Information
Author(s): Ni Yingying, Ni Wei
Primary Institution: Shanghai Jiao Tong University
Hypothesis
Can an emotion correlation-enhanced sentiment analysis model improve the accuracy of text emotion recognition?
Conclusion
The ECO-SAM effectively models sentiment semantics and achieves excellent classification performance in public sentiment analysis datasets.
Supporting Evidence
- The ECO-SAM improved precision by 13.33%, recall by 3.69%, and F1 score by 8.44% compared to baseline models.
- The model demonstrated effective emotion correlation modeling through extensive experiments.
- Results showed that emotions with similar connotations have strong semantic correlations.
Takeaway
This study created a new model that helps computers understand emotions in text better, making it easier to tell how people feel based on what they write.
Methodology
The ECO-SAM uses a pre-trained BERT encoder and a self-attention mechanism to analyze text emotions.
Limitations
The model is limited to text-only data and lacks large-scale high-quality training data.
Statistical Information
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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