Research map / Label-efficient and robust learning / Recognition and long-tail
Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add topic factor to self-spreading factor (ablation) | TSSM · Twitter3 and Twitter5, neutral class | precision | Helped |
| add interest-based spreading factor (without interest term) (ablation) | TSSM (Self+Topic) · Twitter3 and Twitter5, negative class | precision | Helped |
| add the user-interest refinement term to the spreading factor (ablation) | TSSM (T+S+I) · Twitter3 and Twitter5 | precision | Helped |
| use TSSM instead of EIC (retweet-based cascade) | sentiment status prediction · Twitter5, negative class | F1-score: 0.1520 -> 0.3844 | Helped |
| use TSSM instead of logistic regression on topic features | sentiment status prediction · Twitter3, positive class | F1-score: 0.3289 -> 0.6012 | Helped |
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