Research map / Label-efficient and robust learning / Recognition and long-tail
A Neural Group-wise Sentiment Analysis Model with Data Sparsity Awareness
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add rating-bias modeling (ablation) | NGSAM · Yelp 2013 | RMSE: 0.7268 -> 0.7163 | Helped |
| Add rating-bias modeling (ablation) | NGSAM · Yelp 2014 | RMSE: 0.7099 -> 0.7031 | Helped |
| Add rating-bias modeling (ablation) | NGSAM · Twitter | RMSE: 1.5890 -> 1.5011 | Helped |
| Add rating-variance modeling (ablation) | NGSAM · Yelp 2013 | RMSE: 0.7396 -> 0.7163 | Helped |
| Add rating-variance modeling (ablation) | NGSAM · Yelp 2014 | RMSE: 0.7211 -> 0.7031 | Helped |
| Add rating-variance modeling (ablation) | NGSAM · Twitter | RMSE: 1.6011 -> 1.5011 | Helped |
| Add group-embedding loss L3 (ablation) | NGSAM · Yelp 2013 | RMSE: 0.7368 -> 0.7163 | Helped |
| Add group-embedding loss L3 (ablation) | NGSAM · Yelp 2014 | RMSE: 0.7177 -> 0.7031 | Helped |
| Add group-embedding loss L3 (ablation) | NGSAM · Twitter | RMSE: 1.5917 -> 1.5011 | Helped |
| Use more than 10 user groups (ablation) | NGSAM on Yelp 2013 · Yelp 2013 | RMSE | No clear effect |
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