Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Graph Classification | Accuracy68.7 | 57 | ||
| Maximum Clique | Twitter MC instances (static) | Mean ApR1 | 38 | |
| Target-dependent sentiment classification | Twitter (test) | Accuracy78.61 | 31 | |
| Visual Emotion Recognition | Twitter II | Accuracy74.9 | 26 | |
| Visual Emotion Recognition | Twitter I | Accuracy82.9 | 26 | |
| Multimodal Named Entity Recognition | Twitter 2017 | F1 Score88.07 | 22 | |
| Multimodal Named Entity Recognition | Twitter 2015 | F1 Score79.33 | 21 | |
| Multimodal Named Entity Recognition | Twitter-15 (test) | F1 Score79.21 | 21 | |
| Sentiment Analysis | Accuracy80.59 | 20 | ||
| Rumour Detection | Twitter 15 | Accuracy92.3 | 19 | |
| Aspect term-polarity pair extraction | Twitter ST (test) | F1 Score51.37 | 18 | |
| Aspect-Based Sentiment Analysis | Twitter (test) | Acc77.17 | 17 | |
| Aspect Polarity Classification | F1 Score (APC)75.16 | 17 | ||
| Bot Detection | Twitter tweet-level | Precision96 | 17 | |
| Crisis response generation | Professionalism99 | 16 | ||
| Node Classification | Twitter HOMO (test) | Mean Accuracy75.92 | 15 | |
| Node Classification | Twitter HET (test) | Mean Accuracy0.7317 | 15 | |
| Multimodal Aspect Sentiment Classification | TWITTER 2015 | F1 Score78.1 | 15 | |
| Aspect-Based Sentiment Analysis | Accuracy78.75 | 14 | ||
| Multimodal Named Entity Recognition | Twitter 17 (test) | F1 Score90.67 | 14 | |
| Multimodal Aspect Sentiment Classification | TWITTER 2017 | F1 Score76.8 | 14 | |
| Named Entity Recognition | Twitter NER | F1 Score77.97 | 14 | |
| Text Categorization | TWITTER (test) | Classification Error25.42 | 14 | |
| Depression Diagnosis | Twitter (5-fold Cross-Validation) | Accuracy94.3 | 13 | |
| Graph Classification | Twitter GraphOOD (test) | Accuracy59.33 | 13 |
Showing 25 of 110 rows