From: Readers’ affect: predicting and understanding readers’ emotions with deep learning
Statistics | REN-10k | RENh-4k | SemEval-2007 |
---|---|---|---|
Source | Rappler | Rappler | The New York Times, CNN, BBC, Google News |
Year span | 2014 to 2019 | 2015 to 2018 | – |
Length | Short-text (after pre-processing) | Short-text | Short-text |
Number of news documents | 10,272 | 4000 | 1246 (valid documents after pre-processing) |
Total number of words | 305,160 | 124,172 | 6364 |
Number of unique words | 27,749 | 13,260 | 3286 |
Average words per document | 29.70 | 31.043 | 5.09 |
Average sentences per document | 1.18 | 1.1875 | 1.00 |
Number of annotations | 528,327 | 242,680 | 6 (annotators) |
Mean percentage of votes for each emotion class | Anger: 0.2124 | Anger: 0.3388 | Anger: 0.1013 |
Fear: 0.0658 | Fear: 0.1475 | Fear: 0.1639 | |
Joy: 0.4215 | Joy: 0.3137 | Joy: 0.2860 | |
Sadness: 0.1399 | Sadness: 0.0781 | Sadness: 0.2069 | |
Surprise: 0.1606 | Surprise: 0.1218 | Surprise: 0.2416 | |
Number of articles associated with each emotion class | Anger: 6904 | Anger: 3068 | Anger: 652 |
Fear: 4233 | Fear: 1850 | Fear: 820 | |
Joy: 8917 | Joy: 3267 | Joy: 786 | |
Sadness: 5972 | Sadness: 2489 | Sadness: 863 | |
Surprise: 6431 | Surprise: 2312 | Surprise: 1102 |