Research Article
A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words
Table 11
Performance of four different sentiment lexicons for sentiment word datasets4.
| ā | ā | | | | Average |
| | | 0.4855 | 0.6315 | 0.5648 | 0.5606 | | 0.4853 | 0.6365 | 0.5720 | 0.5646 | | 0.4854 | 0.6340 | 0.5684 | 0.5626 | AC | 0.4855 | 0.6315 | 0.5648 | 0.5606 |
| | | 0.5010 | 0.6395 | 0.58275 | 0.5744 | | 0.5010 | 0.6421 | 0.5841 | 0.5757 | | 0.5010 | 0.6408 | 0.5835 | 0.5751 | AC | 0.5010 | 0.6395 | 0.58275 | 0.5744 |
| - | | 0.5445 | 0.6735 | 0.6058 | 0.6079 | | 0.5449 | 0.6758 | 0.6125 | 0.6111 | | 0.5447 | 0.6746 | 0.6091 | 0.6095 | AC | 0.5445 | 0.6735 | 0.6058 | 0.6079 |
| - | | 0.5453 | 0.6785 | 0.6060 | 0.6099 | | 0.5456 | 0.6789 | 0.6001 | 0.6082 | | 0.5454 | 0.6787 | 0.6030 | 0.6090 | AC | 0.5453 | 0.6785 | 0.6060 | 0.6099 |
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