Research Article
A Fuzzy Computing Model for Identifying Polarity of Chinese Sentiment Words
Table 9
Performance of four different sentiment lexicons for sentiment word datasets2.
| ā | ā | | | | Average |
| | | 0.62375 | 0.64675 | 0.6365 | 0.6357 | | 0.6542 | 0.6906 | 0.6936 | 0.6795 | | 0.6386 | 0.6680 | 0.6638 | 0.6568 | AC | 0.62375 | 0.64675 | 0.6365 | 0.6357 |
| | | 0.6270 | 0.66325 | 0.65075 | 0.6470 | | 0.6552 | 0.6985 | 0.7017 | 0.6851 | | 0.6408 | 0.6804 | 0.6753 | 0.6655 | AC | 0.6270 | 0.66325 | 0.65075 | 0.6470 |
| - | | 0.68575 | 0.67375 | 0.6645 | 0.6747 | | 0.6968 | 0.6961 | 0.7042 | 0.6990 | | 0.6912 | 0.6847 | 0.6838 | 0.6866 | AC | 0.68575 | 0.67375 | 0.6645 | 0.6747 |
| - | | 0.7045 | 0.6800 | 0.66975 | 0.6848 | | 0.7183 | 0.6940 | 0.7058 | 0.7060 | | 0.7113 | 0.6869 | 0.6873 | 0.6952 | AC | 0.7045 | 0.6800 | 0.66975 | 0.6848 |
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