Automatic Construction and Global Optimization of a Multisentiment Lexicon

Table 5

Sentiment classification based on different lexicons.

Result of positive text
Lexicon	Precision	Recall
NTUSD	0.603	0.375	0.462
HowNet	0.728	0.540	0.620
DUT	0.721	0.552	0.593
SentiRuc (before disambiguation)	0.744	0.588	0.657
SentiRuc (after disambiguation)	0.782	0.678	0.726
Result of negative text
Lexicon	Precision	Recall
NTUSD	0.480	0.319	0.383
HowNet	0.611	0.451	0.519
DUT	0.572	0.445	0.501
SentiRuc (before disambiguation)	0.633	0.468	0.538
SentiRuc (after disambiguation)	0.671	0.589	0.627