使用stanford core nlp java代码获得情绪分析结果

当我们在斯坦福演示页面上测试它时： http ： //nlp.stanford.edu ： 8080/sentiment/rntnDemo.html

它给树提供每个节点的情感分数如下：

我试图使用命令在我的本地系统上测试它：

H:\Drive E\Stanford\stanfor-corenlp-full-2013~>java -cp "*" edu.stanford.nlp.sen timent.Evaluate edu/stanford/nlp/models/sentiment/sentiment.ser.gz test.txt 

text.txt有

This movie doesn't care about cleverness, wit or any other kind of intelligent humor. Those who find ugly meanings in beautiful things are corrupt without being charming.

结果如下：

任何人都可以告诉我为什么它是空的？ 或许我在执行中犯了什么错误？ 我的目的是分析文本并用分数得到情绪结果。

Answer 1

您使用的文件错误，命令也不完整。 以下是您应该使用的命令。

java -cp“*”edu.stanford.nlp.sentiment.Evaluate -model edu / stanford / nlp / models / sentiment / sentiment.ser.gz -treebank test.txt

和text.txt文件不包含普通句子，而是包含树库

例如

 (2 (3 (3 Effective) (2 but)) (1 (1 too-tepid) (2 biopic))) (3 (3 (2 If) (3 (2 you) (3 (2 sometimes) (2 (2 like) (3 (2 to) (3 (3 (2 go) (2 (2 to) (2 (2 the) (2 movies)))) (3 (2 to) (3 (2 have) (4 fun))))))))) (2 (2 ,) (2 (2 Wasabi) (3 (3 (2 is) (2 (2 a) (2 (3 good) (2 (2 place) (2 (2 to) (2 start)))))) (2 .))))) (4 (4 (4 (3 (2 Emerges) (3 (2 as) (3 (2 something) (3 rare)))) (2 ,)) (4 (2 (2 an) (2 (2 issue) (2 movie))) (3 (2 that) (3 (3 (2 's) (4 (3 (3 (2 so) (4 honest)) (2 and)) (3 (2 keenly) (2 observed)))) (2 (2 that) (2 (2 it) (2 (1 (2 does) (2 n't)) (2 (2 feel) (2 (2 like) (2 one)))))))))) (2 .)) (2 (2 (2 The) (2 film)) (3 (3 (3 (3 provides) (2 (2 some) (3 (4 great) (2 insight)))) (3 (2 into) (3 (2 (2 the) (2 (2 neurotic) (2 mindset))) (3 (2 of) (2 (2 (2 (2 (2 all) (2 comics)) (2 --)) (2 even)) (3 (2 those) (4 (2 who) (4 (2 have) (4 (2 reached) (4 (4 (2 the) (3 (2 absolute) (2 top))) (2 (2 of) (2 (2 the) (2 game))))))))))))) (2 .))) 

收到的输出是

 EVALUATION SUMMARY Tested 82600 labels 66258 correct 16342 incorrect 0.802155 accuracy Tested 2210 roots 976 correct 1234 incorrect 0.441629 accuracy Label confusion matrix: rows are gold label, columns predicted label 323 1294 292 99 0 161 5498 2993 602 1 27 2245 51972 2283 21 3 652 2868 7247 228 3 148 282 2140 1218 Root label confusion matrix: rows are gold label, columns predicted label 44 193 23 19 0 39 451 62 81 0 9 190 82 101 7 0 131 30 299 50 0 36 8 255 100 Approximate Negative label accuracy: 0.912008 Approximate Positive label accuracy: 0.930750 Combined approximate label accuracy: 0.923128 Approximate Negative root label accuracy: 0.879081 Approximate Positive root label accuracy: 0.808266 Combined approximate root label accuracy: 0.842756 

希望这可以帮助 ：） ！！