问题描述
当我们在斯坦福演示页面上测试时：
When we test it on Stanford demo page: http://nlp.stanford.edu:8080/sentiment/rntnDemo.html
它为树提供每个节点的情绪分数如下：
it gives the tree with the sentiment score of each node as below:
我正在尝试使用命令在我的本地系统上测试它：
I am trying to test it on my local system using command:
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 已
这部电影并不关心聪明，机智或任何其他智能幽默。
那些在美丽的事物中找到丑陋意义的人是腐败而不迷人。
产生结果：
任何人都可以告诉我为什么它为空？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。
Can anyone please tell me why it is null? Or maybe I'm making any mistake in execution? My purpose is to analyze the text and get the sentiment result with the score.
推荐答案
您使用的文件是错误的命令不完整。下面是你应该使用的命令。
The file you are using is wrong and also the command is incomplete. Below is the command you should be using.
java -cp*edu.stanford.nlp.sentiment.Evaluate -model edu / stanford / nlp / models / sentiment /sentiment.ser.gz-treebank test.txt
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treebank
and text.txt file does not contain plain sentence, rather it contains treebank
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
希望这会有所帮助:) !!
Hope this helps :) !!
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