Get analysis result using nlp code stanford core nlp

Question

When we test it on the Stanford demo page: http://nlp.stanford.edu:8080/sentiment/rntnDemo.html

it gives a tree with a mood score for each node, as shown below:

I am trying to test it on my local system using the 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 It has

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.

which gives the result:

Can someone tell me why this is null? Or maybe I'm wrong in the performance? My goal is to analyze the text and get a result with an assessment.

Answer 1

The file you are using is invalid and the command is incomplete. The following is the command you should use.

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

text.txt ,

.

(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 .)))

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

, :)!!