Getting sentiment analysis result using stanford core nlp java code

Question

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

it gives the tree with the sentiment score of each node as below:

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 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 yields result:

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.

Answer 1

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

and text.txt file does not contain plain sentence, rather it contains treebank

E.g.

(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 .)))

and output received is

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

Hope this helps :) !!