Classifying Multinomial Naive Bayes Classifier with Python Example

Hi new here so please bear with me. I am looking for a simple example on how to run a Multinomial Naive Bayes Classifier. I came across this example from StackOverflow:

http://stackoverflow.com/questions/10098533/implementing-bag-of-words-naive-bayes-classifier-in-nltk

import numpy as np
from nltk.probability import FreqDist
from nltk.classify import SklearnClassifier
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline

pipeline = Pipeline([('tfidf', TfidfTransformer()),
                     ('chi2', SelectKBest(chi2, k=1000)),
                     ('nb', MultinomialNB())])
classif = SklearnClassifier(pipeline)

from nltk.corpus import movie_reviews
pos = [FreqDist(movie_reviews.words(i)) for i in movie_reviews.fileids('pos')]
neg = [FreqDist(movie_reviews.words(i)) for i in movie_reviews.fileids('neg')]
add_label = lambda lst, lab: [(x, lab) for x in lst]
#Original code from thread:
#classif.train(add_label(pos[:100], 'pos') + add_label(neg[:100], 'neg'))
classif.train(add_label(pos, 'pos') + add_label(neg, 'neg'))#Made changes here

#Original code from thread:    
#l_pos = np.array(classif.batch_classify(pos[100:]))
#l_neg = np.array(classif.batch_classify(neg[100:]))
l_pos = np.array(classif.batch_classify(pos))#Made changes here
l_neg = np.array(classif.batch_classify(neg))#Made changes here
print "Confusion matrix:\n%d\t%d\n%d\t%d" % (
          (l_pos == 'pos').sum(), (l_pos == 'neg').sum(),
          (l_neg == 'pos').sum(), (l_neg == 'neg').sum())

I received a warning after running this example.

C:\Python27\lib\site-packages\scikit_learn-0.13.1-py2.7-win32.egg\sklearn\feature_selection\univariate_selection.py:327: 
UserWarning: Duplicate scores. Result may depend on feature ordering.There are probably duplicate features, 
or you used a classification score for a regression task.
warn("Duplicate scores. Result may depend on feature ordering."

Confusion matrix:
876 124
63  937

So, my questions are..

1. Can anyone tell me what does this error message means?
2. I made some changes to the original code but why is the confusion matrix results so much higher than the one in the original thread?
3. I have about 5,000 documents. How can I split the documents to 75%:25% for train:test purposes?
4. How can I test the accuracy of this of this classifier?
5. I modified the example above to test with my own dataset. I noticed that if I increase the chi2, k=1000 to k=2000 or higher, it yields a higher results. So, what would be the most ideal value for k?

Thanks!