How to use the textblob.sentiments.NaiveBayesAnalyzer function in textblob

def test_discrete_sentiment(self):
        blob = tb.TextBlob("I feel great today.", analyzer=NaiveBayesAnalyzer())
        assert_equal(blob.sentiment[0], 'pos')

def test_override_analyzer(self):
        b = tb.Blobber(analyzer=NaiveBayesAnalyzer())
        blob = b("How now?")
        blob2 = b("Brown cow")
        assert_true(isinstance(blob.analyzer, NaiveBayesAnalyzer))
        assert_true(blob.analyzer is blob2.analyzer)

def setUp(self):
        self.analyzer = NaiveBayesAnalyzer()

def test_can_use_different_sentanalyzer(self):
        blob = tb.TextBlob("I love this car", analyzer=NaiveBayesAnalyzer())
        assert_true(isinstance(blob.analyzer, NaiveBayesAnalyzer))

def test_can_get_subjectivity_and_polarity_with_different_analyzer(self):
        blob = tb.TextBlob("I love this car.", analyzer=NaiveBayesAnalyzer())
        pattern = PatternAnalyzer()
        assert_equal(blob.polarity, pattern.analyze(str(blob))[0])
        assert_equal(blob.subjectivity, pattern.analyze(str(blob))[1])

#######################

polar=0
subj=0
count=0
pos=0
neg=0
countForSentiment=0

print("CSV_FILE_NAME:"+csv_file)

for t in csv.DictReader(open(csv_file), delimiter=','):
    count=count+1
    into = str(t['title'])
    into = into.decode('utf-8')
    blob = TextBlob(into, analyzer = NaiveBayesAnalyzer())
    print "positivity:",blob.sentiment.p_pos
    print "negativity:",blob.sentiment.p_neg

    pos = pos + blob.sentiment.p_pos
    neg = neg + blob.sentiment.p_neg
    test = TextBlob(into)
    if(test.sentiment.polarity != 0 or test.sentiment.subjectivity !=0 ):
        polar = polar + test.sentiment.polarity
        subj  = subj + test.sentiment.subjectivity
        print "subjectivity:", test.sentiment.subjectivity
        print "polarity:", test.sentiment.polarity
        countForSentiment = countForSentiment+1
        print (str(t['id'])+"\t"+str(test.sentiment.subjectivity)+"\t"+str(test.sentiment.polarity))
    #print test.sentiment.polarity
    #print test.sentiment.subjectivity
    words.extend(t['title'].lower().split()) # <-----------

# -*- coding: utf-8 -*-

import re

from nltk import RegexpParser
from textblob import TextBlob, Word
from textblob.taggers import PatternTagger
from textblob.sentiments import NaiveBayesAnalyzer

pattern_tagger = PatternTagger()
naive_bayes_analyzer = NaiveBayesAnalyzer()


stopwords = [
  "a", "a's", "able", "about", "above", "according", "accordingly", "across", 
  "actually", "after", "afterwards", "again", "against", "ain't", "all", 
  "allow", "allows", "almost", "alone", "along", "already", "also", 
  "although", "always", "am", "among", "amongst", "an", "and", "another", 
  "any", "anybody", "anyhow", "anyone", "anything", "anyway", "anyways", 
  "anywhere", "apart", "appear", "appreciate", "appropriate", "are", 
  "aren't", "around", "as", "aside", "ask", "asking", "associated", "at", 
  "available", "away", "awfully", "b", "be", "became", "because", "become", 
  "becomes", "becoming", "been", "before", "beforehand", "behind", "being", 
  "believe", "below", "beside", "besides", "best", "better", "between", 
  "beyond", "both", "brief", "but", "by", "c", "c'mon", "c's", "came", "can", 
  "can't", "cannot", "cant", "cause", "causes", "certain", "certainly", 
  "changes", "clearly", "co", "com", "come", "comes", "concerning",

def on_status(self, data):
        """"Callback of tweepy to process each new received tweet."""
        tweet = self.clean(data.text)

        if "RT" not in tweet:
            if data.user.location:
                location = self.clean(data.user.location)
            else:
                location = "None"

            if data.lang:
                lang = data.lang
            else:
                lang = "None"

            blob = tb(tweet, analyzer=NaiveBayesAnalyzer())
            print(str(blob.sentiment.p_pos) + "," +
                  str(blob.sentiment.p_neg) + "," +
                  lang + "," +
                  location)
            sys.stdout.flush()
        return True

media_id = get_post_id(insta_username)
    request_url = (BASE_URL + 'media/%s/comments/?access_token=%s') % (media_id, APP_ACCESS_TOKEN)
    print 'GET request url : %s' % (request_url)
    comment_info = requests.get(request_url).json()

    if comment_info['meta']['code'] == 200:

        if len(comment_info['data']):

            #Here's a naive implementation of how to delete the negative comments :)

            for x in range(0, len(comment_info['data'])):
                comment_id = comment_info['data'][x]['id']
                comment_text = comment_info['data'][x]['text']
                total_comments.append(comment_text)  #adding total number of comments to the list
                blob = TextBlob(comment_text, analyzer=NaiveBayesAnalyzer())

                if (blob.sentiment.p_neg > blob.sentiment.p_pos):
                    negative_comments.append(comment_text)  # adding negative comments
                    print Fore.RED+Style.BRIGHT+'Negative comment : %s' % (comment_text)
                    delete_url = (BASE_URL + 'media/%s/comments/%s/?access_token=%s') % (media_id, comment_id, APP_ACCESS_TOKEN)
                    print 'DELETE request url : %s' % (delete_url)
                    delete_info = requests.delete(delete_url).json()

                    if delete_info['meta']['code'] == 200:
                        print 'Comment successfully deleted!\n'
                    else:
                        print 'Unable to delete comment!'
                else:
                    positive_comments.append(comment_text)  #adding positive comments
                    print Fore.BLUE+Style.BRIGHT+'Positive comment : %s\n' % (comment_text)
        else: