Loading a classifier using Pickle?

Question

I am trying run a sentiment analysis. I have managed to use Naive Bayes through nltk to classify a corpus of negative and positive tweets. However I do not want to go through the process of running this classifier every time I run this program so I tried to use pickle to save, and then load into a different script the classifier. However when I try to run the script it returns the error NameError: name classifier is not defined, although I thought it was defined through the def load_classifier():

The code I have atm is below:

import nltk, pickle
from nltk.corpus import stopwords

customstopwords = ['']

p = open('xxx', 'r')
postxt = p.readlines()

n = open('xxx', 'r')
negtxt = n.readlines()

neglist = []
poslist = []

for i in range(0,len(negtxt)):
    neglist.append('negative')

for i in range(0,len(postxt)):
    poslist.append('positive')

postagged = zip(postxt, poslist)
negtagged = zip(negtxt, neglist)


taggedtweets = postagged + negtagged

tweets = []

for (word, sentiment) in taggedtweets:
    word_filter = [i.lower() for i in word.split()]
    tweets.append((word_filter, sentiment))

def getwords(tweets):
    allwords = []
    for (words, sentiment) in tweets:
            allwords.extend(words)
    return allwords

def getwordfeatures(listoftweets):
    wordfreq = nltk.FreqDist(listoftweets)
    words = wordfreq.keys()
    return words

wordlist = [i for i in getwordfeatures(getwords(tweets)) if not i in                  stopwords.words('english')]
wordlist = [i for i in getwordfeatures(getwords(tweets)) if not i in customstopwords]


def feature_extractor(doc):
    docwords = set(doc)
    features = {}
    for i in wordlist:
        features['contains(%s)' % i] = (i in docwords)
    return features


training_set = nltk.classify.apply_features(feature_extractor, tweets)

def load_classifier():
   f = open('my_classifier.pickle', 'rb')
   classifier = pickle.load(f)
   f.close
   return classifier

while True:
    input = raw_input('I hate this film')
    if input == 'exit':
        break
    elif input == 'informfeatures':
        print classifier.show_most_informative_features(n=30)
        continue
    else:
        input = input.lower()
        input = input.split()
        print '\nSentiment is ' + classifier.classify(feature_extractor(input)) + ' in that sentence.\n'

p.close()
n.close()

Any help would be great, the script seems to make it to the print '\nSentiment is ' + classifier.classify(feature_extractor(input)) + ' in that sentence.\n'" before returning the error...

Answer 1

Well, you have declared and defined the load_classifier() method but never called it and assigned a variable using it. That means, by the time, the execution reaches the print '\nSentiment is... ' line, there is no variable names classifier. Naturally, the execution throws an exception.

Add the line classifier = load_classifier() just before while loop. (without any indentation)