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machine-learningnlpnltktext-classificationnaivebayes

AttributeError: 'NoneType' object has no attribute 'items' for classifier = nltk.NaiveBayesClassifier.train(training_set)


I am getting this error for AttributeError: 'NoneType' object has no attribute 'items. The code is as follows:

import nltk
import random
from nltk.corpus import movie_reviews
from nltk.classify import NaiveBayesClassifier
from nltk.probability import FreqDist

documents = [ (list(movie_reviews.words(fileid)), category)
            for category in movie_reviews.categories()
                for fileid in movie_reviews.fileids(category)]


random.shuffle(documents)

all_words = []
for w in movie_reviews.words():
all_words.append(w.lower())

all_words =nltk.FreqDist(all_words)

word_features = list(all_words.keys())[:3000]
print (word_features)

def find_features(documents):
    words = set(documents)
    features = {}
    for w in word_features:
        features[w] = (w in words)


featuresets = [(find_features(rev), category) for (rev, category) in 
documents]

training_set = featuresets[:1500]
testing_set = featuresets[1500:]


classifier = nltk.NaiveBayesClassifier.train(training_set)

print ("Naive Bayes Classifier Algo Accuracy: ",nltk.classify.accuracy(classifier, testing_set))*100)

classifier.show_most_informative_features(15)

I have been following a video tutorial and it is the same code there. There it runs fine but here it shows the following error:

AttributeError: 'NoneType' object has no attribute 'items'

for the line

classifier = nltk.NaiveBayesClassifier.train(training_set)

What is the reason and the solution?


Solution

  • You're not returning the list from the function. In your find_feature function use:

    def find_features(documents):
        words = set(documents)
        features = {}
        for w in word_features:
            features[w] = (w in words)
        return features #ADD THIS LINE