[Py3.5] Classify SPAM and HAM using Naive Bayes w/ NLTK

/*******************************************************************************************************************
-- Title : [Py3.5] Classify SPAM and HAM using Naive Bayes w/ NLTK
-- Reference : clarkgrubb.com
-- Key word : naivebayes naive bayes word_token word tokenizer word tokenization word count nltk
                  word embedding word feature spam mail ham classfication classifier word frequency 스펨 햄
                  나이브 베이즈 분류기 단어 토큰 토크나이저 토크나이징 단어 빈도수 단어 빈도 단어 임베딩
*******************************************************************************************************************/

■ Scripts

import nltk
 
# =======================================
# -- Sourcing
# =======================================
data = [('Hi John, I will see you at 9:00pm', 'ham'),
        ('Buy Viagra at low, low prices.  One time offer.', 'spam'),
        ('Your Amazon Order is for the book "Harry Potter"', 'ham'),
        ('Earn a million dollars by working at home.', 'spam')]
 
print(type(data))
print(data)
print("... source", "." * 100, "\n")
 
 
# =======================================
# -- Word_token & Word featuring
# =======================================
def features(s):
    dict_vec = {}
    for wtoken in nltk.tokenize.word_tokenize(s):
        dict_vec[wtoken] = 1 if wtoken not in dict_vec else dict_vec[wtoken] + 1
    return dict_vec
 
processed_data = [(features(tup[0]), tup[1]) for tup in data]
 
print(type(processed_data))
print(processed_data)
print(",,, word_featuring", "," * 100, "\n")
 
 
# =======================================
# -- Training and Testing
# =======================================
 
# -- Training
# -- nltk.NaiveBayesClassifier.train( [( {'John': 1, ... }, 'ham'), ...] )
classifier = nltk.NaiveBayesClassifier.train(processed_data)
 
# -- Testing
print(classifier.classify(features("This is good Viagra!")))
print(classifier.classify(features("We will meet you at 7pm. Take care")))