part 4

multinomialNaiveBayes_part4.py

# calculate a score for a given class
def calculate_class_score(sentence, class_name, show_details=True):
    score = 0
    # tokenize each word in our new sentence
    for word in nltk.word_tokenize(sentence):
        # check to see if the stem of the word is in any of our classes
        if stemmer.stem(word.lower()) in class_words[class_name]:
            # treat each word with same weight
            score += 1
            
            if show_details:
                print ("   match: %s" % stemmer.stem(word.lower() ))
    return score