The algorithm

We use the function list_words() to get a list of unique words with more than three characters in lower case:

def list_words(text): 
    words = [] 
    words_tmp = text.lower().split() 
    for w in words_tmp: 
        if w not in words and len(w) > 3: 
            words.append(w) 
    return words 

Tip

For a more advanced term-document matrix, we can use the Python textmining package from: https://pypi.python.org/pypi/textmining/1.0

The training() function creates variables to store the data needed for the classification. The c_words variable is a dictionary with the unique words and its number of occurrences in the text (frequency) by category. The c_categories variable stores a dictionary of each category and its number of texts. Finally, c_text and c_total_words store the total count of texts and words, respectively:

def training(texts): 
    c_words ={} 
    c_categories ={} 
    c_texts = 0 
    c_total_words =0 
    #add the classes to the categories 
    for t in texts...