Calculate which subreddit a title(sentence) should be posted to based on previous titles

titles.py

import json
import math
from collections import Counter

sentence = "I love this game"
json_data = open('reddits.json')
data = json.load(json_data)

stop_words = set(['a', 'about', 'above', 'across', 'after', 'afterwards', 'again', 'against', 'all', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'amoungst', 'amount', 'an', 'and', 'another', 'any', 'anyhow', 'anyone', 'anything', 'anyway', 'anywhere', 'are', 'around', 'as', 'at', 'back', 'be', 'became', 'because', 'become', 'becomes', 'becoming', 'been', 'before', 'beforehand', 'behind', 'being', 'below', 'beside', 'besides', 'between', 'beyond', 'bill', 'both', 'bottom', 'but', 'by', 'call', 'can', 'cannot', 'cant', 'co', 'computer', 'con', 'could', 'couldnt', 'cry', 'de', 'describe', 'detail', 'did', 'do', 'done', 'down', 'due', 'during', 'each', 'eg', 'eight', 'either', 'eleven', 'else', 'elsewhere', 'empty', 'enough', 'etc', 'even', 'ever', 'every', 'everyone', 'everything', 'everywhere', 'except', 'few', 'fifteen', 'fifty', 'fill', 'find', 'fire', 'first', 'five', 'for', 'former', 'formerly', 'forty', 'found', 'four', 'from', 'front', 'full', 'further', 'get', 'give', 'go', 'had', 'has', 'hasnt', 'have', 'he', 'hence', 'her', 'here', 'hereafter', 'hereby', 'herein', 'hereupon', 'hers', 'herself', 'him', 'himself', 'his', 'how', 'however', 'hundred', 'i', 'ie', 'if', 'in', 'inc', 'indeed', 'interest', 'into', 'is', 'it', 'its', 'itself', 'keep', 'last', 'latter', 'latterly', 'least', 'less', 'ltd', 'made', 'many', 'may', 'me', 'meanwhile', 'might', 'mill', 'mine', 'more', 'moreover', 'most', 'mostly', 'move', 'much', 'must', 'my', 'myself', 'name', 'namely', 'neither', 'never', 'nevertheless', 'next', 'nine', 'no', 'nobody', 'none', 'noone', 'nor', 'not', 'nothing', 'now', 'nowhere', 'of', 'off', 'often', 'on', 'once', 'one', 'only', 'onto', 'or', 'other', 'others', 'otherwise', 'our', 'ours', 'ourselves', 'out', 'over', 'own', 'part', 'per', 'perhaps', 'please', 'put', 'rather', 're', 's', 'same', 'see', 'seem', 'seemed', 'seeming', 'seems', 'serious', 'several', 'she', 'should', 'show', 'side', 'since', 'sincere', 'six', 'sixty', 'so', 'some', 'somehow', 'someone', 'something', 'sometime', 'sometimes', 'somewhere', 'still', 'such', 'system', 'take', 'ten', 'than', 'that', 'the', 'their', 'them', 'themselves', 'then', 'thence', 'there', 'thereafter', 'thereby', 'therefore', 'therein', 'thereupon', 'these', 'they', 'thick', 'thin', 'third', 'this', 'those', 'though', 'three', 'three', 'through', 'throughout', 'thru', 'thus', 'to', 'together', 'too', 'top', 'toward', 'towards', 'twelve', 'twenty', 'two', 'un', 'under', 'until', 'up', 'upon', 'us', 'very', 'via', 'was', 'we', 'well', 'were', 'what', 'whatever', 'when', 'whence', 'whenever', 'where', 'whereafter', 'whereas', 'whereby', 'wherein', 'whereupon', 'wherever', 'whether', 'which', 'while', 'whither', 'who', 'whoever', 'whole', 'whom', 'whose', 'why', 'will', 'with', 'within', 'without', 'would', 'yet', 'you', 'your', 'yours', 'yourself', 'yourselves'])

# Calculate word counts for each sub
word_banks = {}
for sub, titles in data.items():
    word_banks[sub] = {}
    word_banks[sub]['single'] = Counter(filter(lambda x: x not in stop_words,' '.join(titles).lower().split(' ')))
    word_banks[sub]['double'] = {}
    title = [title.lower().split(' ') for title in titles]
    for words in title:
        for i in range(len(words)-1):
            if words[i] in word_banks[sub]['double']:
                word_banks[sub]['double'][words[i]].append(words[i+1])
            else:
                word_banks[sub]['double'][words[i]] = [words[i+1]]

#  Calculate probablities for each word for each sub
prob = {}
for sub, words in word_banks.items():
    prob[sub] = {}
    n = sum(words['single'].values()) #  Total words
    vocab = len(words['single']) #  unique word count
    prob[sub]['single'] = {word: (float(word_count + 1)/(n+vocab)) for word, word_count in words['single'].items()}
    prob[sub]['double'] = {}
    for word, word_list in words['double'].items():
        prob[sub]['double'][word] ={second_word: (float(sum([1 for other_word in word_list if second_word==other_word]))/len(word_list)) for second_word in word_list}

#  Score sentence
#  Normally you multiply the probablities but the numbers are too low here so we log and sum them
def find_subreddit(sentence):
    best_sub_score = 0
    best_sub = ""
    words_in_sentence = sentence.lower().split()
    word_length = len(words_in_sentence)
    for sub in prob.keys():
        double_result = 0.0
        result = 0.0
        for i in range(word_length):
            if words_in_sentence[i] in prob[sub]['single']:
                result += math.log(prob[sub]['single'][words_in_sentence[i]])
            #  Add the probably of the next word following this one, this doesn't run on the last word of a sentence
            if i < word_length -1  and words_in_sentence[i] in prob[sub]['double'] and words_in_sentence[i+1] in prob[sub]['double'][words_in_sentence[i]]:
                double_result += math.log(prob[sub]['double'][words_in_sentence[i]][words_in_sentence[i+1]])
        #  Comment the line below to see results without looking ahead to the next word
        result += double_result
        if result < best_sub_score:
            best_sub = sub
            best_sub_score = result
    
    return best_sub

print find_subreddit(sentence)
'''
#  testing report here
total = 0
correct = 0
for sub, titles in data.items():
    for title in titles:
        total += 1
        if sub==find_subreddit(title):
            correct += 1
print str(correct) + "/"+str(total)
print float(correct)/total
'''