3-Building-the-Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building the Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading the corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Πρασινο αυκουι μες το πασχαλινο ποτήρι που έπιασε ο μιτσης #αισχος 🤣🤣🤣   @HARRIS_APOEL https://t.co/y9X7CmBEa5',\n",
       " '@HARRIS_APOEL @pirpoitis @vassrules Καμνουν πολλα ανακαινιση στα Περβολια .',\n",
       " '@MUFCChristian Ελα συγγενη τζιαι εχουμε νεοτερα π το Νικολη.']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "from nltk import sent_tokenize\n",
    "\n",
    "cg_sents = []\n",
    "smg_sents = []\n",
    "\n",
    "def remove_duplicate_punctuation(s):\n",
    "    return re.sub(r'([\\.\\?!;])\\1+', r'\\1 ', s)\n",
    "\n",
    "def fix_new_sentence_spacing(s):\n",
    "    return re.sub(r'([a-zα-ωίϊΐόάέύϋΰήώ])([\\.\\?!;])([A-ZΑ-ΩΆΈΊΌΎΏΉ])', r'\\1\\2 \\3', s)\n",
    "\n",
    "with open('./Data/cg_twitter.txt', 'r', encoding='utf-8') as in_file:\n",
    "    text = remove_duplicate_punctuation(in_file.read())\n",
    "    text = fix_new_sentence_spacing(text)\n",
    "    lines = [p for p in text.split('\\n') if p]\n",
    "    for line in lines:\n",
    "        cg_sents += sent_tokenize(line)\n",
    "    \n",
    "with open('./Data/cg_fb.txt', 'r', encoding='utf-8') as in_file:\n",
    "    text = remove_duplicate_punctuation(in_file.read())\n",
    "    text = fix_new_sentence_spacing(text)\n",
    "    lines = [p for p in text.split('\\n') if p]\n",
    "    for line in lines:\n",
    "        cg_sents += sent_tokenize(line)\n",
    "    \n",
    "with open('./Data/cg_other.txt', 'r', encoding='utf-8') as in_file:\n",
    "    text = remove_duplicate_punctuation(in_file.read())\n",
    "    text = fix_new_sentence_spacing(text)\n",
    "    lines = [p for p in text.split('\\n') if p]\n",
    "    for line in lines:\n",
    "        cg_sents += sent_tokenize(line)\n",
    "\n",
    "with open('./Data/smg_twitter.txt', 'r', encoding='utf-8') as in_file:\n",
    "    text = remove_duplicate_punctuation(in_file.read())\n",
    "    text = fix_new_sentence_spacing(text)\n",
    "    lines = [p for p in text.split('\\n') if p]\n",
    "    for line in lines:\n",
    "        smg_sents += sent_tokenize(line)\n",
    "    \n",
    "with open('./Data/smg_fb.txt', 'r', encoding='utf-8') as in_file:\n",
    "    text = remove_duplicate_punctuation(in_file.read())\n",
    "    text = fix_new_sentence_spacing(text)\n",
    "    lines = [p for p in text.split('\\n') if p]\n",
    "    for line in lines:\n",
    "        smg_sents += sent_tokenize(line)\n",
    "    \n",
    "with open('./Data/smg_other.txt', 'r', encoding='utf-8') as in_file:\n",
    "    text = remove_duplicate_punctuation(in_file.read())\n",
    "    text = fix_new_sentence_spacing(text)\n",
    "    lines = [p for p in text.split('\\n') if p]\n",
    "    for line in lines:\n",
    "        smg_sents += sent_tokenize(line)\n",
    "\n",
    "cg_sents[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Cleaning the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['πρασινο αυκουι μες το πασχαλινο ποτηρι που επιασε ο μιτσης',\n",
       " 'καμνουν πολλα ανακαινιση στα περβολια',\n",
       " 'ελα συγγενη τζιαι εχουμε νεοτερα π το νικολη']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unicodedata\n",
    "from string import punctuation\n",
    "from nltk.tokenize import WhitespaceTokenizer\n",
    "\n",
    "punctuation += '´΄’…“”–—―»«'\n",
    "\n",
    "def strip_accents(s):\n",
    "    return ''.join(c for c in unicodedata.normalize('NFD', s)\n",
    "                   if unicodedata.category(c) != 'Mn')\n",
    "\n",
    "def get_clean_sent_el(sentence):\n",
    "    sentence = re.sub(r'^RT', '', sentence)\n",
    "    sentence = re.sub(r'\\&\\w*;', '', sentence)\n",
    "    sentence = re.sub(r'\\@\\w*', '', sentence)\n",
    "    sentence = re.sub(r'\\$\\w*', '', sentence)\n",
    "    sentence = re.sub(r'https?:\\/\\/.*\\/\\w*', '', sentence)\n",
    "    sentence = ''.join(c for c in sentence if c <= '\\uFFFF')\n",
    "    sentence = strip_accents(sentence)\n",
    "    sentence = re.sub(r'#\\w*', '', sentence)\n",
    "    sentence = sentence.lower()\n",
    "    tokens = WhitespaceTokenizer().tokenize(sentence)\n",
    "    new_tokens = []\n",
    "    for token in tokens:\n",
    "        if token == 'ο,τι' or token == 'ό,τι' or token == 'o,ti' or token == 'ó,ti':\n",
    "            new_tokens.append(token)\n",
    "        else:\n",
    "            token = re.sub(r'(?<=[.,!\\?;\\'΄´])(?=[^\\s])', r' ', token)\n",
    "            new_token = token.translate(str.maketrans({key: None for key in punctuation}))\n",
    "            if new_token != '':\n",
    "                new_tokens.append(new_token)\n",
    "    sentence =' '.join(new_tokens)\n",
    "    sentence = sentence.replace('\\ufeff', '')\n",
    "    sentence = sentence.strip(' ')\n",
    "    sentence = sentence.replace('  ', ' ')\n",
    "    return sentence\n",
    "\n",
    "cg_sents_clean = []\n",
    "smg_sents_clean = []\n",
    "\n",
    "for sent in cg_sents:\n",
    "    cg_sents_clean.append(get_clean_sent_el(sent))\n",
    "for sent in smg_sents:\n",
    "    smg_sents_clean.append(get_clean_sent_el(sent))\n",
    "\n",
    "cg_sents_clean = list(filter(None, cg_sents_clean))\n",
    "smg_sents_clean = list(filter(None, smg_sents_clean))\n",
    "cg_sents_clean[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Building the feature extractor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import ngrams\n",
    "\n",
    "def get_word_ngrams(tokens, n):\n",
    "    ngrams_list = []\n",
    "    ngrams_list.append(list(ngrams(tokens, n)))\n",
    "    ngrams_flat_tuples = [ngram for ngram_list in ngrams_list for ngram in ngram_list]\n",
    "    format_string = '%s'\n",
    "    for _ in range(1, n):\n",
    "        format_string += (' %s')\n",
    "    ngrams_list_flat = [format_string % ngram_tuple for ngram_tuple in ngrams_flat_tuples]\n",
    "    return ngrams_list_flat\n",
    "\n",
    "def get_char_ngrams(word, n):\n",
    "    ngrams_list = []\n",
    "    word = word.replace('ς', 'σ')\n",
    "    ngrams_list.append(list(ngrams(word, n, pad_left=True, pad_right=True, left_pad_symbol='_', right_pad_symbol='_')))\n",
    "    \n",
    "    # Removing redundant ngrams:\n",
    "    if n > 2:\n",
    "        redundant_combinations = n - 2\n",
    "        ngrams_list = [ngram_list[redundant_combinations : -redundant_combinations] for ngram_list in ngrams_list]\n",
    "    \n",
    "    ngrams_flat_tuples = [ngram for ngram_list in ngrams_list for ngram in ngram_list]\n",
    "    format_string = ''\n",
    "    for _ in range(0, n):\n",
    "        format_string += ('%s')\n",
    "    ngrams_list_flat = [format_string % ngram_tuple for ngram_tuple in ngrams_flat_tuples]\n",
    "    return ngrams_list_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'char(α)': 3,\n",
       " 'char(ε)': 1,\n",
       " 'char(η)': 3,\n",
       " 'char(ι)': 2,\n",
       " 'char(ν)': 1,\n",
       " 'char(π)': 1,\n",
       " 'char(ρ)': 1,\n",
       " 'char(σ)': 1,\n",
       " 'char(τ)': 2,\n",
       " 'char(υ)': 1,\n",
       " 'char_bigram(_α)': 1,\n",
       " 'char_bigram(_ε)': 1,\n",
       " 'char_bigram(_η)': 1,\n",
       " 'char_bigram(_σ)': 1,\n",
       " 'char_bigram(αι)': 1,\n",
       " 'char_bigram(αρ)': 1,\n",
       " 'char_bigram(αυ)': 1,\n",
       " 'char_bigram(ει)': 1,\n",
       " 'char_bigram(η_)': 3,\n",
       " 'char_bigram(ι_)': 1,\n",
       " 'char_bigram(ιν)': 1,\n",
       " 'char_bigram(να)': 1,\n",
       " 'char_bigram(πα)': 1,\n",
       " 'char_bigram(ρτ)': 1,\n",
       " 'char_bigram(σπ)': 1,\n",
       " 'char_bigram(τη)': 2,\n",
       " 'char_bigram(υτ)': 1,\n",
       " 'char_trigram(_αυ)': 1,\n",
       " 'char_trigram(_ει)': 1,\n",
       " 'char_trigram(_η_)': 1,\n",
       " 'char_trigram(_σπ)': 1,\n",
       " 'char_trigram(αι_)': 1,\n",
       " 'char_trigram(αρτ)': 1,\n",
       " 'char_trigram(αυτ)': 1,\n",
       " 'char_trigram(ειν)': 1,\n",
       " 'char_trigram(ινα)': 1,\n",
       " 'char_trigram(ναι)': 1,\n",
       " 'char_trigram(παρ)': 1,\n",
       " 'char_trigram(ρτη)': 1,\n",
       " 'char_trigram(σπα)': 1,\n",
       " 'char_trigram(τη_)': 2,\n",
       " 'char_trigram(υτη)': 1,\n",
       " 'word(αυτη)': 1,\n",
       " 'word(ειναι)': 1,\n",
       " 'word(η)': 1,\n",
       " 'word(σπαρτη)': 1,\n",
       " 'word_bigram(αυτη ειναι)': 1,\n",
       " 'word_bigram(ειναι η)': 1,\n",
       " 'word_bigram(η σπαρτη)': 1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature extractor\n",
    "def get_ngram_features(sent): # The reason I do not use NLTK's everygrams to extract the features quickly is because the behavior of my n-gram extractor is modified to remove redundant n-grams. Also, I need to label word and char n-grams to avoid ambiguity\n",
    "    sentence_tokens = WhitespaceTokenizer().tokenize(sent)\n",
    "    \n",
    "    features = {}\n",
    "    \n",
    "    # Word unigrams\n",
    "    ngrams = get_word_ngrams(sentence_tokens, 1)\n",
    "    for ngram in ngrams:\n",
    "        features[f'word({ngram})'] = features.get(f'word({ngram})', 0) + 1 # The second parameter to .get() is a default value if the key doesn't exist.\n",
    "    \n",
    "    # Word bigrams\n",
    "    ngrams = get_word_ngrams(sentence_tokens, 2)\n",
    "    for ngram in ngrams:\n",
    "        features[f'word_bigram({ngram})'] = features.get(f'word_bigram({ngram})', 0) + 1\n",
    "    \n",
    "    # Char unigrams\n",
    "    for word in sentence_tokens:\n",
    "        ngrams = get_char_ngrams(word, 1)\n",
    "        for ngram in ngrams:\n",
    "            features[f'char({ngram})'] = features.get(f'char({ngram})', 0) + 1\n",
    "    \n",
    "    # Char bigrams\n",
    "    for word in sentence_tokens:\n",
    "        ngrams = get_char_ngrams(word, 2)\n",
    "        for ngram in ngrams:\n",
    "            features[f'char_bigram({ngram})'] = features.get(f'char_bigram({ngram})', 0) + 1\n",
    "    \n",
    "    # Char trigrams\n",
    "    for word in sentence_tokens:\n",
    "        ngrams = get_char_ngrams(word, 3)\n",
    "        for ngram in ngrams:\n",
    "            features[f'char_trigram({ngram})'] = features.get(f'char_trigram({ngram})', 0) + 1\n",
    "    \n",
    "    return features\n",
    "\n",
    "get_ngram_features('αυτη ειναι η σπαρτη')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from nltk import everygrams\n",
    "\n",
    "# def sent_process(sent):\n",
    "#     return [''.join(ng) for ng in everygrams(sent.replace(' ', '_ _'), 1, 4) \n",
    "#             if ' ' not in ng and '\\n' not in ng and ng != ('_',)]\n",
    "\n",
    "# sent_process('αυτη ειναι η σπαρτη')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Creating the training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('ουτε ενα μαντορινι εν τους αφηννε να κοψουν ρε ο ππιντης', 'CG')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "all_sents_labeled = ([(sentence, 'CG') for sentence in cg_sents_clean] + [(sentence, 'SMG') for sentence in smg_sents_clean])\n",
    "random.shuffle(all_sents_labeled)\n",
    "all_sents_labeled[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ουτε ενα μαντορινι εν τους αφηννε να κοψουν ρε ο ππιντης CG\n"
     ]
    }
   ],
   "source": [
    "NO_ALL_SENTENCES = len(all_sents_labeled)\n",
    "NO_TRAIN_SENTENCES = round(NO_ALL_SENTENCES * .8)\n",
    "\n",
    "train_set = all_sents_labeled[:NO_TRAIN_SENTENCES]\n",
    "test_set = all_sents_labeled[NO_TRAIN_SENTENCES:]\n",
    "\n",
    "train_set_sents = [sent[0] for sent in train_set]\n",
    "train_set_labels = [sent[1] for sent in train_set]\n",
    "test_set_sents = [sent[0] for sent in test_set]\n",
    "test_set_labels = [sent[1] for sent in test_set]\n",
    "\n",
    "print(train_set_sents[0], train_set_labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATASET\t SENTENCES\n",
      "All\t 1039\n",
      "Training 831\n",
      "Testing\t 208\n"
     ]
    }
   ],
   "source": [
    "print('DATASET\\t', 'SENTENCES')\n",
    "print('All\\t', NO_ALL_SENTENCES)\n",
    "print('Training', NO_TRAIN_SENTENCES)\n",
    "print('Testing\\t', NO_ALL_SENTENCES - NO_TRAIN_SENTENCES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<831x16007 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 124849 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect = CountVectorizer(analyzer=get_ngram_features)\n",
    "\n",
    "train_set_vectors = count_vect.fit_transform(train_set_sents)\n",
    "test_set_vectors = count_vect.transform(test_set_sents) # Unlike fit_transform(), transform() does not change the count vectorizer's vocabulary so it should be used for the test set.\n",
    "train_set_vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import set_printoptions, nan\n",
    "set_printoptions(threshold=nan) # Prints whole array. Required because by default an array with thousands of elements wouldn't be printed in full.\n",
    "\n",
    "train_set_vectors.toarray()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'word(ουτε)': 5944,\n",
       " 'word(ενα)': 4569,\n",
       " 'word(μαντορινι)': 5548,\n",
       " 'word(εν)': 4568,\n",
       " 'word(τους)': 6854,\n",
       " 'word(αφηννε)': 3932,\n",
       " 'word(να)': 5733,\n",
       " 'word(κοψουν)': 5371,\n",
       " 'word(ρε)': 6432,\n",
       " 'word(ο)': 5842,\n",
       " 'word(ππιντης)': 6320,\n",
       " 'word_bigram(ουτε ενα)': 12658,\n",
       " 'word_bigram(ενα μαντορινι)': 9189,\n",
       " 'word_bigram(μαντορινι εν)': 11164,\n",
       " 'word_bigram(εν τους)': 9163,\n",
       " 'word_bigram(τους αφηννε)': 15417,\n",
       " 'word_bigram(αφηννε να)': 7877,\n",
       " 'word_bigram(να κοψουν)': 11923,\n",
       " 'word_bigram(κοψουν ρε)': 10872,\n",
       " 'word_bigram(ρε ο)': 13638,\n",
       " 'word_bigram(ο ππιντης)': 12279,\n",
       " 'char(ο)': 22,\n",
       " 'char(υ)': 27,\n",
       " 'char(τ)': 26,\n",
       " 'char(ε)': 12,\n",
       " 'char(ν)': 20,\n",
       " 'char(α)': 8,\n",
       " 'char(μ)': 19,\n",
       " 'char(ρ)': 24,\n",
       " 'char(ι)': 16,\n",
       " 'char(σ)': 25,\n",
       " 'char(φ)': 28,\n",
       " 'char(η)': 14,\n",
       " 'char(κ)': 17,\n",
       " 'char(ψ)': 30,\n",
       " 'char(π)': 23,\n",
       " 'char_bigram(_ο)': 50,\n",
       " 'char_bigram(ου)': 313,\n",
       " 'char_bigram(υτ)': 408,\n",
       " 'char_bigram(τε)': 378,\n",
       " 'char_bigram(ε_)': 134,\n",
       " 'char_bigram(_ε)': 40,\n",
       " 'char_bigram(εν)': 146,\n",
       " 'char_bigram(να)': 272,\n",
       " 'char_bigram(α_)': 72,\n",
       " 'char_bigram(_μ)': 47,\n",
       " 'char_bigram(μα)': 259,\n",
       " 'char_bigram(αν)': 85,\n",
       " 'char_bigram(ντ)': 281,\n",
       " 'char_bigram(το)': 383,\n",
       " 'char_bigram(ορ)': 310,\n",
       " 'char_bigram(ρι)': 342,\n",
       " 'char_bigram(ιν)': 212,\n",
       " 'char_bigram(νι)': 277,\n",
       " 'char_bigram(ι_)': 200,\n",
       " 'char_bigram(ν_)': 271,\n",
       " 'char_bigram(_τ)': 54,\n",
       " 'char_bigram(υσ)': 407,\n",
       " 'char_bigram(σ_)': 356,\n",
       " 'char_bigram(_α)': 36,\n",
       " 'char_bigram(αφ)': 93,\n",
       " 'char_bigram(φη)': 415,\n",
       " 'char_bigram(ην)': 177,\n",
       " 'char_bigram(νν)': 278,\n",
       " 'char_bigram(νε)': 274,\n",
       " 'char_bigram(_ν)': 48,\n",
       " 'char_bigram(_κ)': 45,\n",
       " 'char_bigram(κο)': 235,\n",
       " 'char_bigram(οψ)': 316,\n",
       " 'char_bigram(ψο)': 443,\n",
       " 'char_bigram(υν)': 402,\n",
       " 'char_bigram(_ρ)': 52,\n",
       " 'char_bigram(ρε)': 339,\n",
       " 'char_bigram(ο_)': 293,\n",
       " 'char_bigram(_π)': 51,\n",
       " 'char_bigram(ππ)': 328,\n",
       " 'char_bigram(πι)': 323,\n",
       " 'char_bigram(τη)': 380,\n",
       " 'char_bigram(ησ)': 182,\n",
       " 'char_trigram(_ου)': 640,\n",
       " 'char_trigram(ουτ)': 2527,\n",
       " 'char_trigram(υτε)': 3237,\n",
       " 'char_trigram(τε_)': 3005,\n",
       " 'char_trigram(_εν)': 532,\n",
       " 'char_trigram(ενα)': 1306,\n",
       " 'char_trigram(να_)': 2191,\n",
       " 'char_trigram(_μα)': 603,\n",
       " 'char_trigram(μαν)': 2090,\n",
       " 'char_trigram(αντ)': 859,\n",
       " 'char_trigram(ντο)': 2296,\n",
       " 'char_trigram(τορ)': 3070,\n",
       " 'char_trigram(ορι)': 2474,\n",
       " 'char_trigram(ριν)': 2757,\n",
       " 'char_trigram(ινι)': 1745,\n",
       " 'char_trigram(νι_)': 2250,\n",
       " 'char_trigram(εν_)': 1305,\n",
       " 'char_trigram(_το)': 686,\n",
       " 'char_trigram(του)': 3073,\n",
       " 'char_trigram(ουσ)': 2526,\n",
       " 'char_trigram(υσ_)': 3224,\n",
       " 'char_trigram(_αφ)': 489,\n",
       " 'char_trigram(αφη)': 944,\n",
       " 'char_trigram(φην)': 3294,\n",
       " 'char_trigram(ηνν)': 1523,\n",
       " 'char_trigram(ννε)': 2267,\n",
       " 'char_trigram(νε_)': 2216,\n",
       " 'char_trigram(_να)': 614,\n",
       " 'char_trigram(_κο)': 590,\n",
       " 'char_trigram(κοψ)': 1923,\n",
       " 'char_trigram(οψο)': 2553,\n",
       " 'char_trigram(ψου)': 3444,\n",
       " 'char_trigram(ουν)': 2523,\n",
       " 'char_trigram(υν_)': 3183,\n",
       " 'char_trigram(_ρε)': 658,\n",
       " 'char_trigram(ρε_)': 2709,\n",
       " 'char_trigram(_ο_)': 628,\n",
       " 'char_trigram(_ππ)': 651,\n",
       " 'char_trigram(ππι)': 2651,\n",
       " 'char_trigram(πιν)': 2612,\n",
       " 'char_trigram(ιντ)': 1748,\n",
       " 'char_trigram(ντη)': 2294,\n",
       " 'char_trigram(τησ)': 3038,\n",
       " 'char_trigram(ησ_)': 1544,\n",
       " 'word(οδηγαμε)': 5844,\n",
       " 'word(σε)': 6469,\n",
       " 'word(λεωφορους)': 5459,\n",
       " 'word(με)': 5563,\n",
       " 'word(καμμενα)': 5119,\n",
       " 'word(λαστιχα)': 5437,\n",
       " 'word(καλυμμενα)': 5103,\n",
       " 'word(τα)': 6709,\n",
       " 'word(ματια)': 5557,\n",
       " 'word(και)': 5063,\n",
       " 'word(ξεφουσκωτους)': 5830,\n",
       " 'word(αεροσακκους)': 3558,\n",
       " 'word_bigram(οδηγαμε σε)': 12302,\n",
       " 'word_bigram(σε λεωφορους)': 13774,\n",
       " 'word_bigram(λεωφορους με)': 11014,\n",
       " 'word_bigram(με καμμενα)': 11261,\n",
       " 'word_bigram(καμμενα λαστιχα)': 10456,\n",
       " 'word_bigram(λαστιχα με)': 10975,\n",
       " 'word_bigram(με καλυμμενα)': 11260,\n",
       " 'word_bigram(καλυμμενα τα)': 10434,\n",
       " 'word_bigram(τα ματια)': 14349,\n",
       " 'word_bigram(ματια και)': 11224,\n",
       " 'word_bigram(και ξεφουσκωτους)': 10294,\n",
       " 'word_bigram(ξεφουσκωτους τους)': 12209,\n",
       " 'word_bigram(τους αεροσακκους)': 15411,\n",
       " 'char(δ)': 11,\n",
       " 'char(γ)': 10,\n",
       " 'char(λ)': 18,\n",
       " 'char(ω)': 31,\n",
       " 'char(χ)': 29,\n",
       " 'char(ξ)': 21,\n",
       " 'char_bigram(οδ)': 297,\n",
       " 'char_bigram(δη)': 127,\n",
       " 'char_bigram(ηγ)': 169,\n",
       " 'char_bigram(γα)': 110,\n",
       " 'char_bigram(αμ)': 84,\n",
       " 'char_bigram(με)': 261,\n",
       " 'char_bigram(_σ)': 53,\n",
       " 'char_bigram(σε)': 360,\n",
       " 'char_bigram(_λ)': 46,\n",
       " 'char_bigram(λε)': 245,\n",
       " 'char_bigram(εω)': 157,\n",
       " 'char_bigram(ωφ)': 462,\n",
       " 'char_bigram(φο)': 420,\n",
       " 'char_bigram(ρο)': 347,\n",
       " 'char_bigram(κα)': 225,\n",
       " 'char_bigram(μμ)': 264,\n",
       " 'char_bigram(λα)': 243,\n",
       " 'char_bigram(ασ)': 90,\n",
       " 'char_bigram(στ)': 370,\n",
       " 'char_bigram(τι)': 381,\n",
       " 'char_bigram(ιχ)': 221,\n",
       " 'char_bigram(χα)': 426,\n",
       " 'char_bigram(αλ)': 83,\n",
       " 'char_bigram(λυ)': 255,\n",
       " 'char_bigram(υμ)': 401,\n",
       " 'char_bigram(τα)': 377,\n",
       " 'char_bigram(ατ)': 91,\n",
       " 'char_bigram(ια)': 201,\n",
       " 'char_bigram(αι)': 81,\n",
       " 'char_bigram(_ξ)': 49,\n",
       " 'char_bigram(ξε)': 286,\n",
       " 'char_bigram(εφ)': 154,\n",
       " 'char_bigram(σκ)': 364,\n",
       " 'char_bigram(κω)': 241,\n",
       " 'char_bigram(ωτ)': 461,\n",
       " 'char_bigram(αε)': 77,\n",
       " 'char_bigram(ερ)': 150,\n",
       " 'char_bigram(οσ)': 311,\n",
       " 'char_bigram(σα)': 357,\n",
       " 'char_bigram(ακ)': 82,\n",
       " 'char_bigram(κκ)': 231,\n",
       " 'char_trigram(_οδ)': 629,\n",
       " 'char_trigram(οδη)': 2375,\n",
       " 'char_trigram(δηγ)': 1149,\n",
       " 'char_trigram(ηγα)': 1480,\n",
       " 'char_trigram(γαμ)': 1037,\n",
       " 'char_trigram(αμε)': 840,\n",
       " 'char_trigram(με_)': 2102,\n",
       " 'char_trigram(_σε)': 667,\n",
       " 'char_trigram(σε_)': 2871,\n",
       " 'char_trigram(_λε)': 596,\n",
       " 'char_trigram(λεω)': 1998,\n",
       " 'char_trigram(εωφ)': 1434,\n",
       " 'char_trigram(ωφο)': 3516,\n",
       " 'char_trigram(φορ)': 3322,\n",
       " 'char_trigram(ορο)': 2478,\n",
       " 'char_trigram(ρου)': 2801,\n",
       " 'char_trigram(_με)': 604,\n",
       " 'char_trigram(_κα)': 585,\n",
       " 'char_trigram(καμ)': 1845,\n",
       " 'char_trigram(αμμ)': 843,\n",
       " 'char_trigram(μμε)': 2139,\n",
       " 'char_trigram(μεν)': 2108,\n",
       " 'char_trigram(_λα)': 595,\n",
       " 'char_trigram(λασ)': 1975,\n",
       " 'char_trigram(αστ)': 916,\n",
       " 'char_trigram(στι)': 2953,\n",
       " 'char_trigram(τιχ)': 3056,\n",
       " 'char_trigram(ιχα)': 1820,\n",
       " 'char_trigram(χα_)': 3349,\n",
       " 'char_trigram(καλ)': 1844,\n",
       " 'char_trigram(αλυ)': 835,\n",
       " 'char_trigram(λυμ)': 2065,\n",
       " 'char_trigram(υμμ)': 3177,\n",
       " 'char_trigram(_τα)': 680,\n",
       " 'char_trigram(τα_)': 2984,\n",
       " 'char_trigram(ματ)': 2095,\n",
       " 'char_trigram(ατι)': 926,\n",
       " 'char_trigram(τια)': 3042,\n",
       " 'char_trigram(ια_)': 1644,\n",
       " 'char_trigram(και)': 1842,\n",
       " 'char_trigram(αι_)': 794,\n",
       " 'char_trigram(_ξε)': 623,\n",
       " 'char_trigram(ξεφ)': 2329,\n",
       " 'char_trigram(εφο)': 1412,\n",
       " 'char_trigram(φου)': 3324,\n",
       " 'char_trigram(υσκ)': 3229,\n",
       " 'char_trigram(σκω)': 2918,\n",
       " 'char_trigram(κωτ)': 1958,\n",
       " 'char_trigram(ωτο)': 3512,\n",
       " 'char_trigram(_αε)': 474,\n",
       " 'char_trigram(αερ)': 769,\n",
       " 'char_trigram(ερο)': 1358,\n",
       " 'char_trigram(ροσ)': 2799,\n",
       " 'char_trigram(οσα)': 2485,\n",
       " 'char_trigram(σακ)': 2855,\n",
       " 'char_trigram(ακκ)': 817,\n",
       " 'char_trigram(κκο)': 1895,\n",
       " 'char_trigram(κου)': 1921,\n",
       " 'word(ειμαστε)': 4437,\n",
       " 'word(πλουσιοι)': 6235,\n",
       " 'word(τζιαι)': 6781,\n",
       " 'word(χωρις)': 7182,\n",
       " 'word(την)': 6809,\n",
       " 'word(κοπιαν)': 5319,\n",
       " 'word(ολαν)': 5859,\n",
       " 'word_bigram(ειμαστε πλουσιοι)': 8737,\n",
       " 'word_bigram(πλουσιοι τζιαι)': 13086,\n",
       " 'word_bigram(τζιαι χωρις)': 14667,\n",
       " 'word_bigram(χωρις την)': 15928,\n",
       " 'word_bigram(την κοπιαν)': 14799,\n",
       " 'word_bigram(κοπιαν ολαν)': 10812,\n",
       " 'char(ζ)': 13,\n",
       " 'char_bigram(ει)': 142,\n",
       " 'char_bigram(ιμ)': 211,\n",
       " 'char_bigram(πλ)': 325,\n",
       " 'char_bigram(λο)': 252,\n",
       " 'char_bigram(σι)': 363,\n",
       " 'char_bigram(ιο)': 214,\n",
       " 'char_bigram(οι)': 302,\n",
       " 'char_bigram(τζ)': 379,\n",
       " 'char_bigram(ζι)': 162,\n",
       " 'char_bigram(_χ)': 57,\n",
       " 'char_bigram(χω)': 438,\n",
       " 'char_bigram(ωρ)': 459,\n",
       " 'char_bigram(ισ)': 217,\n",
       " 'char_bigram(οπ)': 309,\n",
       " 'char_bigram(ολ)': 304,\n",
       " 'char_trigram(_ει)': 528,\n",
       " 'char_trigram(ειμ)': 1252,\n",
       " 'char_trigram(ιμα)': 1733,\n",
       " 'char_trigram(μασ)': 2094,\n",
       " 'char_trigram(στε)': 2951,\n",
       " 'char_trigram(_πλ)': 648,\n",
       " 'char_trigram(πλο)': 2623,\n",
       " 'char_trigram(λου)': 2049,\n",
       " 'char_trigram(υσι)': 3228,\n",
       " 'char_trigram(σιο)': 2905,\n",
       " 'char_trigram(ιοι)': 1760,\n",
       " 'char_trigram(οι_)': 2391,\n",
       " 'char_trigram(_τζ)': 682,\n",
       " 'char_trigram(τζι)': 3026,\n",
       " 'char_trigram(ζια)': 1455,\n",
       " 'char_trigram(ιαι)': 1651,\n",
       " 'char_trigram(_χω)': 717,\n",
       " 'char_trigram(χωρ)': 3424,\n",
       " 'char_trigram(ωρι)': 3492,\n",
       " 'char_trigram(ρισ)': 2761,\n",
       " 'char_trigram(ισ_)': 1786,\n",
       " 'char_trigram(_τη)': 683,\n",
       " 'char_trigram(την)': 3036,\n",
       " 'char_trigram(ην_)': 1518,\n",
       " 'char_trigram(κοπ)': 1917,\n",
       " 'char_trigram(οπι)': 2461,\n",
       " 'char_trigram(πια)': 2604,\n",
       " 'char_trigram(ιαν)': 1655,\n",
       " 'char_trigram(αν_)': 850,\n",
       " 'char_trigram(_ολ)': 632,\n",
       " 'char_trigram(ολα)': 2419,\n",
       " 'char_trigram(λαν)': 1970,\n",
       " 'word(ζητειται)': 4878,\n",
       " 'word(κυπριος)': 5401,\n",
       " 'word(φοιτητης)': 7057,\n",
       " 'word(που)': 6305,\n",
       " 'word(δεν)': 4206,\n",
       " 'word(εχει)': 4854,\n",
       " 'word(αυτοκινητο)': 3915,\n",
       " 'word_bigram(ζητειται κυπριος)': 9700,\n",
       " 'word_bigram(κυπριος φοιτητης)': 10905,\n",
       " 'word_bigram(φοιτητης που)': 15762,\n",
       " 'word_bigram(που δεν)': 13267,\n",
       " 'word_bigram(δεν εχει)': 8348,\n",
       " 'word_bigram(εχει αυτοκινητο)': 9608,\n",
       " 'char_bigram(_ζ)': 41,\n",
       " 'char_bigram(ζη)': 161,\n",
       " 'char_bigram(ητ)': 183,\n",
       " 'char_bigram(ιτ)': 218,\n",
       " 'char_bigram(κυ)': 239,\n",
       " 'char_bigram(υπ)': 405,\n",
       " 'char_bigram(πρ)': 329,\n",
       " 'char_bigram(_φ)': 56,\n",
       " 'char_bigram(πο)': 327,\n",
       " 'char_bigram(υ_)': 389,\n",
       " 'char_bigram(_δ)': 39,\n",
       " 'char_bigram(δε)': 126,\n",
       " 'char_bigram(εχ)': 155,\n",
       " 'char_bigram(χε)': 427,\n",
       " 'char_bigram(αυ)': 92,\n",
       " 'char_bigram(οκ)': 303,\n",
       " 'char_bigram(κι)': 230,\n",
       " 'char_bigram(νη)': 275,\n",
       " 'char_trigram(_ζη)': 545,\n",
       " 'char_trigram(ζητ)': 1453,\n",
       " 'char_trigram(ητε)': 1556,\n",
       " 'char_trigram(τει)': 3008,\n",
       " 'char_trigram(ειτ)': 1259,\n",
       " 'char_trigram(ιτα)': 1802,\n",
       " 'char_trigram(ται)': 2991,\n",
       " 'char_trigram(_κυ)': 593,\n",
       " 'char_trigram(κυπ)': 1949,\n",
       " 'char_trigram(υπρ)': 3208,\n",
       " 'char_trigram(πρι)': 2657,\n",
       " 'char_trigram(ριο)': 2759,\n",
       " 'char_trigram(ιοσ)': 1766,\n",
       " 'char_trigram(οσ_)': 2484,\n",
       " 'char_trigram(_φο)': 703,\n",
       " 'char_trigram(φοι)': 3320,\n",
       " 'char_trigram(οιτ)': 2404,\n",
       " 'char_trigram(ιτη)': 1805,\n",
       " 'char_trigram(τητ)': 3039,\n",
       " 'char_trigram(ητη)': 1557,\n",
       " 'char_trigram(_πο)': 650,\n",
       " 'char_trigram(που)': 2645,\n",
       " 'char_trigram(ου_)': 2511,\n",
       " 'char_trigram(_δε)': 513,\n",
       " 'char_trigram(δεν)': 1140,\n",
       " 'char_trigram(_εχ)': 540,\n",
       " 'char_trigram(εχε)': 1417,\n",
       " 'char_trigram(χει)': 3362,\n",
       " 'char_trigram(ει_)': 1244,\n",
       " 'char_trigram(_αυ)': 488,\n",
       " 'char_trigram(αυτ)': 941,\n",
       " 'char_trigram(υτο)': 3241,\n",
       " 'char_trigram(τοκ)': 3064,\n",
       " 'char_trigram(οκι)': 2411,\n",
       " 'char_trigram(κιν)': 1885,\n",
       " 'char_trigram(ινη)': 1743,\n",
       " 'char_trigram(νητ)': 2244,\n",
       " 'char_trigram(ητο)': 1559,\n",
       " 'char_trigram(το_)': 3060,\n",
       " 'word(τουτο)': 6859,\n",
       " 'word(φκαινουν)': 7040,\n",
       " 'word(τραουδιστες)': 6871,\n",
       " 'word(ηξερε)': 4918,\n",
       " 'word(η)': 4891,\n",
       " 'word(μανα)': 5540,\n",
       " 'word(ξεπερνα)': 5820,\n",
       " 'word_bigram(τουτο που)': 15504,\n",
       " 'word_bigram(που φκαινουν)': 13434,\n",
       " 'word_bigram(φκαινουν τραουδιστες)': 15745,\n",
       " 'word_bigram(τραουδιστες που)': 15528,\n",
       " 'word_bigram(που εν)': 13300,\n",
       " 'word_bigram(τους ηξερε)': 15439,\n",
       " 'word_bigram(ηξερε η)': 9903,\n",
       " 'word_bigram(η μανα)': 9808,\n",
       " 'word_bigram(μανα τους)': 11156,\n",
       " 'word_bigram(τους ξεπερνα)': 15455,\n",
       " 'word_bigram(ξεπερνα με)': 12176,\n",
       " 'char_bigram(φκ)': 418,\n",
       " 'char_bigram(νο)': 279,\n",
       " 'char_bigram(τρ)': 384,\n",
       " 'char_bigram(ρα)': 335,\n",
       " 'char_bigram(αο)': 87,\n",
       " 'char_bigram(υδ)': 393,\n",
       " 'char_bigram(δι)': 128,\n",
       " 'char_bigram(εσ)': 151,\n",
       " 'char_bigram(_η)': 42,\n",
       " 'char_bigram(ηξ)': 178,\n",
       " 'char_bigram(η_)': 166,\n",
       " 'char_bigram(επ)': 149,\n",
       " 'char_bigram(πε)': 321,\n",
       " 'char_bigram(ρν)': 345,\n",
       " 'char_trigram(_φκ)': 701,\n",
       " 'char_trigram(φκα)': 3309,\n",
       " 'char_trigram(αιν)': 804,\n",
       " 'char_trigram(ινο)': 1746,\n",
       " 'char_trigram(νου)': 2284,\n",
       " 'char_trigram(_τρ)': 687,\n",
       " 'char_trigram(τρα)': 3076,\n",
       " 'char_trigram(ραο)': 2689,\n",
       " 'char_trigram(αου)': 871,\n",
       " 'char_trigram(ουδ)': 2515,\n",
       " 'char_trigram(υδι)': 3133,\n",
       " 'char_trigram(δισ)': 1166,\n",
       " 'char_trigram(ιστ)': 1798,\n",
       " 'char_trigram(τεσ)': 3016,\n",
       " 'char_trigram(εσ_)': 1366,\n",
       " 'char_trigram(_ηξ)': 558,\n",
       " 'char_trigram(ηξε)': 1527,\n",
       " 'char_trigram(ξερ)': 2326,\n",
       " 'char_trigram(ερε)': 1351,\n",
       " 'char_trigram(_η_)': 550,\n",
       " 'char_trigram(ανα)': 851,\n",
       " 'char_trigram(ξεπ)': 2325,\n",
       " 'char_trigram(επε)': 1337,\n",
       " 'char_trigram(περ)': 2586,\n",
       " 'char_trigram(ερν)': 1357,\n",
       " 'char_trigram(ρνα)': 2777,\n",
       " 'word(ενδιαφερεται)': 4578,\n",
       " 'word(για)': 4100,\n",
       " 'word(πολιτικη)': 6269,\n",
       " 'word_bigram(που ενδιαφερεται)': 13302,\n",
       " 'word_bigram(ενδιαφερεται για)': 9238,\n",
       " 'word_bigram(για την)': 8127,\n",
       " 'word_bigram(την πολιτικη)': 14826,\n",
       " 'char_bigram(νδ)': 273,\n",
       " 'char_bigram(φε)': 414,\n",
       " 'char_bigram(ετ)': 152,\n",
       " 'char_bigram(_γ)': 38,\n",
       " 'char_bigram(γι)': 114,\n",
       " 'char_bigram(λι)': 247,\n",
       " 'char_bigram(ικ)': 209,\n",
       " 'char_bigram(κη)': 228,\n",
       " 'char_trigram(ενδ)': 1307,\n",
       " 'char_trigram(νδι)': 2213,\n",
       " 'char_trigram(δια)': 1157,\n",
       " 'char_trigram(ιαφ)': 1661,\n",
       " 'char_trigram(αφε)': 943,\n",
       " 'char_trigram(φερ)': 3285,\n",
       " 'char_trigram(ρετ)': 2723,\n",
       " 'char_trigram(ετα)': 1381,\n",
       " 'char_trigram(_γι)': 504,\n",
       " 'char_trigram(για)': 1069,\n",
       " 'char_trigram(πολ)': 2639,\n",
       " 'char_trigram(ολι)': 2422,\n",
       " 'char_trigram(λιτ)': 2022,\n",
       " 'char_trigram(ιτι)': 1806,\n",
       " 'char_trigram(τικ)': 3048,\n",
       " 'char_trigram(ικη)': 1716,\n",
       " 'char_trigram(κη_)': 1869,\n",
       " 'word(επικινδυνα)': 4694,\n",
       " 'word(λαθη)': 5415,\n",
       " 'word(μπορουμε)': 5706,\n",
       " 'word(κανουμε)': 5151,\n",
       " 'word(οταν)': 5925,\n",
       " 'word(παρεχουμε)': 6052,\n",
       " 'word(πρωτες)': 6414,\n",
       " 'word(βοηθειες)': 4020,\n",
       " 'word(ατομο)': 3895,\n",
       " 'word_bigram(επικινδυνα λαθη)': 9381,\n",
       " 'word_bigram(λαθη που)': 10926,\n",
       " 'word_bigram(που μπορουμε)': 13371,\n",
       " 'word_bigram(μπορουμε να)': 11750,\n",
       " 'word_bigram(να κανουμε)': 11910,\n",
       " 'word_bigram(κανουμε οταν)': 10560,\n",
       " 'word_bigram(οταν παρεχουμε)': 12535,\n",
       " 'word_bigram(παρεχουμε πρωτες)': 12833,\n",
       " 'word_bigram(πρωτες βοηθειες)': 13572,\n",
       " 'word_bigram(βοηθειες σε)': 7994,\n",
       " 'word_bigram(σε ενα)': 13753,\n",
       " 'word_bigram(ενα ατομο)': 9176,\n",
       " 'char(θ)': 15,\n",
       " 'char(β)': 9,\n",
       " 'char_bigram(δυ)': 132,\n",
       " 'char_bigram(αθ)': 80,\n",
       " 'char_bigram(θη)': 189,\n",
       " 'char_bigram(μπ)': 267,\n",
       " 'char_bigram(οτ)': 312,\n",
       " 'char_bigram(πα)': 319,\n",
       " 'char_bigram(αρ)': 89,\n",
       " 'char_bigram(χο)': 434,\n",
       " 'char_bigram(ρω)': 355,\n",
       " 'char_bigram(_β)': 37,\n",
       " 'char_bigram(βο)': 106,\n",
       " 'char_bigram(οη)': 300,\n",
       " 'char_bigram(ηθ)': 173,\n",
       " 'char_bigram(θε)': 188,\n",
       " 'char_bigram(ιε)': 205,\n",
       " 'char_bigram(ομ)': 305,\n",
       " 'char_bigram(μο)': 266,\n",
       " 'char_trigram(_επ)': 534,\n",
       " 'char_trigram(επι)': 1339,\n",
       " 'char_trigram(πικ)': 2610,\n",
       " 'char_trigram(ικι)': 1717,\n",
       " 'char_trigram(ινδ)': 1741,\n",
       " 'char_trigram(νδυ)': 2215,\n",
       " 'char_trigram(δυν)': 1189,\n",
       " 'char_trigram(υνα)': 3184,\n",
       " 'char_trigram(λαθ)': 1965,\n",
       " 'char_trigram(αθη)': 784,\n",
       " 'char_trigram(θη_)': 1593,\n",
       " 'char_trigram(_μπ)': 610,\n",
       " 'char_trigram(μπο)': 2170,\n",
       " 'char_trigram(πορ)': 2642,\n",
       " 'char_trigram(ουμ)': 2522,\n",
       " 'char_trigram(υμε)': 3174,\n",
       " 'char_trigram(καν)': 1846,\n",
       " 'char_trigram(ανο)': 858,\n",
       " 'char_trigram(_οτ)': 639,\n",
       " 'char_trigram(οτα)': 2501,\n",
       " 'char_trigram(ταν)': 2995,\n",
       " 'char_trigram(_πα)': 643,\n",
       " 'char_trigram(παρ)': 2568,\n",
       " 'char_trigram(αρε)': 888,\n",
       " 'char_trigram(ρεχ)': 2726,\n",
       " 'char_trigram(εχο)': 1421,\n",
       " 'char_trigram(χου)': 3404,\n",
       " 'char_trigram(_πρ)': 652,\n",
       " 'char_trigram(πρω)': 2659,\n",
       " 'char_trigram(ρωτ)': 2848,\n",
       " 'char_trigram(ωτε)': 3509,\n",
       " 'char_trigram(_βο)': 498,\n",
       " 'char_trigram(βοη)': 1015,\n",
       " 'char_trigram(οηθ)': 2384,\n",
       " 'char_trigram(ηθε)': 1493,\n",
       " 'char_trigram(θει)': 1584,\n",
       " 'char_trigram(ειε)': 1248,\n",
       " 'char_trigram(ιεσ)': 1690,\n",
       " 'char_trigram(_ατ)': 487,\n",
       " 'char_trigram(ατο)': 928,\n",
       " 'char_trigram(τομ)': 3066,\n",
       " 'char_trigram(ομο)': 2434,\n",
       " 'char_trigram(μο_)': 2148,\n",
       " 'word(το)': 6822,\n",
       " 'word(ειναι)': 4439,\n",
       " 'word(αποδεκτος)': 3778,\n",
       " 'word(βιασμος)': 3992,\n",
       " 'word(αποδειξεις)': 3776,\n",
       " 'word(παλης)': 5986,\n",
       " 'word(σημαινει)': 6490,\n",
       " 'word(οτι)': 5926,\n",
       " 'word(οποιοσδηποτε)': 5897,\n",
       " 'word(μπορει)': 5703,\n",
       " 'word(κατηγορησει)': 5221,\n",
       " 'word(οποιονδηποτε)': 5895,\n",
       " 'word(βιασμο)': 3991,\n",
       " 'word_bigram(το να)': 15130,\n",
       " 'word_bigram(να ειναι)': 11846,\n",
       " 'word_bigram(ειναι αποδεκτος)': 8751,\n",
       " 'word_bigram(αποδεκτος ο)': 7682,\n",
       " 'word_bigram(ο βιασμος)': 12237,\n",
       " 'word_bigram(βιασμος χωρις)': 7957,\n",
       " 'word_bigram(χωρις αποδειξεις)': 15924,\n",
       " 'word_bigram(αποδειξεις παλης)': 7679,\n",
       " 'word_bigram(παλης σημαινει)': 12733,\n",
       " 'word_bigram(σημαινει οτι)': 13842,\n",
       " 'word_bigram(οτι οποιοσδηποτε)': 12592,\n",
       " 'word_bigram(οποιοσδηποτε μπορει)': 12470,\n",
       " 'word_bigram(μπορει να)': 11744,\n",
       " 'word_bigram(να κατηγορησει)': 11919,\n",
       " 'word_bigram(κατηγορησει οποιονδηποτε)': 10662,\n",
       " 'word_bigram(οποιονδηποτε για)': 12468,\n",
       " 'word_bigram(για βιασμο)': 8081,\n",
       " 'char_bigram(απ)': 88,\n",
       " 'char_bigram(εκ)': 143,\n",
       " 'char_bigram(κτ)': 238,\n",
       " 'char_bigram(βι)': 104,\n",
       " 'char_bigram(σμ)': 366,\n",
       " 'char_bigram(ιξ)': 213,\n",
       " 'char_bigram(λη)': 246,\n",
       " 'char_bigram(ση)': 361,\n",
       " 'char_bigram(ημ)': 176,\n",
       " 'char_bigram(σδ)': 359,\n",
       " 'char_bigram(ηπ)': 180,\n",
       " 'char_bigram(γο)': 119,\n",
       " 'char_bigram(ρη)': 340,\n",
       " 'char_bigram(ον)': 306,\n",
       " 'char_trigram(ειν)': 1253,\n",
       " 'char_trigram(ινα)': 1740,\n",
       " 'char_trigram(ναι)': 2197,\n",
       " 'char_trigram(_απ)': 484,\n",
       " 'char_trigram(απο)': 879,\n",
       " 'char_trigram(ποδ)': 2633,\n",
       " 'char_trigram(οδε)': 2374,\n",
       " 'char_trigram(δεκ)': 1137,\n",
       " 'char_trigram(εκτ)': 1277,\n",
       " 'char_trigram(κτο)': 1937,\n",
       " 'char_trigram(τοσ)': 3071,\n",
       " 'char_trigram(_βι)': 496,\n",
       " 'char_trigram(βια)': 999,\n",
       " 'char_trigram(ιασ)': 1659,\n",
       " 'char_trigram(ασμ)': 912,\n",
       " 'char_trigram(σμο)': 2923,\n",
       " 'char_trigram(μοσ)': 2158,\n",
       " 'char_trigram(δει)': 1136,\n",
       " 'char_trigram(ειξ)': 1254,\n",
       " 'char_trigram(ιξε)': 1751,\n",
       " 'char_trigram(ξει)': 2322,\n",
       " 'char_trigram(εισ)': 1258,\n",
       " 'char_trigram(παλ)': 2562,\n",
       " 'char_trigram(αλη)': 828,\n",
       " 'char_trigram(λησ)': 2006,\n",
       " 'char_trigram(_ση)': 668,\n",
       " 'char_trigram(σημ)': 2888,\n",
       " 'char_trigram(ημα)': 1512,\n",
       " 'char_trigram(μαι)': 2086,\n",
       " 'char_trigram(ινε)': 1742,\n",
       " 'char_trigram(νει)': 2221,\n",
       " 'char_trigram(οτι)': 2505,\n",
       " 'char_trigram(τι_)': 3041,\n",
       " 'char_trigram(_οπ)': 636,\n",
       " 'char_trigram(οπο)': 2463,\n",
       " 'char_trigram(ποι)': 2637,\n",
       " 'char_trigram(οιο)': 2400,\n",
       " 'char_trigram(οσδ)': 2487,\n",
       " 'char_trigram(σδη)': 2870,\n",
       " 'char_trigram(δηπ)': 1154,\n",
       " 'char_trigram(ηπο)': 1532,\n",
       " 'char_trigram(ποτ)': 2644,\n",
       " 'char_trigram(οτε)': 2502,\n",
       " 'char_trigram(ορε)': 2471,\n",
       " 'char_trigram(ρει)': 2714,\n",
       " 'char_trigram(κατ)': 1851,\n",
       " 'char_trigram(ατη)': 925,\n",
       " 'char_trigram(τηγ)': 3030,\n",
       " 'char_trigram(ηγο)': 1484,\n",
       " 'char_trigram(γορ)': 1101,\n",
       " 'char_trigram(ορη)': 2472,\n",
       " 'char_trigram(ρησ)': 2737,\n",
       " 'char_trigram(ησε)': 1546,\n",
       " 'char_trigram(σει)': 2875,\n",
       " 'char_trigram(ιον)': 1763,\n",
       " 'char_trigram(ονδ)': 2439,\n",
       " 'char_trigram(νδη)': 2212,\n",
       " 'word(φιλε)': 7022,\n",
       " 'word(μου)': 5676,\n",
       " 'word(ειπα)': 4441,\n",
       " 'word(σου)': 6560,\n",
       " 'word(τζιαμε)': 6787,\n",
       " 'word_bigram(φιλε μου)': 15726,\n",
       " 'word_bigram(μου ειπα)': 11639,\n",
       " 'word_bigram(ειπα σου)': 8837,\n",
       " 'word_bigram(σου το)': 13985,\n",
       " 'word_bigram(το τζιαι)': 15210,\n",
       " 'word_bigram(τζιαι τζιαμε)': 14656,\n",
       " 'char_bigram(φι)': 417,\n",
       " 'char_bigram(ιλ)': 210,\n",
       " 'char_bigram(ιπ)': 215,\n",
       " 'char_bigram(σο)': 367,\n",
       " 'char_trigram(_φι)': 700,\n",
       " 'char_trigram(φιλ)': 3303,\n",
       " 'char_trigram(ιλε)': 1726,\n",
       " 'char_trigram(λε_)': 1981,\n",
       " 'char_trigram(_μο)': 609,\n",
       " 'char_trigram(μου)': 2160,\n",
       " 'char_trigram(ειπ)': 1256,\n",
       " 'char_trigram(ιπα)': 1771,\n",
       " 'char_trigram(πα_)': 2556,\n",
       " 'char_trigram(_σο)': 671,\n",
       " 'char_trigram(σου)': 2935,\n",
       " 'char_trigram(ιαμ)': 1654,\n",
       " 'word(επιθεση)': 4691,\n",
       " 'word(μπογιες)': 5702,\n",
       " 'word(στο)': 6607,\n",
       " 'word(γραφειο)': 4155,\n",
       " 'word(της)': 6811,\n",
       " 'word(επιτιμου)': 4715,\n",
       " 'word(προξενου)': 6356,\n",
       " 'word(βρετανιας)': 4038,\n",
       " 'word(γαλλικο)': 4059,\n",
       " 'word(ινστιτουτο)': 5028,\n",
       " 'word_bigram(επιθεση με)': 9378,\n",
       " 'word_bigram(με μπογιες)': 11276,\n",
       " 'word_bigram(μπογιες στο)': 11741,\n",
       " 'word_bigram(στο γραφειο)': 14127,\n",
       " 'word_bigram(γραφειο της)': 8236,\n",
       " 'word_bigram(της επιτιμου)': 14871,\n",
       " 'word_bigram(επιτιμου προξενου)': 9404,\n",
       " 'word_bigram(προξενου βρετανιας)': 13524,\n",
       " 'word_bigram(βρετανιας και)': 8012,\n",
       " 'word_bigram(και το)': 10344,\n",
       " 'word_bigram(το γαλλικο)': 15009,\n",
       " 'word_bigram(γαλλικο ινστιτουτο)': 8032,\n",
       " 'char_bigram(ιθ)': 208,\n",
       " 'char_bigram(ογ)': 296,\n",
       " 'char_bigram(γρ)': 120,\n",
       " 'char_bigram(οξ)': 307,\n",
       " 'char_bigram(βρ)': 107,\n",
       " 'char_bigram(λλ)': 249,\n",
       " 'char_bigram(_ι)': 44,\n",
       " 'char_bigram(νσ)': 280,\n",
       " 'char_trigram(πιθ)': 2609,\n",
       " 'char_trigram(ιθε)': 1707,\n",
       " 'char_trigram(θεσ)': 1590,\n",
       " 'char_trigram(εση)': 1369,\n",
       " 'char_trigram(ση_)': 2886,\n",
       " 'char_trigram(πογ)': 2632,\n",
       " 'char_trigram(ογι)': 2368,\n",
       " 'char_trigram(γιε)': 1070,\n",
       " 'char_trigram(_στ)': 674,\n",
       " 'char_trigram(στο)': 2954,\n",
       " 'char_trigram(_γρ)': 509,\n",
       " 'char_trigram(γρα)': 1105,\n",
       " 'char_trigram(ραφ)': 2694,\n",
       " 'char_trigram(φει)': 3283,\n",
       " 'char_trigram(ειο)': 1255,\n",
       " 'char_trigram(ιο_)': 1756,\n",
       " 'char_trigram(πιτ)': 2616,\n",
       " 'char_trigram(τιμ)': 3050,\n",
       " 'char_trigram(ιμο)': 1736,\n",
       " 'char_trigram(προ)': 2658,\n",
       " 'char_trigram(ροξ)': 2796,\n",
       " 'char_trigram(οξε)': 2447,\n",
       " 'char_trigram(ξεν)': 2324,\n",
       " 'char_trigram(ενο)': 1313,\n",
       " 'char_trigram(_βρ)': 499,\n",
       " 'char_trigram(βρε)': 1022,\n",
       " 'char_trigram(ανι)': 856,\n",
       " 'char_trigram(νια)': 2251,\n",
       " 'char_trigram(ασ_)': 905,\n",
       " 'char_trigram(_γα)': 501,\n",
       " 'char_trigram(γαλ)': 1036,\n",
       " 'char_trigram(αλλ)': 831,\n",
       " 'char_trigram(λλι)': 2030,\n",
       " 'char_trigram(λικ)': 2017,\n",
       " 'char_trigram(ικο)': 1720,\n",
       " 'char_trigram(κο_)': 1908,\n",
       " 'char_trigram(_ιν)': 577,\n",
       " 'char_trigram(ινσ)': 1747,\n",
       " 'char_trigram(νστ)': 2289,\n",
       " 'char_trigram(τιτ)': 3055,\n",
       " 'char_trigram(ιτο)': 1807,\n",
       " 'word(τι)': 6812,\n",
       " 'word(εκερδισαμεν)': 4490,\n",
       " 'word(αδεσμευτους)': 3548,\n",
       " 'word_bigram(τι εκερδισαμεν)': 14917,\n",
       " 'word_bigram(εκερδισαμεν που)': 8931,\n",
       " 'word_bigram(που τους)': 13425,\n",
       " 'word_bigram(τους αδεσμευτους)': 15410,\n",
       " 'char_bigram(κε)': 227,\n",
       " 'char_bigram(ρδ)': 338,\n",
       " 'char_bigram(αδ)': 76,\n",
       " 'char_bigram(ευ)': 153,\n",
       " 'char_trigram(_τι)': 684,\n",
       " 'char_trigram(_εκ)': 529,\n",
       " 'char_trigram(εκε)': 1267,\n",
       " 'char_trigram(κερ)': 1863,\n",
       " 'char_trigram(ερδ)': 1350,\n",
       " 'char_trigram(ρδι)': 2707,\n",
       " 'char_trigram(ισα)': 1787,\n",
       " 'char_trigram(σαμ)': 2857,\n",
       " 'char_trigram(_αδ)': 473,\n",
       " 'char_trigram(αδε)': 760,\n",
       " 'char_trigram(δεσ)': 1143,\n",
       " 'char_trigram(εσμ)': 1372,\n",
       " 'char_trigram(σμε)': 2921,\n",
       " 'char_trigram(μευ)': 2112,\n",
       " 'char_trigram(ευτ)': 1403,\n",
       " 'word(ενομησες)': 4608,\n",
       " 'word(καμνω)': 5127,\n",
       " 'word(ακελικη)': 3586,\n",
       " 'word(προπαγανδα)': 6360,\n",
       " 'word(χοχοι)': 7154,\n",
       " 'word(μα)': 5509,\n",
       " 'word(μας)': 5553,\n",
       " 'word(πελλανετε)': 6103,\n",
       " 'word_bigram(ενομησες οτι)': 9282,\n",
       " 'word_bigram(οτι καμνω)': 12579,\n",
       " 'word_bigram(καμνω ακελικη)': 10484,\n",
       " 'word_bigram(ακελικη προπαγανδα)': 7286,\n",
       " 'word_bigram(προπαγανδα ρε)': 13525,\n",
       " 'word_bigram(ρε χοχοι)': 13653,\n",
       " 'word_bigram(χοχοι μα)': 15884,\n",
       " 'word_bigram(μα εν)': 11101,\n",
       " 'word_bigram(εν να)': 9117,\n",
       " 'word_bigram(να μας)': 11934,\n",
       " 'word_bigram(μας πελλανετε)': 11205,\n",
       " 'char_bigram(μη)': 262,\n",
       " 'char_bigram(μν)': 265,\n",
       " 'char_bigram(νω)': 283,\n",
       " 'char_bigram(ω_)': 446,\n",
       " 'char_bigram(ελ)': 144,\n",
       " 'char_bigram(αγ)': 75,\n",
       " 'char_bigram(δα)': 125,\n",
       " 'char_bigram(οχ)': 315,\n",
       " 'char_trigram(νομ)': 2278,\n",
       " 'char_trigram(ομη)': 2431,\n",
       " 'char_trigram(μησ)': 2123,\n",
       " 'char_trigram(σεσ)': 2881,\n",
       " 'char_trigram(αμν)': 844,\n",
       " 'char_trigram(μνω)': 2147,\n",
       " 'char_trigram(νω_)': 2306,\n",
       " 'char_trigram(_ακ)': 478,\n",
       " 'char_trigram(ακε)': 814,\n",
       " 'char_trigram(κελ)': 1861,\n",
       " 'char_trigram(ελι)': 1285,\n",
       " 'char_trigram(ροπ)': 2798,\n",
       " 'char_trigram(οπα)': 2458,\n",
       " 'char_trigram(παγ)': 2557,\n",
       " 'char_trigram(αγα)': 748,\n",
       " 'char_trigram(γαν)': 1038,\n",
       " 'char_trigram(ανδ)': 852,\n",
       " 'char_trigram(νδα)': 2210,\n",
       " 'char_trigram(δα_)': 1119,\n",
       " 'char_trigram(_χο)': 713,\n",
       " 'char_trigram(χοχ)': 3405,\n",
       " 'char_trigram(οχο)': 2545,\n",
       " 'char_trigram(χοι)': 3396,\n",
       " 'char_trigram(μα_)': 2081,\n",
       " 'char_trigram(_πε)': 644,\n",
       " 'char_trigram(πελ)': 2581,\n",
       " 'char_trigram(ελλ)': 1287,\n",
       " 'char_trigram(λλα)': 2027,\n",
       " 'char_trigram(ανε)': 853,\n",
       " 'char_trigram(νετ)': 2228,\n",
       " 'char_trigram(ετε)': 1382,\n",
       " 'word(μην)': 5615,\n",
       " 'word(παιζετε)': 5968,\n",
       " 'word(τοσο)': 6836,\n",
       " 'word(αριστεροι)': 3836,\n",
       " 'word(αθεοι)': 3562,\n",
       " 'word(φασιστες)': 7006,\n",
       " 'word(ταυτοχρονως)': 6730,\n",
       " 'word(διοτι)': 4295,\n",
       " 'word(ζαλιζομαι)': 4870,\n",
       " 'word(εγκυος)': 4393,\n",
       " 'word_bigram(μην το)': 11462,\n",
       " 'word_bigram(το παιζετε)': 15150,\n",
       " 'word_bigram(παιζετε τοσο)': 12707,\n",
       " 'word_bigram(τοσο αριστεροι)': 15303,\n",
       " 'word_bigram(αριστεροι αθεοι)': 7752,\n",
       " 'word_bigram(αθεοι και)': 7267,\n",
       " 'word_bigram(και φασιστες)': 10354,\n",
       " 'word_bigram(φασιστες ταυτοχρονως)': 15708,\n",
       " 'word_bigram(ταυτοχρονως διοτι)': 14424,\n",
       " 'word_bigram(διοτι ζαλιζομαι)': 8503,\n",
       " 'word_bigram(ζαλιζομαι η)': 9692,\n",
       " 'word_bigram(η εγκυος)': 9763,\n",
       " 'char_bigram(ιζ)': 206,\n",
       " 'char_bigram(ζε)': 160,\n",
       " 'char_bigram(εο)': 148,\n",
       " 'char_bigram(φα)': 413,\n",
       " 'char_bigram(χρ)': 435,\n",
       " 'char_bigram(ωσ)': 460,\n",
       " 'char_bigram(ζα)': 159,\n",
       " 'char_bigram(ζο)': 163,\n",
       " 'char_bigram(εγ)': 137,\n",
       " 'char_bigram(γκ)': 115,\n",
       " 'char_bigram(υο)': 404,\n",
       " 'char_trigram(_μη)': 605,\n",
       " 'char_trigram(μην)': 2120,\n",
       " 'char_trigram(παι)': 2560,\n",
       " 'char_trigram(αιζ)': 800,\n",
       " 'char_trigram(ιζε)': 1696,\n",
       " 'char_trigram(ζετ)': 1449,\n",
       " 'char_trigram(οσο)': 2494,\n",
       " 'char_trigram(σο_)': 2924,\n",
       " 'char_trigram(_αρ)': 485,\n",
       " 'char_trigram(αρι)': 891,\n",
       " 'char_trigram(τερ)': 3015,\n",
       " 'char_trigram(ροι)': 2791,\n",
       " 'char_trigram(_αθ)': 476,\n",
       " 'char_trigram(αθε)': 783,\n",
       " 'char_trigram(θεο)': 1588,\n",
       " 'char_trigram(εοι)': 1327,\n",
       " 'char_trigram(_φα)': 697,\n",
       " 'char_trigram(φασ)': 3277,\n",
       " 'char_trigram(ασι)': 910,\n",
       " 'char_trigram(σισ)': 2907,\n",
       " 'char_trigram(ταυ)': 3001,\n",
       " 'char_trigram(τοχ)': 3075,\n",
       " 'char_trigram(οχρ)': 2546,\n",
       " 'char_trigram(χρο)': 3409,\n",
       " 'char_trigram(ρον)': 2795,\n",
       " 'char_trigram(ονω)': 2445,\n",
       " 'char_trigram(νωσ)': 2311,\n",
       " 'char_trigram(ωσ_)': 3496,\n",
       " 'char_trigram(_δι)': 515,\n",
       " 'char_trigram(διο)': 1164,\n",
       " 'char_trigram(ιοτ)': 1767,\n",
       " 'char_trigram(_ζα)': 543,\n",
       " 'char_trigram(ζαλ)': 1438,\n",
       " 'char_trigram(αλι)': 829,\n",
       " 'char_trigram(λιζ)': 2014,\n",
       " 'char_trigram(ιζο)': 1698,\n",
       " 'char_trigram(ζομ)': 1467,\n",
       " 'char_trigram(ομα)': 2429,\n",
       " 'char_trigram(_εγ)': 524,\n",
       " 'char_trigram(εγκ)': 1215,\n",
       " 'char_trigram(γκυ)': 1086,\n",
       " 'char_trigram(κυο)': 1948,\n",
       " 'char_trigram(υοσ)': 3199,\n",
       " 'word(τελευταια)': 6743,\n",
       " 'word(ευκαιρια)': 4800,\n",
       " 'word(σταματησουμε)': 6587,\n",
       " 'word(τη)': 6803,\n",
       " 'word(βιομηχανικη)': 4005,\n",
       " 'word(γεωργια)': 4096,\n",
       " 'word(απο)': 3769,\n",
       " 'word(καταστρεψει)': 5210,\n",
       " 'word(φυση)': 7087,\n",
       " 'word_bigram(τελευταια ευκαιρια)': 14439,\n",
       " 'word_bigram(ευκαιρια να)': 9550,\n",
       " 'word_bigram(να σταματησουμε)': 12017,\n",
       " 'word_bigram(σταματησουμε τη)': 14031,\n",
       " 'word_bigram(τη βιομηχανικη)': 14690,\n",
       " 'word_bigram(βιομηχανικη γεωργια)': 7970,\n",
       " 'word_bigram(γεωργια απο)': 8067,\n",
       " 'word_bigram(απο το)': 7665,\n",
       " 'word_bigram(να καταστρεψει)': 11917,\n",
       " 'word_bigram(καταστρεψει τη)': 10653,\n",
       " 'word_bigram(τη φυση)': 14726,\n",
       " 'char_bigram(υκ)': 399,\n",
       " 'char_bigram(ιρ)': 216,\n",
       " 'char_bigram(ηχ)': 185,\n",
       " 'char_bigram(γε)': 112,\n",
       " 'char_bigram(ργ)': 337,\n",
       " 'char_bigram(εψ)': 156,\n",
       " 'char_bigram(ψε)': 440,\n",
       " 'char_bigram(φυ)': 423,\n",
       " 'char_trigram(_τε)': 681,\n",
       " 'char_trigram(τελ)': 3010,\n",
       " 'char_trigram(ελε)': 1283,\n",
       " 'char_trigram(λευ)': 1995,\n",
       " 'char_trigram(υτα)': 3236,\n",
       " 'char_trigram(αια)': 795,\n",
       " 'char_trigram(_ευ)': 538,\n",
       " 'char_trigram(ευκ)': 1396,\n",
       " 'char_trigram(υκα)': 3156,\n",
       " 'char_trigram(αιρ)': 808,\n",
       " 'char_trigram(ιρι)': 1782,\n",
       " 'char_trigram(ρια)': 2748,\n",
       " 'char_trigram(στα)': 2950,\n",
       " 'char_trigram(ταμ)': 2994,\n",
       " 'char_trigram(αμα)': 838,\n",
       " 'char_trigram(ησο)': 1551,\n",
       " 'char_trigram(τη_)': 3029,\n",
       " 'char_trigram(βιο)': 1005,\n",
       " 'char_trigram(ιομ)': 1762,\n",
       " 'char_trigram(μηχ)': 2125,\n",
       " 'char_trigram(ηχα)': 1565,\n",
       " 'char_trigram(χαν)': 3356,\n",
       " 'char_trigram(νικ)': 2257,\n",
       " 'char_trigram(_γε)': 502,\n",
       " 'char_trigram(γεω)': 1059,\n",
       " 'char_trigram(εωρ)': 1432,\n",
       " 'char_trigram(ωργ)': 3489,\n",
       " 'char_trigram(ργι)': 2702,\n",
       " 'char_trigram(πο_)': 2631,\n",
       " 'char_trigram(ατα)': 922,\n",
       " 'char_trigram(τασ)': 2999,\n",
       " 'char_trigram(στρ)': 2955,\n",
       " 'char_trigram(τρε)': 3077,\n",
       " 'char_trigram(ρεψ)': 2727,\n",
       " 'char_trigram(εψε)': 1426,\n",
       " 'char_trigram(ψει)': 3432,\n",
       " 'char_trigram(_φυ)': 706,\n",
       " 'char_trigram(φυσ)': 3341,\n",
       " 'char_trigram(υση)': 3227,\n",
       " 'word(στριμωξαμε)': 6616,\n",
       " 'word(διαδικτυο)': 4240,\n",
       " 'word(ανθρωπους)': 3693,\n",
       " 'word(λεξεις)': 5447,\n",
       " 'word(σκεψεις)': 6538,\n",
       " 'word(μουσικη)': 5681,\n",
       " 'word(αισθηματα)': 3575,\n",
       " 'word_bigram(στριμωξαμε στο)': 14191,\n",
       " 'word_bigram(στο διαδικτυο)': 14129,\n",
       " 'word_bigram(διαδικτυο ανθρωπους)': 8440,\n",
       " 'word_bigram(ανθρωπους λεξεις)': 7545,\n",
       " 'word_bigram(λεξεις σκεψεις)': 10992,\n",
       " 'word_bigram(σκεψεις μουσικη)': 13912,\n",
       " 'word_bigram(μουσικη αισθηματα)': 11716,\n",
       " 'char_bigram(μω)': 270,\n",
       " 'char_bigram(ωξ)': 457,\n",
       " 'char_bigram(ξα)': 285,\n",
       " ...}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.vocabulary_ # The numbers are not counts but indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16007"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(count_vect.vocabulary_) # This is the same as the length of each vector."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Building the classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "def show_performance_header_and_accuracy(predictions):\n",
    "    print('\\t\\t\\tPERFORMANCE\\n')\n",
    "    print('Accuracy:', round(accuracy_score(test_set_labels, predictions), 2), '\\n')\n",
    "\n",
    "def show_confusion_matrix(cm):\n",
    "    print('\\t         Predicted')\n",
    "    print('\\t        CG       SMG')\n",
    "    print('\\t     -------- --------')\n",
    "    print('\\tCG  | {:^6} | {:^6}'.format(cm[0][0], cm[0][1]))\n",
    "    print('Actual\\t     -------- --------')\n",
    "    print('\\tSMG | {:^6} | {:^6}'.format(cm[1][0], cm[1][1]))\n",
    "\n",
    "def show_most_informative_features(vectorizer, clf, n=10):\n",
    "    print(\"\\t\\t    CG\\t\\t\\t\\t\\t\\t    SMG\\n\")\n",
    "    feature_names = vectorizer.get_feature_names()\n",
    "    coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))\n",
    "    top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n + 1):-1])\n",
    "    for (coef_1, fn_1), (coef_2, fn_2) in top:\n",
    "        print(\"\\t%.4f\\t%17s\\t\\t\\t%.4f\\t%17s\" % (coef_1, fn_1, coef_2, fn_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 Multinomial Naive Bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_multinomialNB = MultinomialNB() # There are no params for MultinomialDB that prevent overfitting, so any overfitting is caused by the small dataset size.\n",
    "clf_multinomialNB.fit(train_set_vectors, train_set_labels) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tPERFORMANCE\n",
      "\n",
      "Accuracy: 0.96 \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         CG       0.94      0.98      0.96       109\n",
      "        SMG       0.98      0.93      0.95        99\n",
      "\n",
      "avg / total       0.96      0.96      0.96       208\n",
      "\n",
      "\t         Predicted\n",
      "\t        CG       SMG\n",
      "\t     -------- --------\n",
      "\tCG  |  107   |   2   \n",
      "Actual\t     -------- --------\n",
      "\tSMG |   7    |   92  \n"
     ]
    }
   ],
   "source": [
    "clf_multinomialNB_predictions = clf_multinomialNB.predict(test_set_vectors)\n",
    "\n",
    "show_performance_header_and_accuracy(clf_multinomialNB_predictions)\n",
    "\n",
    "print(classification_report(test_set_labels, clf_multinomialNB_predictions))\n",
    "\n",
    "cmatrix = confusion_matrix(test_set_labels, clf_multinomialNB_predictions)\n",
    "show_confusion_matrix(cmatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t    CG\t\t\t\t\t\t    SMG\n",
      "\n",
      "\t-11.2815\t  char_bigram(αα)\t\t\t-5.3154\t          char(α)\n",
      "\t-11.2815\t  char_bigram(β_)\t\t\t-5.3231\t          char(ι)\n",
      "\t-11.2815\t  char_bigram(δ_)\t\t\t-5.3309\t          char(ο)\n",
      "\t-11.2815\t  char_bigram(δκ)\t\t\t-5.3335\t          char(ε)\n",
      "\t-11.2815\t  char_bigram(ηα)\t\t\t-5.3413\t          char(τ)\n",
      "\t-11.2815\t  char_bigram(ηβ)\t\t\t-5.3466\t          char(ν)\n",
      "\t-11.2815\t  char_bigram(ηε)\t\t\t-5.3519\t          char(σ)\n",
      "\t-11.2815\t  char_bigram(ηο)\t\t\t-5.4010\t  char_bigram(α_)\n",
      "\t-11.2815\t  char_bigram(θθ)\t\t\t-5.4122\t          char(υ)\n",
      "\t-11.2815\t  char_bigram(θκ)\t\t\t-5.4179\t          char(ρ)\n",
      "\t-11.2815\t  char_bigram(ιυ)\t\t\t-5.4293\t          char(κ)\n",
      "\t-11.2815\t  char_bigram(οζ)\t\t\t-5.4467\t          char(μ)\n",
      "\t-11.2815\t  char_bigram(πδ)\t\t\t-5.4526\t          char(π)\n",
      "\t-11.2815\t  char_bigram(πκ)\t\t\t-5.4644\t  char_bigram(ι_)\n",
      "\t-11.2815\t  char_bigram(ππ)\t\t\t-5.4885\t          char(η)\n",
      "\t-11.2815\t  char_bigram(φκ)\t\t\t-5.4915\t          char(λ)\n",
      "\t-11.2815\t  char_bigram(ωε)\t\t\t-5.5008\t  char_bigram(_τ)\n",
      "\t-11.2815\tchar_trigram(_α_)\t\t\t-5.6013\t  char_bigram(ου)\n",
      "\t-11.2815\tchar_trigram(_αο)\t\t\t-5.6013\t  char_bigram(ο_)\n",
      "\t-11.2815\tchar_trigram(_γω)\t\t\t-5.6116\t  char_bigram(ει)\n"
     ]
    }
   ],
   "source": [
    "show_most_informative_features(count_vect, clf_multinomialNB, n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Linear Support Vector classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1500,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_linearSVC = LinearSVC(max_iter=1500) # n_samples < n_features in training set so the dual param is kept at its default value of True. Default max_iter = 1000\n",
    "clf_linearSVC.fit(train_set_vectors, train_set_labels) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tPERFORMANCE\n",
      "\n",
      "Accuracy: 0.91 \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         CG       0.92      0.90      0.91       109\n",
      "        SMG       0.89      0.92      0.91        99\n",
      "\n",
      "avg / total       0.91      0.91      0.91       208\n",
      "\n",
      "\t         Predicted\n",
      "\t        CG       SMG\n",
      "\t     -------- --------\n",
      "\tCG  |   98   |   11  \n",
      "Actual\t     -------- --------\n",
      "\tSMG |   8    |   91  \n"
     ]
    }
   ],
   "source": [
    "clf_linearSVC_predictions = clf_linearSVC.predict(test_set_vectors)\n",
    "\n",
    "show_performance_header_and_accuracy(clf_linearSVC_predictions)\n",
    "\n",
    "print(classification_report(test_set_labels, clf_linearSVC_predictions))\n",
    "\n",
    "cmatrix = confusion_matrix(test_set_labels, clf_linearSVC_predictions)\n",
    "show_confusion_matrix(cmatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t    CG\t\t\t\t\t\t    SMG\n",
      "\n",
      "\t-0.3471\tchar_trigram(εν_)\t\t\t0.1548\t        word(και)\n",
      "\t-0.3013\t         word(εν)\t\t\t0.1405\t  char_bigram(λυ)\n",
      "\t-0.2699\t  char_bigram(τζ)\t\t\t0.1393\tchar_trigram(αλλ)\n",
      "\t-0.2593\tchar_trigram(_τζ)\t\t\t0.1342\t  char_bigram(_β)\n",
      "\t-0.2262\t  char_bigram(αμ)\t\t\t0.1301\t        word(δεν)\n",
      "\t-0.1907\t  char_bigram(_ε)\t\t\t0.1301\tchar_trigram(δεν)\n",
      "\t-0.1864\t  char_bigram(θκ)\t\t\t0.1296\tchar_trigram(τασ)\n",
      "\t-0.1544\tchar_trigram(καμ)\t\t\t0.1294\tchar_trigram(ινα)\n",
      "\t-0.1523\tchar_trigram(_ου)\t\t\t0.1252\t  char_bigram(να)\n",
      "\t-0.1464\tchar_trigram(_εν)\t\t\t0.1212\t  char_bigram(_ξ)\n",
      "\t-0.1449\t          char(π)\t\t\t0.1206\t          char(χ)\n",
      "\t-0.1382\tchar_trigram(θκι)\t\t\t0.1147\t  char_bigram(τα)\n",
      "\t-0.1362\t          char(ζ)\t\t\t0.1132\tchar_trigram(_τα)\n",
      "\t-0.1335\t  char_bigram(_λ)\t\t\t0.1106\t  char_bigram(ικ)\n",
      "\t-0.1331\tchar_trigram(_λα)\t\t\t0.1078\tchar_trigram(ιστ)\n",
      "\t-0.1299\t  char_bigram(φκ)\t\t\t0.1074\t          char(δ)\n",
      "\t-0.1257\t  char_bigram(νν)\t\t\t0.1070\tchar_trigram(θελ)\n",
      "\t-0.1216\tchar_trigram(αμα)\t\t\t0.1070\tchar_trigram(μαλ)\n",
      "\t-0.1213\tchar_trigram(ιαν)\t\t\t0.1034\t         word(τα)\n",
      "\t-0.1189\tchar_trigram(υλλ)\t\t\t0.1029\t          char(ξ)\n"
     ]
    }
   ],
   "source": [
    "show_most_informative_features(count_vect, clf_linearSVC, n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Logistic Regression classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_logisticRegression = LogisticRegression() # Again, dual = True. Default solver = 'liblinear'. It's recommended for smaller databases. For bigger databases, 'saga' could be used.\n",
    "clf_logisticRegression.fit(train_set_vectors, train_set_labels) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t\tPERFORMANCE\n",
      "\n",
      "Accuracy: 0.91 \n",
      "\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "         CG       0.94      0.89      0.92       109\n",
      "        SMG       0.89      0.94      0.91        99\n",
      "\n",
      "avg / total       0.92      0.91      0.91       208\n",
      "\n",
      "\t         Predicted\n",
      "\t        CG       SMG\n",
      "\t     -------- --------\n",
      "\tCG  |   97   |   12  \n",
      "Actual\t     -------- --------\n",
      "\tSMG |   6    |   93  \n"
     ]
    }
   ],
   "source": [
    "clf_logisticRegression_predictions = clf_logisticRegression.predict(test_set_vectors)\n",
    "\n",
    "show_performance_header_and_accuracy(clf_logisticRegression_predictions)\n",
    "\n",
    "print(classification_report(test_set_labels, clf_logisticRegression_predictions))\n",
    "\n",
    "cmatrix = confusion_matrix(test_set_labels, clf_logisticRegression_predictions)\n",
    "show_confusion_matrix(cmatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t\t    CG\t\t\t\t\t\t    SMG\n",
      "\n",
      "\t-1.2587\tchar_trigram(εν_)\t\t\t0.6666\t        word(και)\n",
      "\t-1.1392\t         word(εν)\t\t\t0.4642\tchar_trigram(δεν)\n",
      "\t-1.0289\t  char_bigram(τζ)\t\t\t0.4625\t  char_bigram(λυ)\n",
      "\t-0.9605\tchar_trigram(_τζ)\t\t\t0.4611\t        word(δεν)\n",
      "\t-0.8454\t  char_bigram(αμ)\t\t\t0.4540\tchar_trigram(ινα)\n",
      "\t-0.6719\t  char_bigram(θκ)\t\t\t0.4383\t  char_bigram(_β)\n",
      "\t-0.6625\t  char_bigram(_ε)\t\t\t0.4321\t  char_bigram(να)\n",
      "\t-0.6533\tchar_trigram(_εν)\t\t\t0.4241\t          char(χ)\n",
      "\t-0.5861\tchar_trigram(καμ)\t\t\t0.4208\t          char(δ)\n",
      "\t-0.5038\t          char(ζ)\t\t\t0.4150\tchar_trigram(και)\n",
      "\t-0.4929\tchar_trigram(θκι)\t\t\t0.4109\tchar_trigram(αλλ)\n",
      "\t-0.4926\tchar_trigram(τζι)\t\t\t0.4021\t  char_bigram(τα)\n",
      "\t-0.4795\tchar_trigram(_ου)\t\t\t0.3963\tchar_trigram(_απ)\n",
      "\t-0.4702\t  char_bigram(ζι)\t\t\t0.3951\t  char_bigram(ικ)\n",
      "\t-0.4546\t  char_bigram(φκ)\t\t\t0.3829\tchar_trigram(τασ)\n",
      "\t-0.4521\tchar_trigram(αμα)\t\t\t0.3620\tchar_trigram(ιστ)\n",
      "\t-0.4311\t          char(π)\t\t\t0.3611\t      word(ειναι)\n",
      "\t-0.4307\tchar_trigram(_λα)\t\t\t0.3548\tchar_trigram(αυτ)\n",
      "\t-0.4295\t  char_bigram(_λ)\t\t\t0.3489\tchar_trigram(_ει)\n",
      "\t-0.4239\t  char_bigram(εν)\t\t\t0.3477\tchar_trigram(μαλ)\n"
     ]
    }
   ],
   "source": [
    "show_most_informative_features(count_vect, clf_logisticRegression, n=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**It seems that the classification algorithm with the best performance is *Multinomial Naive Bayes***."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Analyzing misclassifications made by the Multinomial Naive Bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MISCLASSIFICATIONS\n",
      "\n",
      "1. ολοι ετσι κανουν (CORRECT = SMG, PREDICTED = CG)\n",
      "\n",
      "2. μιλω σας ποιος με ειδεν και δεν με φοβηθηκε (CORRECT = CG, PREDICTED = SMG)\n",
      "\n",
      "3. το προβλημα με το παρανομο κυνηγι εξω εχει παραγινει (CORRECT = SMG, PREDICTED = CG)\n",
      "\n",
      "4. αν εμεις δεν καταφεραμε τοτε οσα λαχταρησαμε δεν ξεσπαμε πανω τους (CORRECT = SMG, PREDICTED = CG)\n",
      "\n",
      "5. παστιτσιο και παντζαρια σαλατα το μενου για σημερα καλη μας ορεξη (CORRECT = SMG, PREDICTED = CG)\n",
      "\n",
      "6. πολλα φοουμαι πως το ακελ κατερριψε και αυτο (CORRECT = CG, PREDICTED = SMG)\n",
      "\n",
      "7. ισως γιατι δεν θυμομαστε καλα (CORRECT = SMG, PREDICTED = CG)\n",
      "\n",
      "8. τελικα οι πορνες δεν ηταν και τοσο αλλοδαπες αλλα τι σημασια εχει η δουλεια εγινε (CORRECT = SMG, PREDICTED = CG)\n",
      "\n",
      "9. στη ζωη μου εισαι γουρι στο τζατζικι το αγγουρι (CORRECT = SMG, PREDICTED = CG)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('MISCLASSIFICATIONS\\n')\n",
    "\n",
    "misclassificationCount = 0\n",
    "\n",
    "for i, sent in enumerate(test_set_sents):\n",
    "    if test_set_labels[i] != clf_multinomialNB_predictions[i]:\n",
    "        misclassificationCount += 1\n",
    "        print(f'{misclassificationCount}.', sent, f'(CORRECT = {test_set_labels[i]},', f'PREDICTED = {clf_multinomialNB_predictions[i]})\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Trying the Multinomial Naive Bayes classifier with custom input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, a more powerful version of the classifier is built by using all the data available:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_set_sents = [sent[0] for sent in all_sents_labeled]\n",
    "full_set_labels = [sent[1] for sent in all_sents_labeled]\n",
    "full_set_vectors = count_vect.fit_transform(full_set_sents)\n",
    "\n",
    "clf_super_multinomialNB = MultinomialNB()\n",
    "clf_super_multinomialNB.fit(full_set_vectors, full_set_labels) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trying 2 custom sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SENTENCE 1: “Η Κύπρος εν που τες πιο όμορφες χώρες.”\n",
      "PREDICTION: Cypriot Greek (Confidence: 1.00)\n",
      "\n",
      "SENTENCE 2: “Η Κύπρος είναι από τις πιο όμορφες χώρες.”\n",
      "PREDICTION: Standard Modern Greek (Confidence: 1.00)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cgSent = 'Η Κύπρος εν που τες πιο όμορφες χώρες.'\n",
    "smgSent = 'Η Κύπρος είναι από τις πιο όμορφες χώρες.'\n",
    "\n",
    "demoSentences = [cgSent, smgSent]\n",
    "\n",
    "cgSent = get_clean_sent_el(cgSent)\n",
    "smgSent = get_clean_sent_el(smgSent)\n",
    "\n",
    "test_vec = count_vect.transform([cgSent, smgSent])\n",
    "\n",
    "for sentenceNumber, predictionArr in enumerate(clf_super_multinomialNB.predict_proba(test_vec)):\n",
    "    print(f'SENTENCE {sentenceNumber + 1}: “{demoSentences[sentenceNumber]}”')\n",
    "    if predictionArr[0] > predictionArr[1]:\n",
    "        print(f'PREDICTION: Cypriot Greek (Confidence: {predictionArr[0]:.2f})\\n')\n",
    "    else:\n",
    "        print(f'PREDICTION: Standard Modern Greek (Confidence: {predictionArr[1]:.2f})\\n')"
   ]
  }
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