Exploratory Emoji Analysis in the Saudi Twitter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; font-size:30px; text-align:center; font-weight: bold;\" >\n",
    "تحليل استكشافي لاستخدام الوجوه التعبيرية في المجتمع السعودي (عبر موقع تويتر)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; font-size:20px; text-align:center; font-style: italic\" > \n",
    "بقلم : فهد الحازمي (<a href=\"https://twitter.com/fahd09\">Twitter</a> | <a href=\"https://github.com/fahd09\">Github</a>)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right\"> \n",
    "شكل الانتشار العالمي للوجوه التعبيرية - التي تعرف بالايموجي- بداية ثورة جديدة في فضاء اللغة الالكترونية، حيث أصبحت وسيلة لغوية وتواصلية لا غنى عنها خصوصاً لدى الأجيال الشابة.\n",
    "\n",
    "في هذا التحليل نحاول أن نلتمس أبعاد هذ التغير في أحد أشهر مواقع التواصل الالكتروني، تويتر. حيث سنقوم بقراءة عدد ضخم من التغريدات ومن كافة مناطق المملكة العربية السعودية ومن ثم البحث عن الوجوه التعبيرية وتحليل ما يمكن أن تعكسه من متغيرات اجتماعية.\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "سنقوم أولاً باستيراد المكتبات اللازمة في بايثون وتثبيت بعض المتغيرات..\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right\">\n",
    "\n",
    "جمعت التغريدات كالتالي. أولاً، جهزت قائمة بأسماء بعض المدن مع خطوط الطول وخطوط العرض. تم استخدمت الواجهة البرمجية لموقع تويتر (Twitter Search API) مدعوماً بمكتبة (Tweepy) في بايثون لقراءة آلاف التغريدات التي تحتوي على أي وجه تعبيري ضمن قائمة 74 وجه تعبيري شائع. ثم حفظت نتائج البحث في جدول مستقل يحتوي على المدينة، تاريخ التغريدة، نص التغريدة وأخيراً مصدر التغريدة (البرنامج المستخدم لكتابتها). بالطبع، لا غنى عن مكتبة pandas في التعامل مع الجداول.\n",
    "\n",
    "<br /> <br />\n",
    "يلي استيراد قاعدة البيانات طباعة رأس الجدول لنتأكد من سلامة البيانات..\n",
    "\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tweets = pd.read_csv('./data/tweets_emojis_dataset_74emojis.csv', encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(140656, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>created_at</th>\n",
       "      <th>text</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Riyadh</td>\n",
       "      <td>Wed Jul 12 18:47:24 +0000 2017</td>\n",
       "      <td>#امسيه_منيف_الخمشي\\n\\nيكفي انها كلمات مؤثرة تت...</td>\n",
       "      <td>Twitter Web Client</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Riyadh</td>\n",
       "      <td>Wed Jul 12 18:46:49 +0000 2017</td>\n",
       "      <td>وإن أحبُّوك ألفاً فلن يحبُّوك إلا قِطرة من بحري❤️</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Riyadh</td>\n",
       "      <td>Wed Jul 12 18:46:49 +0000 2017</td>\n",
       "      <td>البيت الاخير يختصر كل المعاناة ❤️❤️😢 https://t...</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Riyadh</td>\n",
       "      <td>Wed Jul 12 18:46:31 +0000 2017</td>\n",
       "      <td>هلا هلا ❤❤😢 miss you \\nالطقم حكايه ثانيه افضل ...</td>\n",
       "      <td>Twitter for Android</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Riyadh</td>\n",
       "      <td>Wed Jul 12 18:46:30 +0000 2017</td>\n",
       "      <td>قلب قلببببي❤️❤️❤️❤️.</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city                      created_at  \\\n",
       "0  Riyadh  Wed Jul 12 18:47:24 +0000 2017   \n",
       "1  Riyadh  Wed Jul 12 18:46:49 +0000 2017   \n",
       "2  Riyadh  Wed Jul 12 18:46:49 +0000 2017   \n",
       "3  Riyadh  Wed Jul 12 18:46:31 +0000 2017   \n",
       "4  Riyadh  Wed Jul 12 18:46:30 +0000 2017   \n",
       "\n",
       "                                                text               source  \n",
       "0  #امسيه_منيف_الخمشي\\n\\nيكفي انها كلمات مؤثرة تت...   Twitter Web Client  \n",
       "1  وإن أحبُّوك ألفاً فلن يحبُّوك إلا قِطرة من بحري❤️   Twitter for iPhone  \n",
       "2  البيت الاخير يختصر كل المعاناة ❤️❤️😢 https://t...   Twitter for iPhone  \n",
       "3  هلا هلا ❤❤😢 miss you \\nالطقم حكايه ثانيه افضل ...  Twitter for Android  \n",
       "4                               قلب قلببببي❤️❤️❤️❤️.   Twitter for iPhone  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right\">\n",
    "\n",
    "كلنا نعرف كمية التغريدات المزعجة (السبام) في تويتر العربي. ابتدا من \" تبي ينلحس مخك؟\" إلى الإعلانات الغثيثة ومروراً بحسابات الأدعية والأذكاء والتي سرعان ما تصل إلى أعداد متابعين كبيرة لتُباع مرة أخرى لمن أحب بريق الشهرة. على أية حال، هنا طريقة بسيطة وفعالة لفلترة التغريدات المزعجة وذلك بتحديد مصدر التغريدة. إن كنت مهتماً بمجال تحليل تغريدات تويتر وتواجه مشكلة في التغريدات المزعجة فلدي أفكار أخرى قليلة لا أمانع من مشاركتها، ولكن هذه الطريقة أدنا (الفلترة بالمصدر) هي الطريقة الأكثر فعالية.\n",
    "\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "good_sources = ['Twitter Web Client', 'Twitter for iPhone', 'Twitter for Android', 'Twitter for iPad', 'Twitter Lite']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "tweets = tweets[tweets.source.isin(good_sources)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right\">\n",
    "كإجراء احتياطي، سنقوم بحذف الصفوف المكررة إن كان هناك صفوف مكررة حتى يحوي كل صف على تغريدة فريدة وغير مكررة. يلي ذلك طباعة عدد التغريدات في الجدول ألا وهو 134763 وهو عدد لا بأس به لغرض الاستكشاف\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tweets.drop_duplicates(inplace=True)\n",
    "tweets.reset_index(inplace=True, drop=['index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tweets:  134763\n"
     ]
    }
   ],
   "source": [
    "print('Total number of tweets: ', len(tweets))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; font-size:25px; text-align:right; font-weight: bold;\" > \n",
    "تنظيف البيانات"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right\"> \n",
    "في الخانات التالية، سنقوم بأصعب مهمة في مجال تحليل البيانات، ألا هي تنظيف البيانات. ما يزيد المهمة صعوبة هو تنظيف النصوص العربية نظراً لوجود الكثير من الأخطاء الإملائية في النصوص العربية والزخارف وما إلى ذلك.\n",
    "\n",
    "<br /> <br /> \n",
    "\n",
    "هناك الكثير من الخطوات التي يمكن عملها في مهمة تنظيف نص التغريدات ولكني اقتصرت على خطوات أساسية أعتقد أنها كافية لإعطاء نتائج على قدر لا بأس به من الدقة للمجالات العامة. بالمناسبة، نحن لن نحتاج إلى أي نصوص في هذه التدوينة سوى الوجوه التعبيرية ( والتي لا تستدعي كل هذه الخطوات على أية حال). ولكن لأني أيضاً قمت بالعديد من التحليلات الجانبية المتعلقة بنصوص التغريدات والتي قد لا يتسع المجال للحديث عنها.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def denoise_arabic(text):\n",
    "    noise = re.compile(\"\"\" ّ    | # Tashdid\n",
    "                             َ    | # Fatha\n",
    "                             ً    | # Tanwin Fath\n",
    "                             ُ    | # Damma\n",
    "                             ٌ    | # Tanwin Damm\n",
    "                             ِ    | # Kasra\n",
    "                             ٍ    | # Tanwin Kasr\n",
    "                             ْ    | # Sukun\n",
    "                             ـ     # Tatwil/Kashida\n",
    "                         \"\"\", re.VERBOSE)\n",
    "    text = re.sub(noise, '', text)\n",
    "    return text\n",
    "\n",
    "def normalize_arabic(text):\n",
    "    text = re.sub(\"[إأٱآا]\", \"ا\", text, flags=re.MULTILINE)\n",
    "    text = re.sub(\"ى\", \"ي\", text, flags=re.UNICODE)\n",
    "    text = re.sub(\"ؤ\", \"ء\", text, flags=re.UNICODE)\n",
    "    text = re.sub(\"ئ\", \"ء\", text, flags=re.UNICODE)\n",
    "    return text\n",
    "\n",
    "def clean_tweet(text):\n",
    "    URL_re = r'(http|https|ftp)://[a-zA-Z0-9\\\\./]+' # replace URLs\n",
    "    HASH_re = r'#(\\w+)' # replace Hashtags\n",
    "    HANDLE_re = r'@(\\w+)' # replace Handles\n",
    "    REPUNC_re = r'[\\?\\.\\!]+(?=[\\?\\.\\!])' # normalize repeating punctuations (e.g., ?? or !!!)\n",
    "    RECHAR_re = r'(\\w)\\1{1,}' # remove repeating charachters \n",
    "    NEWLINE_re = r'[\\n]+' # remove new lines \n",
    "    NUM_CHARS_AR_re = r'[١٢٣٤٥٦٧٨٩٠؟!٪:،؛.]+'\n",
    "    NUM_CHARS_EN_re = r'[0123456789!\"$%&\\\\\\'()*+,-.;<>=?@[]^_`{═}—“”•‹|~«°»˓ ̮ ̯]+'\n",
    "    WHITE_SPACE_re = r'\\s+'\n",
    "    \n",
    "    text = re.sub(URL_re,  r'', text, flags=re.UNICODE)\n",
    "    text = re.sub(HASH_re,  r'', text, flags=re.UNICODE)\n",
    "    text = re.sub(HANDLE_re,  r'', text, flags=re.UNICODE)\n",
    "    text = re.sub(REPUNC_re,  r' ', text, flags=re.UNICODE)\n",
    "    text = re.sub(RECHAR_re,  r'\\1', text, flags=re.UNICODE)\n",
    "    text = re.sub(NEWLINE_re,  r'', text, flags=re.UNICODE)\n",
    "    text = re.sub(NUM_CHARS_AR_re,  r'', text, flags=re.UNICODE)\n",
    "    text = re.sub(NUM_CHARS_EN_re,  r'', text, flags=re.UNICODE)\n",
    "    text = re.sub(WHITE_SPACE_re,  r' ', text, flags=re.UNICODE)\n",
    "    \n",
    "    return text\n",
    "\n",
    "tweets.text = tweets.text.apply(lambda x: denoise_arabic(str(x)))\n",
    "tweets.text = tweets.text.apply(lambda x: normalize_arabic(str(x)))\n",
    "tweets.text = tweets.text.apply(lambda x: clean_tweet(str(x)))\n",
    "toremove = '! ... ? ! \\u200b ...  ?  y ֆ ⤵ ⇜ ✎ ❃ ❥ ༺ ༻ ┄ ࿐ | ᅠ\\u200b \\u200c \\u200d — “ ” • ‹ \\u2066 \\u2067 \\u2069 ⃣ ↓ ↴ ⇣ ⌝ ┈ ┉ ┊ ═ ! \" # $ % & \\' ( ) * + , - ... / 0 1 10 10/10 12 13 1373 14 140جرام 15 16 17 17,0 18 19 1935 2 20 2015 2017 2020 2030 21 24 25 26 27 28 2leh_q 3 30 36 4 40 5 5/5 50 6 60 61 7 7/7 70 8 80 9 90 ; < = > ? @ [ ] ^ _ _20 ` { } ~ « ° » ˓ ̮ ̯'        \n",
    "regex = re.compile(\"(\" + \"|\".join(map(re.escape, toremove.split(' '))) + \")\")\n",
    "tweets.text = tweets.text.apply(lambda x: re.sub(regex, r'', x))\n",
    "tweets.text = tweets.text.apply(lambda x: re.sub(r'[0123456789A-Za-z?!]', r'', x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right\"> \n",
    "بعد أن انتهيت من تنظيف النصوص، اكتشفت أن خانة \" المدينة\" في عدد قليل جداً من الصفوف لم يكن صحيحا. لتسوية الأمر، سنقرأ جدول المدن ثم نفلتر كل الصفوف الخاطئة.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cities = pd.read_csv('./data/cities_lat_long.csv')\n",
    "tweets = tweets[tweets.city.isin(cities.city.tolist())]\n",
    "tweets.reset_index(inplace=True, drop=['index'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right\">\n",
    "هذه هي قائمة المدن في قاعدة البيانات الحالية..\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Riyadh', 'Tabuk', 'Jeddah', 'Hail', 'Mecca', 'Medina', 'Jubail',\n",
       "       'Hofuf', 'Jazan', 'Al-Kharj', 'Taif', 'Qatif', 'Dammam', 'Abha',\n",
       "       'Khamis Mushait', 'Najran', 'Buraidah', 'Yanbu', 'Khobar'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets.city.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; font-size:25px; text-align:right; font-weight: bold;\" >\n",
    "تحليل البيانات الاستكشافي (Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "نبدأ الآن استكشاف البيانات. استكشاف البيانات هي عملية إبداعية وفضولية في المقام الأول، إذا لا يوجد لها أي قواعد تحكمها ولا مبادئ تقف عندها. الهدف هو أن نفهم بنية البيانات وطبيعتها بشكل أفضل. تقوم هذه المرحلة بشكل أساسي على التساؤلات التي نود الإجابة عنها. أدناه سردت أبرز الأسئلة التي طالما كانت حاضرة في ذهني ودفعتني للقيام بهذا المشروع..\n",
    "<br>\n",
    "<ul style=\"direction:rtl\">\n",
    "<li>ماهي الرموز التعبيرية الأكثر استخداماً في جميع المدن وماهي الرموز التعبيرية الأقل شيوعاً؟ وهل تشبه هذه القائمة موقع <a href=\"http://emojitracker.com\">EmojiTracker</a> المختص برصد الوجوه التعبيرية الأكثر استخداماً في موقع تويتر؟</li>\n",
    "<li>ماهي المدن الأكثر استخداماً للرموز التعبيرية وماهي المدن الأقل استخداماً للرموز التعبيرية؟</li>\n",
    "<li>هل هناك رموز تعبيرية محددة تستخدم بشكل أكثر في كل مدينة؟ مثلاً هل هناك رموز تعبيرية يستخدمها سكان جدة بنسبة أعلى من سكان الدمام؟</li>\n",
    "<li>هناك رموز تعبيرية \"إيجابية\" مثل القلب والورد والوجه الضاحك، بينما هناك رموز تعبيرية سلبية مثل القلب المكسور والوجه الباكي وغيرها. هل نستطيع معرفة أي المدن أكثر استخداماً للرموز الإيجابية وأيها أكثر استخداماً للرموز السلبية؟</li>\n",
    "<li>أيضاً، بما أن لدينا معلومات حول وقت كتابة التغريدات، هل حديث الليل يشبه حديث النهار؟</li>\n",
    "</ul>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "لا أحب أن أستطرد في الأسئلة التي يمكننا البحث عنها حيث أن هذه البيانات غنية وثرية جداً. كل هذه الأسئلة ونحن لم نستخدم بعد نصوص التغريدات أو علاقات المغردين ببعضهم البعض وغير ذلك من الأسئلة التي تصلح لمشاريع قادمة. في هذا المشروع سوف نركز فقط على أنماط استخدام الرموز التعبيرية بين مجموعة من مدن المملكة..\n",
    "<br/> <br/>\n",
    "الآن سنبدأ بتجهيز البيانات بشكل مناسب للإجابة عن الأسئلة. بكل بساطة، سنجهز جدولاً آخر للتغريدات بحيث تبقى الصفوف تمثل التغريدات ولكن الأعمدة هذه المرة تمثل الرموز التعبيرية. وكل رمز تعبيري يستخدم سيكون له قيمة 1 فيما ستكون الباقية 0. ومن هذا الجدول نستطيع المضي قدماً في التحليل. \n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "emojis_to_extract = '☀ ☁ ☄ ★ ☎ ☔ ☕ ☘ ☝ ☹ ☺ ☻ ♀ ♂ ♠ ♡ ♥ ♨ ♩ ♪ ⚕ ⚘ ⚜ ⚡ ⚫ ⚽ ⛅ ✅ ✈ ✉ ✋ ✌ ✍ ✔ ✨ ✿ ❀ ❁ ❃ ❄ ❋ ❌ ❕ ❗ ❣ ❤ ❥ ⤵ ⬇ ⭐ ️ 🇦 🇧 🇨 🇪 🇬 🇭 🇮 🇰 🇱 🇲 🇳 🇵 🇶 🇷 🇸 🇹 🇺 🇼 🇾 🌈 🌌 🌎 🌑 🌕 🌙 🌚 🌝 🌞 🌟 🌤 🌥 🌧 🌨 🌬 🌱 🌲 🌴 🌷 🌸 🌹 🌺 🌻 🌼 🌾 🌿 🍀 🍁 🍂 🍃 🍆 🍇 🍌 🍏 🍑 🍒 🍔 🍥 🍦 🍫 🍭 🍰 🍳 🍺 🍻 🍼 🎀 🎁 🎂 🎈 🎉 🎊 🎓 🎤 🎧 🎨 🎬 🎭 🎵 🎶 🎷 🎹 🎻 🎼 🏁 🏃 🏆 🏳 🏹 🏻 🏼 🏽 🏾 🏿 🐒 🐙 🐥 🐸 👀 👄 👅 👆 👇 👈 👉 👊 👋 👌 👍 👎 👏 👐 👑 👞 👧 👨 👩 👪 👭 👰 👵 👶 👸 👻 👼 👽 💀 💁 💃 💅 💆 💉 💋 💌 💍 💎 💐 💓 💔 💕 💖 💗 💘 💙 💚 💛 💜 💝 💞 💟 💡 💢 💣 💤 💥 💦 💧 💨 💩 💪 💫 💬 💭 💯 💰 💵 💸 📌 📍 📖 📚 📝 📞 📣 📩 📮 📯 📱 📲 📷 📻 🔁 🔐 🔑 🔕 🔗 🔝 🔞 🔥 🔪 🔫 🔴 🔵 🔶 🔹 🔻 🕊 🕋 🕑 🕛 🕞 🕺 🖐 🖒 🖕 🖖 🖤 🗓 🗝 🗣 😀 😁 😂 😃 😄 😅 😆 😇 😈 😉 😊 😋 😌 😍 😎 😏 😐 😑 😒 😓 😔 😕 😖 😗 😘 😙 😚 😛 😜 😝 😞 😟 😠 😡 😢 😣 😤 😥 😦 😧 😨 😩 😪 😫 😬 😭 😮 😯 😰 😱 😲 😳 😴 😶 😷 😹 😻 😼 😽 😾 😿 🙀 🙁 🙂 🙃 🙄 🙅 🙆 🙇 🙈 🙉 🙊 🙋 🙌 🙍 🙏 🚀 🚗 🚫 🚬 🚶 🛑 🤐 🤒 🤓 🤔 🤕 🤗 🤘 🤙 🤚 🤛 🤝 🤞 🤢 🤣 🤤 🤦 🤧 🤷 🥀 🦋'\n",
    "emojis_to_extract = emojis_to_extract.split(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "word_vectorizer_count = CountVectorizer(ngram_range=(1, 1),\n",
    "                                        strip_accents='unicode', \n",
    "                                        tokenizer=nltk.tokenize.TweetTokenizer().tokenize,\n",
    "                                        vocabulary=emojis_to_extract,\n",
    "                                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(134763, 362)\n",
      "CPU times: user 11.9 s, sys: 71.2 ms, total: 11.9 s\n",
      "Wall time: 15.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "text_vectors = word_vectorizer_count.fit_transform(tweets.text)\n",
    "print(text_vectors.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "features = np.array(word_vectorizer_count.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "لابد أن نتأكد أن الجدول الجديد مبني بشكل صحيح.. سنطبع تغريدة عشوائية أولاً، ثم نرى ماهي الرموز التعبيرية التي تساوي 1 لذات التغريدة. يجب أن يكون الاثنان متساويان.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet:\t\t  اذا لقيتي عطيني 🤗💔 \n",
      "Features:\t\t ['💔' '🤗']\n"
     ]
    }
   ],
   "source": [
    "idx = np.random.choice(tweets.shape[0], 1)[0]\n",
    "print('Tweet:\\t\\t', tweets.text[idx], \n",
    "      '\\nFeatures:\\t\\t', features[np.where(text_vectors.todense()[idx]>0)[1]]\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet:\t\t جد 😍😍😍💙💙 ياسلام 😍😍👏 خلص ولا لسا  \n",
      "Features:\t\t ['👏' '💙' '😍']\n"
     ]
    }
   ],
   "source": [
    "idx = np.random.choice(tweets.shape[0], 1)[0]\n",
    "print('Tweet:\\t\\t', tweets.text[idx], \n",
    "      '\\nFeatures:\\t\\t', features[np.where(text_vectors.todense()[idx]>0)[1]]\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet:\t\t  نتفتي الحلوه فيها كياته موطبيعي 😭❤❤🍥  \n",
      "Features:\t\t ['❤' '🍥' '😭']\n"
     ]
    }
   ],
   "source": [
    "idx = np.random.choice(tweets.shape[0], 1)[0]\n",
    "print('Tweet:\\t\\t', tweets.text[idx], \n",
    "      '\\nFeatures:\\t\\t', features[np.where(text_vectors.todense()[idx]>0)[1]]\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "يبدو أن الأمور على ما يرام (ماعدا بعض الأخطاء المتعلقة بترميز النصوص وليس بسبب أداءنا للتحليل).\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "emoj_df = pd.DataFrame(text_vectors.todense(), columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>☀</th>\n",
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       "   ☀  ☁  ☄  ★  ☎  ☔  ☕  ☘  ☝  ☹ ...  🤝  🤞  🤢  🤣  🤤  🤦  🤧  🤷  🥀  🦋\n",
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       "\n",
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     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emoj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets_emoj = pd.concat([tweets.city, emoj_df], axis=1, ignore_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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       "<p>5 rows × 363 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     city  ☀  ☁  ☄  ★  ☎  ☔  ☕  ☘  ☝ ...  🤝  🤞  🤢  🤣  🤤  🤦  🤧  🤷  🥀  🦋\n",
       "0  Riyadh  0  0  0  0  0  0  0  0  0 ...  0  0  0  0  0  0  0  0  0  0\n",
       "1  Riyadh  0  0  0  0  0  0  0  0  0 ...  0  0  0  0  0  0  0  0  0  0\n",
       "2  Riyadh  0  0  0  0  0  0  0  0  0 ...  0  0  0  0  0  0  0  0  0  0\n",
       "3  Riyadh  0  0  0  0  0  0  0  0  0 ...  0  0  0  0  0  0  0  0  0  0\n",
       "4  Riyadh  0  0  0  0  0  0  0  0  0 ...  0  0  0  0  0  0  0  0  0  0\n",
       "\n",
       "[5 rows x 363 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_emoj.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "بعد أن تم بناء الجدول بشكل سليم وإضافة خانة المدينة، سنقوم الآن بالخطوة النهائية وهي تجميع الصفوف لتصبح المدينة في خانة الصفوف والرموز التعبيرية في خانة الأعمدة. الأرقام الآن ستمثل مجموع المرات التي استخدم فيها رمز تعبيري ما في التغريدات القادمة من مدينة ما. طبعت رأس الجدول لنرى كيف يبدو ذلك.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets_emoj_city = tweets_emoj.groupby(['city']).agg(np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>☀</th>\n",
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       "      <td>0</td>\n",
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       "      <td>15</td>\n",
       "      <td>4</td>\n",
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       "<p>5 rows × 362 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ☀  ☁  ☄  ★  ☎  ☔   ☕  ☘  ☝    ☹ ...  🤝  🤞  🤢    🤣  🤤    🤦  🤧  🤷   🥀  \\\n",
       "city                                      ...                                   \n",
       "Abha      4  2  0  0  0  4  12  0  3  222 ...  2  0  7   23  5   97  1  0  18   \n",
       "Al-Kharj  0  0  0  0  0  0   2  0  1   47 ...  0  1  0    1  8   18  0  1   3   \n",
       "Buraidah  1  3  0  0  0  0  10  4  5  116 ...  0  0  1   23  3   24  2  1   4   \n",
       "Dammam    1  3  0  0  5  0  12  4  3  182 ...  2  3  3  101  8  167  1  4  14   \n",
       "Hail      2  5  1  1  0  1  16  1  1  149 ...  0  1  3   22  4   88  0  8  15   \n",
       "\n",
       "          🦋  \n",
       "city         \n",
       "Abha      3  \n",
       "Al-Kharj  0  \n",
       "Buraidah  0  \n",
       "Dammam    2  \n",
       "Hail      4  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_emoj_city.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "قبل أن نبدأ أي تحليل (نعم لم نحلل شيئاً بعد!)، من الواضح أن لدينا مشكلة في توزيع استخدامات الرموز التعبيرية بين المدن. فمدينة كالرياض يوجد بها عدد هائل من المغردين وعدد هائل من التغريدات كذلك ومن غير المنطقي مقارنتها مع مدينة نجران مثلاً بدون احتساب التفاوت في عدد التغريدات وعدد المغردين.\n",
    "\n",
    "<br/> <br/>\n",
    "\n",
    "لحل هذا الإشكال، سنقوم بتغيير الأرقام لتعكس النسب (داخل المدينة) وذلك بقسمة عدد مرات استخدام أي رمز تعبيري ( مثلاً الوجه الضاحك) داخل مدينة ما بعدد الوجود التعبيرية في تغريدات تلك المدينة. بعد هذه الخطوة نستطيع أن نقارن بين المدن لنقول مثلا أن نسبة استخدام الوجه الضاحك داخل الرياض أكثر من نسبة استخدام الوجه الضاحك داخل نجران بالرغم من اختلاف عدد التغريدات من كل مدينة.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets_emoj_city_size = tweets_emoj.groupby(['city']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets_emoj_city_ratio = np.divide(tweets_emoj_city, np.sqrt(tweets_emoj_city_size[:,None]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "أدناه شكل الجدول النهائي والذي يتكون الآن من نسب بدلاً من أرقام صحيحة.\n",
    "\n",
    "<br />\n",
    "\n",
    "للإجابة عن سؤالنا الأول، نستطيع مباشرة أن نحتسب \"متوسط\" استخدام الرموز التعبيرية داخل كل مدينة وثم نرتب النتائج تصاعدياً لنرى أي المدن لديها أكبر عدد من استخدام الرموز التعبيرية (نهاية القائمة) وأي المدن لديها أقل عدد من الرموز التعبيرية (بداية القائمة).\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "                 ☀         ☁         ☄         ★         ☎         ☔  \\\n",
       "city                                                                   \n",
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       "Al-Kharj  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
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       "\n",
       "                 ☕         ☘         ☝         ☹    ...            🤝  \\\n",
       "city                                                ...                \n",
       "Abha      0.124609  0.000000  0.031152  2.305257    ...     0.020768   \n",
       "Al-Kharj  0.040639  0.000000  0.020319  0.955016    ...     0.000000   \n",
       "Buraidah  0.125373  0.050149  0.062686  1.454324    ...     0.000000   \n",
       "Dammam    0.104629  0.034876  0.026157  1.586874    ...     0.017438   \n",
       "Hail      0.149873  0.009367  0.009367  1.395697    ...     0.000000   \n",
       "\n",
       "                 🤞         🤢         🤣         🤤         🤦         🤧  \\\n",
       "city                                                                   \n",
       "Abha      0.000000  0.072688  0.238833  0.051920  1.007252  0.010384   \n",
       "Al-Kharj  0.020319  0.000000  0.020319  0.162556  0.365751  0.000000   \n",
       "Buraidah  0.000000  0.012537  0.288357  0.037612  0.300895  0.025075   \n",
       "Dammam    0.026157  0.026157  0.880628  0.069753  1.456088  0.008719   \n",
       "Hail      0.009367  0.028101  0.206076  0.037468  0.824304  0.000000   \n",
       "\n",
       "                 🤷         🥀         🦋  \n",
       "city                                    \n",
       "Abha      0.000000  0.186913  0.031152  \n",
       "Al-Kharj  0.020319  0.060958  0.000000  \n",
       "Buraidah  0.012537  0.050149  0.000000  \n",
       "Dammam    0.034876  0.122067  0.017438  \n",
       "Hail      0.074937  0.140506  0.037468  \n",
       "\n",
       "[5 rows x 362 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_emoj_city_ratio.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Najran            0.177028\n",
       "Jazan             0.182721\n",
       "Khamis Mushait    0.251193\n",
       "Al-Kharj          0.312089\n",
       "Yanbu             0.333752\n",
       "Taif              0.360855\n",
       "Jubail            0.388580\n",
       "Hofuf             0.410994\n",
       "Tabuk             0.448958\n",
       "Buraidah          0.492625\n",
       "Khobar            0.649636\n",
       "Abha              0.668996\n",
       "Hail              0.695209\n",
       "Dammam            0.713978\n",
       "Riyadh            0.723166\n",
       "Medina            0.732782\n",
       "Jeddah            0.736642\n",
       "Mecca             0.748855\n",
       "Qatif             0.826966\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_emoj_city_ratio.mean(axis=1).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "كما نستطيع عمل ذات الشيء مع الرموز التعبيرية. وهو أخذ متوسط نسب استخدام كل رمز تعبيري في جميع المدن لنعرف ماهي الرموز التعبيرية الأكثر استخداماً\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "😂    29.799377\n",
       "️    20.908354\n",
       "❤    18.033336\n",
       "💔    13.382810\n",
       "😭    10.055071\n",
       "💙     9.735467\n",
       "😍     5.278099\n",
       "🏻     3.945968\n",
       "🌹     3.881957\n",
       "💛     3.713981\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_emoj_city_ratio.mean(axis=0).sort_values()[::-1][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "أو الرموز التعبيرية الأقل استخداماً...\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "🛑    0.000000\n",
       "🕞    0.000000\n",
       "🕑    0.000000\n",
       "🍏    0.000462\n",
       "🕛    0.001069\n",
       "🍒    0.002823\n",
       "🍇    0.003647\n",
       "🍌    0.004194\n",
       "🔑    0.004369\n",
       "🍑    0.004648\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_emoj_city_ratio.mean(axis=0).sort_values()[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "كما نستطيع تمثيل هذه البيانات في شكل يسهل فهمه فيما يعرف ب \"الخرائط الحرارية\". بكل بساطة، هي إعادة رسم لذات الجدول بنفس الشكل، ولكن الأرقام الآن يتم تحويلها إلى ألوان مأخوذة من طيف لوني يجعل من السهل رؤية القيم العالية والقيم المتدنية.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11aab2400>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SjrqCDpFfUauPtgzAgcfddtk4O46tBmWc1EE64uUURXV3brM52vybzUWT83v3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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bb3c0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "sns.heatmap(tweets_emoj_city_ratio, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "يبدو أن لدينا مشكلة شبيهة في الرموز التعبيرية فيما يخص اختلاف توزيع الاستخدام. فالوجه الضاحك مثلاً يحتل المركز الأول وبالتالي ستكون نسبته عالية في أغلب المدن (وهو بالمناسبة أحد الخطوط العمودية الداكنة في الشكل أعلاه). وهذا لن يجعلنا نعرف إن كانت مدينة ما تستخدمه بشكل أكثر من مدن أخرى (بعد أخذ كل العوامل الأخرى بالاعتبار). فما الحل مع الرموز التعبيرية ؟\n",
    "\n",
    "<br> <br>\n",
    "\n",
    "الحل بسيط جداً. فكر مثلاً كيف سنقارن بين رمز الوجه الضاحك ورمز الفتاة الراقصة. نحن نعرف أن \"متوسط\" استخدام الرمزين مختلف أساساً وبالتالي لكل رمز توزيع مختلف بين المدن. أبسط حل هو أن نقوم بتثبيت هذا المتوسط عند قيمة محددة لكل الرمزين لنتمكن بعدها من المقارنة العادلة. هذا بلغة الإحصاء يسمى \n",
    "<a href=\"https://en.wikipedia.org/wiki/Normalization_(statistics)\">Normalization</a> وأبسط أنواعه يسمى z-scoring ويعني طرح قيمة المتوسط من قيم التوزيع ليصبح المتوسط الجديد هو صفر، ثم قسمة قيم التوزيع على الانحراف المعياري ليصبح الانحراف المعياري موحداً بين الرموز كذلك.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_scaled = (tweets_emoj_city_ratio - tweets_emoj_city_ratio.mean(axis=0)) / tweets_emoj_city_ratio.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\"> \n",
    "وكما هو واضح من الشكل التالي، نستطيع أن نرى الأنماط المختلفة لكل رمز تعبيري بشكل أوضح. \n",
    "\n",
    "خذ بالاعتبار أن متوسط استخدام كل رمز تعبيري هو صفر. بالتالي، حينما تأخذ أي مدينة (لنأخذ أبها مثلاً)، ستجد أن القيم الموجبة (النقاط الحمراء) تمثل معدل استخدام أعلى من باقي الرموز التعبيرية، فيما القيم السالبة (النقاط الزرقاء) تمثل معدل استخدام أقل من باقي الرموز التعبيرية.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11be20e10>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B9JUHAMjPPkr0XSOx3T9T5nEvl0Vf+huBask7+YTM6YVlbi45VL4fbM2LzQk3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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aab2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "sns.heatmap(df_scaled, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\"> \n",
    "بعد هذه الخطوة ، نحن على أتم الجاهزية للإجابة عن أبرز الوجوه التعبيرية المستخدمة التي تميز مدينة ما عن المدن الأخرى وهذا ما سنراه لكل المدن.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Riyadh : 🍏 📞 🌲 🍻 😼 🎤 🙀 ❀ ♩ 🎁 📚 🍔 🍌 📲 🚬\n",
      "\n",
      "\n",
      "Tabuk : ★ 🤓 💟 🍑 🤣 💍 🌿 ⛅ ❌ 😑 📮 💦 😱 💢 🍳\n",
      "\n",
      "\n",
      "Jeddah : 🇺 🌟 🍀 🍼 💸 👅 💚 🍺 👶 ❋ 🌌 💢 🎉 😣 🙉\n",
      "\n",
      "\n",
      "Hail : 👪 🌙 🇮 ⚡ ❣ 🇹 🤙 🔻 ⚜ 🎬 🏹 📖 💤 🍇 🇲\n",
      "\n",
      "\n",
      "Mecca : 🕋 ❥ 🖐 ☝ 👻 ✿ 👼 ⚘ 🕺 😮 👭 💬 🙅 😻 😗\n",
      "\n",
      "\n",
      "Medina : 🏁 ⤵ 💪 🔵 🖒 🤕 🍃 💡 🇬 💨 🗓 😛 🌺 👌 💫\n",
      "\n",
      "\n",
      "Jubail : 🎧 👞 ✅ 🤛 👄 🇶 💝 👶 🌬 ❀ 🌤 🙃 👉 ⚕ 👭\n",
      "\n",
      "\n",
      "Hofuf : 👵 👰 🇧 🌥 🇭 🙍 🌴 ❃ ❁ 🍒 👑 🗝 📌 ☺ 💆\n",
      "\n",
      "\n",
      "Jazan : 😨 🌤 ✉ 🇳 🎹 ⛅ 🙅 🌱 🙇 🖖 💢 🙀 ⚘ 😹 🇮\n",
      "\n",
      "\n",
      "Al-Kharj : 🕛 😲 🤤 💝 ♪ 📝 😰 😹 ❁ ⬇ 😱 🍺 💍 💥 🗝\n",
      "\n",
      "\n",
      "Taif : 🎭 🔗 ❄ 🌨 💐 🔶 💥 ⭐ 🦋 ☔ 🌧 💀 🙁 👆 ⚽\n",
      "\n",
      "\n",
      "Qatif : 😭 🤷 🤝 🖕 🏿 😞 😂 😡 🙂 😰 🙇 ♀ 🙆 🤦 👎\n",
      "\n",
      "\n",
      "Dammam : 📱 🇰 🇼 🗣 🙌 👊 🍫 🎓 ⬇ 🍰 😀 🌱 💦 ✔ 🌼\n",
      "\n",
      "\n",
      "Abha : 🐙 🍆 🍭 🎻 📯 🇨 💖 🏾 🔞 🎵 🏆 ☔ 🚶 🍰 🌞\n",
      "\n",
      "\n",
      "Khamis Mushait : 🍥 🎀 💎 🔫 💞 💋 🌌 🙉 💥 😫 😘 😠 📲 👨 🎻\n",
      "\n",
      "\n",
      "Najran : 🇵 🇱 🎊 ✉ 😆 🇷 🎈 📮 😝 🎹 🏃 🎷 ❕ ♪ 🌴\n",
      "\n",
      "\n",
      "Buraidah : 🔝 🔁 ♠ ⚫ 📣 🔹 🌑 🇾 🌕 😙 😿 ❕ 🐥 🍁 🇪\n",
      "\n",
      "\n",
      "Yanbu : 🏳 😖 😾 💧 💩 🔑 🐒 🔪 ☻ 😈 🌈 🖒 🏹 😴 ⚽\n",
      "\n",
      "\n",
      "Khobar : 😯 ♨ 💉 ☀ ☎ 👧 🌝 🚗 😤 😥 🌈 👩 😦 🤧 🤢\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('')\n",
    "df_scaled = df_scaled.fillna(0)\n",
    "for city in tweets.city.unique():\n",
    "    print(city,':', ' '.join(df_scaled.loc[city].sort_values()[::-1].index[:15].tolist()))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "\n",
    "بناء على الشكل أعلاه سأذكر انطباعي بخصوص هذه الطريقة لحساب الرموز التعبيرية التي تميز كل مدينة عن أخرى. أعتقد أن هناك طرق أكثر متانة وثقة من هذه الطريقة البسيطة مثل \n",
    "<a href=\"https://en.wikipedia.org/wiki/Correspondence_analysis\">Correspondence Analysis</a> أو بناء جدول عدد الرموز أعلاه بطريقة مختلفة مثل استخدام خوارزمية \n",
    "<a href=\"https://en.wikipedia.org/wiki/Tf%E2%80%93idf\">TF-IDF</a> بدلاً من العدد.\n",
    "\n",
    "كما ترى، فهذه الطريقة أعلاه تثبت \"المتوسط\" بين المدن ليكون في الصفر ولكننا مازلنا نرى أن المدن الكبرى التي تمتلك نصيباً أكبر من التغريدات لديها نصيب الذهب من الرموز التعبيرية وذلك لوجود عدد كبير من القيم الموجبة لدى المدن الكبرى. هذا إما قد يعكس انحيازاً لعدد التغريدات، أو أن المغردين في المدن الكبرى يستخدمون الرموز التعبيرية بشكل أكبر من المغردين في باقي المدن. أميل شخصياً للتفسير اللاحق وذلك لأني أكاد أرى \"منطقة حمراء\" لكل مدينة أو مجموعة من الرموز التي تميزها عن غيرها على الرغم من قلة كمية التغريدات فيه (شاهد الشكل أعلاه). على أية حال، هذه الطريقة مازالت مفيدة لمعرفة أي الرموز هي الأكثر استخداماً من غير أن نكترث للفروقات بين الرمز الأول والثاني إن كان كبيراً أم ضئيلاً.\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; font-size:25px; text-align:right; font-weight: bold;\" >\n",
    "تحليل المشاعر (Sentiment Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "الآن سننتقل للسؤال التالي، وهو حول المحتوى العاطفي للرموز التعبيرية والذي يتراوح من رموز تعبيرية إيجابية (كالقلب أو الوجه الضاحك) إلى رموز تعبيرية سلبية (كالوجه الباكي أو الوجه الغاضب). قد تبدو هذه مهمة صعبة، إذا كيف نحدد \"قيمة عاطفية\" لكل وجه تعبيري؟ لحسن الحظ، هناك من سبق وقام بهذه المهمة. ففي \n",
    "<a href=\"http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0144296&type=printable\">ورقة علمية</a>\n",
    "منشورة في مجلة PLOS One، استخدم الباحثون حوالي مليون تغريدة من أكثر من 13 لغة أوروبية وأكثر من 82 محلل ليخرجوا ببحث ممتع كان هذا المعجم التعبيري - العاطفي أبرز نتائجه. لاحظ أنه قد يكون هناك اختلافات ثقافية طفيفة بين الرموز التعبيرية في التغريدات الأوروبية (سواء بالإنجليزية أو بغيرها) إلا أني أعتقد أن هذه المسائل طفيفة عملياً إذا ما كان الغرض هو المعرفة بشكل عام وليس تصنيف كل تغريدة على حدة مثلاً.\n",
    "\n",
    "<br><br>\n",
    "سبق وأن نسخت \n",
    "<a href=\"http://kt.ijs.si/data/Emoji_sentiment_ranking/index.html\">الجدول المرفق هنا</a>\n",
    "من الورقة ليصبح في جدول مستقل قابل للقراءة والاستخدام داخل بايثون.\n",
    "\n",
    "<br><br>\n",
    "\n",
    "في هذا الجدول، نجد أن لكل رمز تعبيري \"درجة عاطفية\" تتراوح من +1 إلى -1، أو من موجب إلى سالب (فيما الصفر يعتبر محايد). سنقوم بقراءة كل تغريدة واستخراج كل الرموز التعبيرية منها، ثم حساب متوسط \"الدرجة العاطفية\" للتغريدة بناء على الرموز المستخدمة (قد يجادلني البعض في هذا التوجه، ولكن هذا هو الطريق الأبسط على أية حال).\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "senti = pd.read_csv('./data/sentiment_scores.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Char</th>\n",
       "      <th>Image</th>\n",
       "      <th>Unicodecodepoint</th>\n",
       "      <th>Occurrences</th>\n",
       "      <th>Position</th>\n",
       "      <th>Neg</th>\n",
       "      <th>Neut</th>\n",
       "      <th>Pos</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>bar</th>\n",
       "      <th>name</th>\n",
       "      <th>block</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>😂</td>\n",
       "      <td>😂</td>\n",
       "      <td>0x1f602</td>\n",
       "      <td>14622.0</td>\n",
       "      <td>0.805</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.468</td>\n",
       "      <td>0.221</td>\n",
       "      <td></td>\n",
       "      <td>FACE WITH TEARS OF JOY</td>\n",
       "      <td>Emoticons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>❤</td>\n",
       "      <td>❤</td>\n",
       "      <td>0x2764</td>\n",
       "      <td>8050.0</td>\n",
       "      <td>0.747</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.166</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0.746</td>\n",
       "      <td></td>\n",
       "      <td>HEAVY BLACK HEART</td>\n",
       "      <td>Dingbats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>♥</td>\n",
       "      <td>♥</td>\n",
       "      <td>0x2665</td>\n",
       "      <td>7144.0</td>\n",
       "      <td>0.754</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.272</td>\n",
       "      <td>0.693</td>\n",
       "      <td>0.657</td>\n",
       "      <td></td>\n",
       "      <td>BLACK HEART SUIT</td>\n",
       "      <td>Miscellaneous Symbols</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>😍</td>\n",
       "      <td>😍</td>\n",
       "      <td>0x1f60d</td>\n",
       "      <td>6359.0</td>\n",
       "      <td>0.765</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.678</td>\n",
       "      <td></td>\n",
       "      <td>SMILING FACE WITH HEART-SHAPED EYES</td>\n",
       "      <td>Emoticons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>😭</td>\n",
       "      <td>😭</td>\n",
       "      <td>0x1f62d</td>\n",
       "      <td>5526.0</td>\n",
       "      <td>0.803</td>\n",
       "      <td>0.436</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.343</td>\n",
       "      <td>-0.093</td>\n",
       "      <td></td>\n",
       "      <td>LOUDLY CRYING FACE</td>\n",
       "      <td>Emoticons</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Char Image Unicodecodepoint  Occurrences  Position    Neg   Neut    Pos  \\\n",
       "0    😂     😂          0x1f602      14622.0     0.805  0.247  0.285  0.468   \n",
       "1    ❤     ❤           0x2764       8050.0     0.747  0.044  0.166  0.790   \n",
       "2    ♥     ♥           0x2665       7144.0     0.754  0.035  0.272  0.693   \n",
       "3    😍     😍          0x1f60d       6359.0     0.765  0.052  0.219  0.729   \n",
       "4    😭     😭          0x1f62d       5526.0     0.803  0.436  0.220  0.343   \n",
       "\n",
       "   Sentiment   bar                                 name                  block  \n",
       "0      0.221                     FACE WITH TEARS OF JOY              Emoticons  \n",
       "1      0.746                          HEAVY BLACK HEART               Dingbats  \n",
       "2      0.657                           BLACK HEART SUIT  Miscellaneous Symbols  \n",
       "3      0.678        SMILING FACE WITH HEART-SHAPED EYES              Emoticons  \n",
       "4     -0.093                         LOUDLY CRYING FACE              Emoticons  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "senti.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets['score'] = tweets.text.apply(lambda x: senti.loc[[emoji in x for emoji in senti['Char']]]['Sentiment'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "الآن، بعد أن صار لكل تغريدة \"درجة عاطفية\"، نستطيع أن نجري حساباً بسيطة لمعرفة ترتيب المدن في مدى إيجابية/سلبية الرموز التعبيرية المستخدمة فيها. كما نلاحظ في الشكل القادم، مدينة حائل تحتل الأولى في استخدام الرموز الإيجابية فيما مدينة القطيف تعتبر الأخيرة.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11be06be0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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bZd52mbd9w5bZvO0yb7s2V95BCrdVQP9Pm9NfgDVr4N4DPB54aWaORcSU50xk5crVUx3e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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bc636a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweets.groupby(['city']).agg({'score':np.mean}).sort_values(by='score').plot(kind='bar', figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "وأخيراً، سنقوم باستخدام \"الوقت\" لمعرفة اختلاف أنماط استخدام الرموز الإيجابية أو السلبية. الشكل التالي يتكون من عدة أشكال مصغرة، شكل لكل مدينة. على محور السينات لدينا ساعات اليوم (من الساعة 12 صباحا في أقصى اليسار إلى منتصف الليل  يمينا)، فيما على محور الصادات لدينا ترمومتر المشاعر في التغريدات المستخرجة من هذه المدينة.\n",
    "\n",
    "<br><br>\n",
    "قارن مثلاً جدة والرياض ( والتي تملك مزاجاً سيئاً في فترة الظهرية) وغيرها من المدن. أغلب المدن تزيد القيمة فيها عن 0.3 وهي ذات القيمة في (الشكل 4) من الورقة سابقة الذكر حينما حسبوا متوسط قيمة المشاعر لكل التغريدات. مما يعكس بشكل ما أن استخدام الرموز الإيجابية يغلب على استخدام الرموز السلبية.. فيما تبقى حائل مدينة \"الروقان\".\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets.created_at = pd.to_datetime(tweets.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l6L0/Rut+/g90rcGA36zZVO/4lvrUFHwmT3FSpEJ0rPjdt2k8fYqw+3+C39x5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\ndjwLvvuwSIbcciuWujpKVr/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ftB3HZGnhi4SNaDQafjr7HkID/Ttt8/DQO4gZEs47SWv4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ERSQRCuc7IsE/Eep9/zvcUORRAEQZjE2OoEkVQQBGE+2bRi+UMpkySJT2/+GAC/uvgSsixPa/uj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+YjxtJm/q8EiShO/ZZ/C/8DwKu5367/wtmsbGqTrdhBEEAcfn7gBBwPvMU1MuxOd/4XnCGzeg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Je+inIcH7yPPiOTxGuuO+c+lltvp/foUWzvvE106Xyi5syd5igDOrdvpenJP6A2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x14d8391d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivoted = tweets.pivot_table('score', \n",
    "                              index=tweets.created_at.dt.hour, \n",
    "                              columns=tweets.city, \n",
    "                              aggfunc=np.mean)\n",
    "pivoted.index.name = 'Hour'\n",
    "plo = pivoted.plot(subplots=True, layout=(-1, 4), sharex=False, sharey=True, figsize=(18, 20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"direction:rtl; font-size:25px; text-align:right; font-weight: bold;\" >\n",
    "الختام.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<p style=\"direction:rtl; text-align:right;\">\n",
    "\n",
    "لا أود الإطالة كما لا أود مناقشة النتائج بشكل مفصل. بل أحب دائماً أن أترك خيوط تفسير مفتوحة لمن أراد أن يتعمق. بالطبع لا أريد لأي أحد أن يتعمق في تفسير هذه النتائج نظراً للكمية المحدودة (والمنحازة إلى حد كبير) من التغريدات. هذا لا يجعلها غير مفيدة بل هي نقطة بداية لمن أحب التعمق.\n",
    "\n",
    "<br><br>\n",
    "كان الهدف من هذه التدوينة التحليلية هي إشباع فضولي العلمي والنقدي من خلال العمل على تحليل البيانات. ولأني أعرف أن الكثير يعتيرهم ذات الفضول ولديهم رغبة كبيرة لإجراء تحليلات مشابهة فقد قررت أولاً استخدام Jupyter Notebooks لكتابة التدوينة، ثم مشاركة الملفات الأولية لتصبح الفرصة متاحة لدى الجميع لإجراء أي تحليلات مشابهة (من دون الحاجة لإبلاغي أو الاستئذان مني في استخدام البيانات).\n",
    "\n",
    "<br><br>\n",
    "أتمنى أن تكون قد راقت لكم هذه التدوينة وأشبعت فضولكم كذلك. وربما دفعت بك إلى التفكير في طرق إبداعية لاستخدام البيانات العامة لفهم المجتمع الذي نعيش فيه بشكل أكبر وأدق. إن كانت لديك أي فكرة من هذا القبيل فأرجو التواصل معي لمشاركة المقترحات والأفكار.\n",
    "\n",
    "<br><br>\n",
    "\n",
    "إلى أن نراكم في المرة القادمة.. \n",
    "<a href=\"https://twitter.com/fahd09\">فهد</a>\n",
    "\n",
    "</p>"
   ]
  }
 ],
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