Riyadh Season.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "By Rawan AlNafesah"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "col": 5,
        "height": 3,
        "row": 0,
        "width": 2
       }
      }
     }
    }
   },
   "source": [
    "# موسم الرياض\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Description "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here ,an Arabic Sentimen anaylsis with Tweets in hashtag #موسم_الرياض  ,\n",
    "The dataset has been taken from Twitter  platform (I am Tweeter developer ).\n",
    "it has  been taken only 1000 tweets to figure out the Saudi people perceived satisfaction towards the Riyadh Season"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "col": 4,
        "height": 3,
        "row": 3,
        "width": 4
       }
      }
     }
    }
   },
   "source": [
    "# 1-Removing the Noise\n",
    "# 2-Classifcation based on lexicons \n",
    "# 3-Text Cleaning \n",
    "# 4-Normlization \n",
    "# 5-Toknization \n",
    "# 6-Stopwords\n",
    "# 5-Sentiment Classification \n",
    "# 6-Topic Modling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Loading  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
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       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv('C:/Users/rawan/Desktop/last/tweet_text9.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning and prepartion "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "clean_df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# Remove nosiy tweets\n",
    "noise=[\"كود خصم\",\"خصومات\",\"كوبون\",\"تخفيضات\",\"العقارات\",\"فريق متخص\",\"لملاك\",\"ملاك\",\"عقارات\",\"جلب الحبيب\",\"فك السحر\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
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     }
    }
   },
   "outputs": [],
   "source": [
    "def remove_noise(tweet):\n",
    "    label=\"ok\"\n",
    "    for word in noise:\n",
    "        if word in tweet:\n",
    "            label=\"noise\"\n",
    "    return label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# apply the method\n",
    "df[\"is Noise\"] = df['Tweets'].apply(lambda x: remove_noise(str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
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      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# remove the noise\n",
    "df=df[df[\"is Noise\"]!=\"noise\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification base on lexican "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "I mad here approxmitly 74000 words in negitive and  positive words |(Saudi accent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
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      }
     }
    }
   },
   "outputs": [],
   "source": [
    "pos_list=pd.read_csv('C:/Users/rawan/Desktop/last/pos.csv')\n",
    "neg_list=pd.read_csv('C:/Users/rawan/Desktop/last/neg.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "def pos_sentiment(tweet):\n",
    "    \n",
    "    count=0\n",
    "    # loop through the list of words and see if they are in the tweet text\n",
    "    for word in pos_list:\n",
    "        \n",
    "        if word in tweet:\n",
    "            count+=1\n",
    "    \n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "pos_list=pos_list[\"word\"].values.tolist()\n",
    "#apply the method to the data\n",
    "df[\"pos_count\"] = df[\"Tweets\"].apply(lambda x: pos_sentiment(str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "neg_list=neg_list[\"word\"].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
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      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def neg_sentiment(tweet):\n",
    "    \n",
    "    count=0\n",
    "     # loop through the list of words and see if they are in the tweet text\n",
    "    for word in neg_list:\n",
    "        if word in tweet:\n",
    "            count+=1\n",
    "               \n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "#apply the method to the data\n",
    "df[\"neg_count\"] = df['Tweets'].apply(lambda x: neg_sentiment(str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def tweet_sentiment(tweet):\n",
    "    range=3\n",
    "    pos_sent=pos_sentiment(tweet)\n",
    "    \n",
    "    neg_sent=neg_sentiment(tweet)\n",
    "    \n",
    "    if pos_sent > neg_sent:\n",
    "        label = \"Positive\"\n",
    "    \n",
    "    elif pos_sent < neg_sent :\n",
    "        label = \"Negative\"\n",
    "    else:\n",
    "        label = \"Neutral\" \n",
    "   \n",
    "   \n",
    "    return label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "df[\"sentiment\"] = df['Tweets'].apply(lambda x: tweet_sentiment(str(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# Q: How many tweets have positive sentiment and how many have negative sentiment\n",
    "tweets_by_sentiment = df['sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import csv\n",
    "import re # for regular expression\n",
    "import string\n",
    "import nltk \n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from collections import Counter \n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "# remove punctuations علامات الترقيم  \n",
    "\n",
    "arabic_punctuations = '''`÷×؛<>_()*&^%][ـ،/:\"؟.,'{}~¦+|!”…“–ـ'''\n",
    "english_punctuations = string.punctuation\n",
    "punctuations_list = arabic_punctuations + english_punctuations\n",
    "\n",
    "def remove_punctuations(text):\n",
    "    translator = str.maketrans('', '', punctuations_list)\n",
    "    return text.translate(translator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normlization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def normalize_arabic(text):\n",
    "    \n",
    "    text = re.sub(\"[إأآا]\", \"ا\", text)\n",
    "    text = re.sub(\"ى\", \"ي\", text)\n",
    "    text = re.sub(\"ة\", \"ه\", text)\n",
    "    text = re.sub(\"گ\", \"ك\", text)\n",
    "    text = re.sub(\"ؤ\", \"و\", text)\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "def remove_repeating_char(text):\n",
    "    return re.sub(r'(.)\\1+', r'\\1', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "#start processing the tweet \n",
    "def processPost(tweet,stemmer):\n",
    "\n",
    "    #Replace @username with empty string\n",
    "    tweet = re.sub('@[^\\s]+', ' ', tweet)\n",
    "    tweet = re.sub(r'_', ' ', tweet)\n",
    "    tweet = re.sub(r'\\n', ' ', tweet)\n",
    "    tweet = re.sub(r'[a-z,A-Z]', '', tweet)\n",
    "    tweet = re.sub(r'\\d', '', tweet)\n",
    "    #Convert www.* or https?://* to \" \"\n",
    "    tweet = re.sub('((www\\.[^\\s]+)|(https?://[^\\s]+))',' ',tweet)\n",
    "    \n",
    "    #Replace #word with word\n",
    "    tweet = re.sub(r'#([^\\s]+)', r'\\1', tweet)\n",
    "\n",
    "    # remove punctuations\n",
    "    tweet= remove_punctuations(tweet)\n",
    "    # remove repeated letters\n",
    "    tweet=remove_repeating_char(tweet)\n",
    "    # normalize the tweet\n",
    "    #check if the word in the dictionary list (it have two letters as part of the word)\n",
    "    tweet= normalize_arabic(tweet)\n",
    "    tweet = stemmer.stem(tweet)\n",
    "    #remove numbers\n",
    "    tweet = ''.join(i for i in tweet if not i.isdigit())\n",
    "    \n",
    "    #remove english letters\n",
    "    tweet= re.sub(r'[a-z]+',\" \", tweet)\n",
    "    tweet= re.sub(r'[A-Z]+',\" \", tweet)\n",
    "    \n",
    "    return tweet\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "from nltk.stem.isri import ISRIStemmer\n",
    "stemmer = ISRIStemmer()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "df[\"clean text\"] = df['Tweets'].apply(lambda x: processPost(x,stemmer))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing the Emojie "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/a/49146722/330558\n",
    "def remove_emoji(string):\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           u\"\\U00002702-\\U000027B0\"\n",
    "                           u\"\\U000024C2-\\U0001F251\"\n",
    "                           u\"\\U00002702-\\U000027B0\"\n",
    "                           u\"\\U000024C2-\\U0001F251\"\n",
    "                           u\"\\U0001f926-\\U0001f937\"\n",
    "                           u\"\\U00010000-\\U0010ffff\"\n",
    "                           u\"\\u2640-\\u2642\"\n",
    "                           u\"\\u2600-\\u2B55\"\n",
    "                           u\"\\u200d\"\n",
    "                           u\"\\u23cf\"\n",
    "                           u\"\\u23e9\"\n",
    "                           u\"\\u231a\"\n",
    "                           u\"\\ufe0f\"\n",
    "                           \"]+\", flags=re.UNICODE)\n",
    "    return emoji_pattern.sub(r'', string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "df[\"clean text\"] = df['clean text'].apply(lambda x: remove_emoji(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tokenization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "tokenizer = RegexpTokenizer(r'\\w+')\n",
    "df[\"tokens\"] = df[\"clean text\"].apply(tokenizer.tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# Q: How many tweets have positive sentiment and how many have negative sentiment\n",
    "tweets_by_sentiment = df['sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    805\n",
       "Negative    181\n",
       "Neutral       5\n",
       "Name: sentiment, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_by_sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "col": 0,
        "height": 12,
        "row": 7,
        "width": 12
       }
      }
     }
    }
   },
   "outputs": [
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       "      <th>User</th>\n",
       "      <th>Location</th>\n",
       "      <th>Tweets</th>\n",
       "      <th>is Noise</th>\n",
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       "      <th>849</th>\n",
       "      <td>hafsa03530132</td>\n",
       "      <td>المملكة العربية السعودية</td>\n",
       "      <td>RT @1Cash9999: ترقبونا الليلة في السحب الثانية...</td>\n",
       "      <td>ok</td>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
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       "      <td>Nada_nnn4</td>\n",
       "      <td>Gaza</td>\n",
       "      <td>RT @RTarabic: الفنانة اللبنانية إليسا تتصدر \"ا...</td>\n",
       "      <td>ok</td>\n",
       "      <td>63</td>\n",
       "      <td>42</td>\n",
       "      <td>Positive</td>\n",
       "      <td>الفنانه البنانيه اليسا تصدر الترند في موسم ال...</td>\n",
       "      <td>[الفنانه, البنانيه, اليسا, تصدر, الترند, في, م...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>ajoker766</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @1Cash9999: ترقبونا الليلة في السحب الثانية...</td>\n",
       "      <td>ok</td>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>Positive</td>\n",
       "      <td>ترقبونا اليله في السحب الثانيه  علما بالحساب ...</td>\n",
       "      <td>[ترقبونا, اليله, في, السحب, الثانيه, علما, بال...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Sa7khs</td>\n",
       "      <td>اللهم ارحم  فيصل بداح السهلي</td>\n",
       "      <td>RT @SaudiFalconClub: نتائج الشوط الخامس\\nمحترف...</td>\n",
       "      <td>ok</td>\n",
       "      <td>74</td>\n",
       "      <td>42</td>\n",
       "      <td>Positive</td>\n",
       "      <td>نتائج الشوط الخامس محترفين سعودين لفئه جير شا...</td>\n",
       "      <td>[نتائج, الشوط, الخامس, محترفين, سعودين, لفئه, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>Safiah88898i</td>\n",
       "      <td>الرياض, المملكة العربية السعودية</td>\n",
       "      <td>RT @JeweleryMansour: مين يستاهل الذهب🤩؟\\nمنشن ...</td>\n",
       "      <td>ok</td>\n",
       "      <td>90</td>\n",
       "      <td>11</td>\n",
       "      <td>Positive</td>\n",
       "      <td>مين يستاهل الذهب منشن شخص تهديه  الذهب البحري...</td>\n",
       "      <td>[مين, يستاهل, الذهب, منشن, شخص, تهديه, الذهب, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>leader_1420</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @1Cash9999: ترقبونا الليلة في السحب الثانية...</td>\n",
       "      <td>ok</td>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>Positive</td>\n",
       "      <td>ترقبونا اليله في السحب الثانيه  علما بالحساب ...</td>\n",
       "      <td>[ترقبونا, اليله, في, السحب, الثانيه, علما, بال...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>ammhmd65924787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @1Cash9999: ترقبونا الليلة في السحب الثانية...</td>\n",
       "      <td>ok</td>\n",
       "      <td>74</td>\n",
       "      <td>60</td>\n",
       "      <td>Positive</td>\n",
       "      <td>ترقبونا اليله في السحب الثانيه  علما بالحساب ...</td>\n",
       "      <td>[ترقبونا, اليله, في, السحب, الثانيه, علما, بال...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>kahledjeddah</td>\n",
       "      <td>جدة, المملكة العربية السعودية</td>\n",
       "      <td>جايكم جايكم 😍🔥🔥\\n\\n#موسم_الرياض   #ونترلاند ht...</td>\n",
       "      <td>ok</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
       "      <td>Positive</td>\n",
       "      <td>جايكم جايكم  موسم الرياض ونترلاند</td>\n",
       "      <td>[جايكم, جايكم, موسم, الرياض, ونترلاند]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>7amoodehkh</td>\n",
       "      <td>Hashemite Kingdom of Jordan</td>\n",
       "      <td>RT @RotanaLive: نجاح مبهر لحفل #اليسا و #الشاب...</td>\n",
       "      <td>ok</td>\n",
       "      <td>92</td>\n",
       "      <td>16</td>\n",
       "      <td>Positive</td>\n",
       "      <td>نجاح مبهر لحفل اليسا و الشاب خالد مع هيئه الت...</td>\n",
       "      <td>[نجاح, مبهر, لحفل, اليسا, و, الشاب, خالد, مع, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>Dody66625319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT @MBCinaWeek: لقاء مع أخطبوط العود \"عبادي ال...</td>\n",
       "      <td>ok</td>\n",
       "      <td>58</td>\n",
       "      <td>12</td>\n",
       "      <td>Positive</td>\n",
       "      <td>لقاء مع اخطبوط العود عبادي الجوهر خلال تواجده...</td>\n",
       "      <td>[لقاء, مع, اخطبوط, العود, عبادي, الجوهر, خلال,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               User                          Location  \\\n",
       "849   hafsa03530132          المملكة العربية السعودية   \n",
       "218       Nada_nnn4                              Gaza   \n",
       "770       ajoker766                               NaN   \n",
       "336          Sa7khs     اللهم ارحم  فيصل بداح السهلي    \n",
       "277    Safiah88898i  الرياض, المملكة العربية السعودية   \n",
       "333     leader_1420                               NaN   \n",
       "838  ammhmd65924787                               NaN   \n",
       "498    kahledjeddah     جدة, المملكة العربية السعودية   \n",
       "513      7amoodehkh       Hashemite Kingdom of Jordan   \n",
       "667    Dody66625319                               NaN   \n",
       "\n",
       "                                                Tweets is Noise  pos_count  \\\n",
       "849  RT @1Cash9999: ترقبونا الليلة في السحب الثانية...       ok         74   \n",
       "218  RT @RTarabic: الفنانة اللبنانية إليسا تتصدر \"ا...       ok         63   \n",
       "770  RT @1Cash9999: ترقبونا الليلة في السحب الثانية...       ok         74   \n",
       "336  RT @SaudiFalconClub: نتائج الشوط الخامس\\nمحترف...       ok         74   \n",
       "277  RT @JeweleryMansour: مين يستاهل الذهب🤩؟\\nمنشن ...       ok         90   \n",
       "333  RT @1Cash9999: ترقبونا الليلة في السحب الثانية...       ok         74   \n",
       "838  RT @1Cash9999: ترقبونا الليلة في السحب الثانية...       ok         74   \n",
       "498  جايكم جايكم 😍🔥🔥\\n\\n#موسم_الرياض   #ونترلاند ht...       ok         24   \n",
       "513  RT @RotanaLive: نجاح مبهر لحفل #اليسا و #الشاب...       ok         92   \n",
       "667  RT @MBCinaWeek: لقاء مع أخطبوط العود \"عبادي ال...       ok         58   \n",
       "\n",
       "     neg_count sentiment                                         clean text  \\\n",
       "849         60  Positive   ترقبونا اليله في السحب الثانيه  علما بالحساب ...   \n",
       "218         42  Positive   الفنانه البنانيه اليسا تصدر الترند في موسم ال...   \n",
       "770         60  Positive   ترقبونا اليله في السحب الثانيه  علما بالحساب ...   \n",
       "336         42  Positive   نتائج الشوط الخامس محترفين سعودين لفئه جير شا...   \n",
       "277         11  Positive   مين يستاهل الذهب منشن شخص تهديه  الذهب البحري...   \n",
       "333         60  Positive   ترقبونا اليله في السحب الثانيه  علما بالحساب ...   \n",
       "838         60  Positive   ترقبونا اليله في السحب الثانيه  علما بالحساب ...   \n",
       "498         16  Positive                 جايكم جايكم  موسم الرياض ونترلاند    \n",
       "513         16  Positive   نجاح مبهر لحفل اليسا و الشاب خالد مع هيئه الت...   \n",
       "667         12  Positive   لقاء مع اخطبوط العود عبادي الجوهر خلال تواجده...   \n",
       "\n",
       "                                                tokens  \n",
       "849  [ترقبونا, اليله, في, السحب, الثانيه, علما, بال...  \n",
       "218  [الفنانه, البنانيه, اليسا, تصدر, الترند, في, م...  \n",
       "770  [ترقبونا, اليله, في, السحب, الثانيه, علما, بال...  \n",
       "336  [نتائج, الشوط, الخامس, محترفين, سعودين, لفئه, ...  \n",
       "277  [مين, يستاهل, الذهب, منشن, شخص, تهديه, الذهب, ...  \n",
       "333  [ترقبونا, اليله, في, السحب, الثانيه, علما, بال...  \n",
       "838  [ترقبونا, اليله, في, السحب, الثانيه, علما, بال...  \n",
       "498             [جايكم, جايكم, موسم, الرياض, ونترلاند]  \n",
       "513  [نجاح, مبهر, لحفل, اليسا, و, الشاب, خالد, مع, ...  \n",
       "667  [لقاء, مع, اخطبوط, العود, عبادي, الجوهر, خلال,...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':' The Number of  Riyadh Season tweets Based on Sentiment Analysis '}, xlabel='Sentiment', ylabel='Number of tweets'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# visualize the results \n",
    "fig, ax = plt.subplots()\n",
    "color=['#C0E8D5','#B0BF1A','#00FFFF']\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "ax.tick_params(axis='y', labelsize=15)\n",
    "\n",
    "ax.set_xlabel('Sentiment', fontsize=2, color = 'black' )\n",
    "ax.set_ylabel('Number of tweets' , fontsize=20 , color = 'black')\n",
    "\n",
    "ax.set_title(' The Number of  Riyadh Season tweets Based on Sentiment Analysis ' ,fontsize=25,color = 'black' )\n",
    "\n",
    "tweets_by_sentiment.plot(ax=ax, kind='bar',color=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "labels=['Positive ','Neagtive ','Netural']\n",
    "color=['#C0E8D5','#B0BF1A','#00FFFF']\n",
    "patches, texts, pcts = ax.pie(\n",
    "    tweets_by_sentiment, labels=labels,autopct='%.1f%%',\n",
    "    wedgeprops={'linewidth': 5.0, 'edgecolor': 'white'},\n",
    "    textprops={'size': 'x-large'},\n",
    "    startangle=90, colors=color)\n",
    "# For each wedge, set the corresponding text label color to the wedge's\n",
    "# face color.\n",
    "for i, patch in enumerate(patches):\n",
    "  texts[i].set_color(patch.get_facecolor())\n",
    "plt.setp(pcts, color='Black')\n",
    "plt.setp(texts, fontweight='bold',color=\"Black\")\n",
    "ax.set_title('Precentage of Tweets', fontsize=25, color=\"Black\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Removing the Stop words "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Using 3 Stopword datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# three stop words dataset \n",
    "from nltk.tokenize import word_tokenize\n",
    "file1 = open('C:/Users/rawan/Downloads/stop_words_arabic(1).txt', 'r', encoding='utf-8')\n",
    "file2 = open('C:/Users/rawan/Downloads/arabic-stop-words-master/arabic-stop-words-master/list.txt', 'r', encoding='utf-8')\n",
    "stopwords_arabic = file1.read().splitlines()\n",
    "stopwords_arabic2 = file2.read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "#3 ed dataset of Stopword\n",
    "stopwords_arabic3 = stopwords.words('arabic')\n",
    "stopwords_arabic4 = ['عبر' , 'بكل','انا']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "stopwords_list=stopwords_arabic3+stopwords_arabic+stopwords_arabic2+stopwords_arabic4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "df[\"stop\"] = df[\"tokens\"].apply(lambda x: [item for item in x if item not in stopwords_list])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Most Frequent Words "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "all_words = [word for tokens in df[\"stop\"] for word in tokens]\n",
    "sentence_lengths = [len(tokens) for tokens in df[\"stop\"]]\n",
    "\n",
    "VOCAB = sorted(list(set(all_words)))\n",
    "# count the number of words\n",
    "word_counter = Counter(all_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'البوليفارد': 25,\n",
       "         'موسم': 512,\n",
       "         'الرياض': 567,\n",
       "         'فوازير': 44,\n",
       "         'طيران': 56,\n",
       "         'اديل': 57,\n",
       "         'مكان': 45,\n",
       "         'شيء': 33,\n",
       "         'نادر': 30,\n",
       "         'شبيه': 30,\n",
       "         'ماهو': 31,\n",
       "         'شروط': 24,\n",
       "         'المسابقه': 24,\n",
       "         'سيتم': 24,\n",
       "         'اختيار': 24,\n",
       "         'فائز': 24,\n",
       "         'شارك': 347,\n",
       "         'بال': 23,\n",
       "         'سفاري': 21,\n",
       "         'ترقبونا': 323,\n",
       "         'اليله': 338,\n",
       "         'السحب': 334,\n",
       "         'الثانيه': 325,\n",
       "         'علما': 324,\n",
       "         'بالحساب': 323,\n",
       "         'جوالين': 323,\n",
       "         'كاش': 323,\n",
       "         'هدايا': 324,\n",
       "         'مكن': 323,\n",
       "         'نصيبك': 323,\n",
       "         'رتويت': 338,\n",
       "         'تا': 323,\n",
       "         'اوفر': 4,\n",
       "         'شهاده': 4,\n",
       "         'ايلتس': 4,\n",
       "         'جهد': 4,\n",
       "         'وعناء': 4,\n",
       "         'اختبار': 5,\n",
       "         'متوفره': 5,\n",
       "         'بسعر': 9,\n",
       "         'ميز': 17,\n",
       "         'بالدرجه': 4,\n",
       "         'المطلوبه': 4,\n",
       "         'تصلك': 4,\n",
       "         'مكانك': 5,\n",
       "         'التكل': 4,\n",
       "         'مقياس': 2,\n",
       "         'الوزن': 3,\n",
       "         'الالكتروني': 1,\n",
       "         'بلوتوث': 1,\n",
       "         'التطبيق': 1,\n",
       "         'الذكي': 1,\n",
       "         'الياقه': 1,\n",
       "         'البدنيه': 1,\n",
       "         'شحن': 3,\n",
       "         'مجاني': 3,\n",
       "         'تخيضات': 3,\n",
       "         'قويه': 3,\n",
       "         'تقيم': 3,\n",
       "         'عالي': 8,\n",
       "         'جده': 22,\n",
       "         'انشر': 7,\n",
       "         'اعلانك': 8,\n",
       "         'بجميع': 7,\n",
       "         'مواقع': 20,\n",
       "         'التواصل': 23,\n",
       "         'الاجتماعي': 20,\n",
       "         'نشر': 7,\n",
       "         'الكتروني': 15,\n",
       "         'تويتر': 8,\n",
       "         'انستا': 7,\n",
       "         'فيسبوك': 7,\n",
       "         'واتساب': 8,\n",
       "         'تلغرام': 7,\n",
       "         'سناب': 7,\n",
       "         'عندهم': 34,\n",
       "         'ويحسدونا': 28,\n",
       "         'عالمطر': 28,\n",
       "         'جلسه': 7,\n",
       "         'طربيه': 4,\n",
       "         'يمتعنا': 4,\n",
       "         'الفنان': 30,\n",
       "         'محمد': 28,\n",
       "         'العالي': 7,\n",
       "         'باعذب': 4,\n",
       "         'المواويل': 4,\n",
       "         'والاغاني': 5,\n",
       "         'بمطعم': 9,\n",
       "         'دخنه': 7,\n",
       "         'بوليفارد': 74,\n",
       "         'رياض': 70,\n",
       "         'سيتي': 71,\n",
       "         'احجز': 28,\n",
       "         'الحي': 3,\n",
       "         'ياحظ': 1,\n",
       "         'جازان': 1,\n",
       "         'اليسا': 175,\n",
       "         'تحصد': 2,\n",
       "         'نجاح': 24,\n",
       "         'حفلها': 5,\n",
       "         'الباهر': 1,\n",
       "         'تزال': 1,\n",
       "         'الالومبيا': 1,\n",
       "         'وه': 1,\n",
       "         'اله': 16,\n",
       "         'ازاله': 4,\n",
       "         'الشعر': 4,\n",
       "         'باليزر': 2,\n",
       "         'لنزع': 2,\n",
       "         'جورج': 2,\n",
       "         'قرادحي': 1,\n",
       "         'جروفز': 7,\n",
       "         'الصحراء': 6,\n",
       "         'لطبيعه': 6,\n",
       "         'السعوديه': 49,\n",
       "         'مناطق': 9,\n",
       "         'داخل': 12,\n",
       "         'كريستال': 9,\n",
       "         'تعيشكم': 9,\n",
       "         'تجارب': 9,\n",
       "         'ترفيهيه': 9,\n",
       "         'فريده': 9,\n",
       "         'لعبه': 5,\n",
       "         'متاهه': 5,\n",
       "         'الكريستال': 5,\n",
       "         'بنعيشها': 5,\n",
       "         'نذكركم': 1,\n",
       "         'بمسارات': 1,\n",
       "         'الدخول': 8,\n",
       "         'والخروج': 1,\n",
       "         'عشان': 6,\n",
       "         'توصلون': 1,\n",
       "         'باسرع': 1,\n",
       "         'وقت': 2,\n",
       "         'قناه': 2,\n",
       "         'تستعرض': 2,\n",
       "         'حفل': 16,\n",
       "         'النجمه': 1,\n",
       "         'سهره': 1,\n",
       "         'كلها': 4,\n",
       "         'احساس': 1,\n",
       "         'متل': 1,\n",
       "         'العاده': 1,\n",
       "         'الجمهور': 1,\n",
       "         'يطلب': 1,\n",
       "         'تعيد': 1,\n",
       "         'اغانيها': 1,\n",
       "         'اكت': 1,\n",
       "         'تذاكر': 18,\n",
       "         'لقريه': 1,\n",
       "         'زمان': 19,\n",
       "         'لبيع': 3,\n",
       "         'خاص': 17,\n",
       "         'قريه': 23,\n",
       "         'تذكره': 6,\n",
       "         'زم': 4,\n",
       "         'برفقه': 1,\n",
       "         'الشاب': 29,\n",
       "         'خالد': 41,\n",
       "         'انتهاء': 1,\n",
       "         'صاحبه': 3,\n",
       "         'الراي': 3,\n",
       "         'حماس': 7,\n",
       "         'تكفون': 3,\n",
       "         'لاتخلون': 2,\n",
       "         'ذعار': 2,\n",
       "         'خلو': 2,\n",
       "         'امه': 2,\n",
       "         'تفرح': 2,\n",
       "         'بشوفته': 2,\n",
       "         'ويرجع': 2,\n",
       "         'لزوجته': 2,\n",
       "         'عياله': 2,\n",
       "         'واخوانه': 2,\n",
       "         'مليجه': 2,\n",
       "         'تغطيه': 10,\n",
       "         'المكان': 22,\n",
       "         'جميل': 14,\n",
       "         'صدق': 8,\n",
       "         'الخيال': 12,\n",
       "         'يحتاج': 9,\n",
       "         'صراحه': 9,\n",
       "         'بانزل': 7,\n",
       "         'حقت': 4,\n",
       "         'احلام': 12,\n",
       "         'حياتي': 2,\n",
       "         'بشوفتك': 2,\n",
       "         'كنا': 2,\n",
       "         'نقول': 3,\n",
       "         'طلعت': 2,\n",
       "         'حتي': 8,\n",
       "         'روح': 2,\n",
       "         'حنا': 4,\n",
       "         'مركز': 2,\n",
       "         'جابر': 2,\n",
       "         'مو': 7,\n",
       "         'عارفين': 2,\n",
       "         'نديره': 1,\n",
       "         'حلم': 2,\n",
       "         'وتحق': 2,\n",
       "         'ملكه': 21,\n",
       "         'الاحساس': 12,\n",
       "         'تصدر': 24,\n",
       "         'الترند': 31,\n",
       "         'العربي': 13,\n",
       "         'تلفزيون': 12,\n",
       "         'فن': 12,\n",
       "         'انساك': 1,\n",
       "         'وعيش': 2,\n",
       "         'بحريه': 1,\n",
       "         'فلاش': 2,\n",
       "         'باك': 2,\n",
       "         'شاهد': 3,\n",
       "         'بطل': 2,\n",
       "         'كاس': 10,\n",
       "         'وزاره': 2,\n",
       "         'الدفاع': 2,\n",
       "         'الفئه': 3,\n",
       "         'الثالثه': 2,\n",
       "         'الجواد': 2,\n",
       "         'اوكو': 2,\n",
       "         'المالك': 5,\n",
       "         'ابناء': 5,\n",
       "         'هيف': 2,\n",
       "         'عبود': 2,\n",
       "         'القحطا': 1,\n",
       "         'هديتكريال': 8,\n",
       "         'رتوت': 8,\n",
       "         'وتابع': 11,\n",
       "         'موثقمن': 8,\n",
       "         'المثبته': 8,\n",
       "         'اجمل': 9,\n",
       "         'بيت': 14,\n",
       "         'شعر': 8,\n",
       "         'تحفظه': 8,\n",
       "         'العتيبيه': 8,\n",
       "         'واطفالها': 8,\n",
       "         'الثمانيه': 8,\n",
       "         'خدمات': 13,\n",
       "         'لكل': 18,\n",
       "         'متجر': 26,\n",
       "         'رسمي': 13,\n",
       "         'لدعم': 13,\n",
       "         'حساباتكم': 13,\n",
       "         'الشخصيه': 13,\n",
       "         'والتجاريه': 13,\n",
       "         'لايكات': 13,\n",
       "         'متابعين': 13,\n",
       "         'مكتوبه': 4,\n",
       "         'ليك': 4,\n",
       "         'مفضلتي': 1,\n",
       "         'هالاغنيه': 1,\n",
       "         'متوفر': 7,\n",
       "         'عائله': 8,\n",
       "         'تس': 8,\n",
       "         'الخميس': 5,\n",
       "         'والجمعه': 1,\n",
       "         'العد': 2,\n",
       "         'محدود': 1,\n",
       "         'جدابوليفارد': 1,\n",
       "         'البعض': 3,\n",
       "         'يرفض': 1,\n",
       "         'يصدق': 2,\n",
       "         'حاله': 1,\n",
       "         'حرب': 1,\n",
       "         'عدو': 1,\n",
       "         'غاشم': 1,\n",
       "         'حاقد': 1,\n",
       "         'لايرحم': 1,\n",
       "         'طفل': 1,\n",
       "         'ولاشيخ': 1,\n",
       "         'ولاامراءه': 1,\n",
       "         'يعلن': 1,\n",
       "         'عدائه': 1,\n",
       "         'بو': 1,\n",
       "         'يبيهم': 3,\n",
       "         'ضمي': 1,\n",
       "         'قلبي': 1,\n",
       "         'واجحاف': 1,\n",
       "         'السنين': 1,\n",
       "         'مناهل': 1,\n",
       "         'وجهك': 1,\n",
       "         'السمح': 1,\n",
       "         'عيني': 1,\n",
       "         'ترتوي': 1,\n",
       "         'سلمان': 1,\n",
       "         'عبدالعزيز': 1,\n",
       "         'العراق': 1,\n",
       "         'غيم': 1,\n",
       "         'مطر': 7,\n",
       "         'المقطع': 4,\n",
       "         'مليان': 5,\n",
       "         'بهجه': 5,\n",
       "         'وفرح': 5,\n",
       "         'عروسه': 5,\n",
       "         'دائما': 6,\n",
       "         'بجمالها': 5,\n",
       "         'شكرا': 8,\n",
       "         'التغير': 5,\n",
       "         'يسعدنا': 5,\n",
       "         'ويبهجنا': 5,\n",
       "         'بلدنا': 5,\n",
       "         'الر': 4,\n",
       "         'سواء': 2,\n",
       "         'منشات': 1,\n",
       "         'حكوميه': 2,\n",
       "         'خاصه': 15,\n",
       "         'مراكز': 1,\n",
       "         'تجاريه': 3,\n",
       "         'مطاعم': 2,\n",
       "         'فنادق': 1,\n",
       "         'فعاليات': 22,\n",
       "         'جميعها': 1,\n",
       "         'تحتاج': 20,\n",
       "         'حمايه': 3,\n",
       "         'متلكاتها': 1,\n",
       "         'الاخطار': 1,\n",
       "         'متنوعه': 2,\n",
       "         'امسيات': 3,\n",
       "         'وحفلات': 1,\n",
       "         'فعاليه': 13,\n",
       "         'وطرب': 1,\n",
       "         'جد': 1,\n",
       "         'الموسم': 12,\n",
       "         'رائع': 2,\n",
       "         'مراح': 1,\n",
       "         'احبها': 1,\n",
       "         'فريق': 2,\n",
       "         'بحر': 1,\n",
       "         'الاعلانات': 1,\n",
       "         'مكون': 1,\n",
       "         'معلنه': 1,\n",
       "         'لنشر': 1,\n",
       "         'لجميع': 2,\n",
       "         'البرامج': 1,\n",
       "         'وعام': 1,\n",
       "         'لاكثر': 1,\n",
       "         'حساب': 1,\n",
       "         'نس': 1,\n",
       "         'بديت': 1,\n",
       "         'اصدق': 1,\n",
       "         'كلام': 5,\n",
       "         'الوالد': 1,\n",
       "         'لمن': 1,\n",
       "         'يقول': 2,\n",
       "         'مافي': 1,\n",
       "         'امطار': 3,\n",
       "         'لفتره': 3,\n",
       "         'محدوده': 1,\n",
       "         'تذكرتك': 7,\n",
       "         'ذهاب': 1,\n",
       "         'وعوده': 1,\n",
       "         'الكويت': 1,\n",
       "         'الخطوط': 2,\n",
       "         'وخلك': 1,\n",
       "         'سلام': 3,\n",
       "         'عليكم': 1,\n",
       "         'عندي': 4,\n",
       "         'تذكرتين': 3,\n",
       "         'حاب': 1,\n",
       "         'ياخذ': 1,\n",
       "         'اقل': 1,\n",
       "         'يتواصل': 1,\n",
       "         'معي': 4,\n",
       "         'لبوليفارد': 1,\n",
       "         'الاحد': 5,\n",
       "         'محتاجين': 2,\n",
       "         'عد': 3,\n",
       "         'منظمين': 2,\n",
       "         'لمعرض': 2,\n",
       "         'فني': 2,\n",
       "         'الشروط': 2,\n",
       "         'لغه': 2,\n",
       "         'متازه': 2,\n",
       "         'لباقه': 2,\n",
       "         'حسن': 3,\n",
       "         'المظهر': 4,\n",
       "         'احترافيه': 2,\n",
       "         'التعامل': 2,\n",
       "         'الزوار': 3,\n",
       "         'نورتي': 1,\n",
       "         'يام': 2,\n",
       "         'نايف': 3,\n",
       "         'مين': 22,\n",
       "         'يستاهل': 20,\n",
       "         'الذهب': 43,\n",
       "         'منشن': 20,\n",
       "         'شخص': 29,\n",
       "         'تهديه': 20,\n",
       "         'البحريني': 21,\n",
       "         'منصور': 22,\n",
       "         'لمجوهرات': 22,\n",
       "         'طقم': 49,\n",
       "         'بحريني': 21,\n",
       "         'الاجواء': 1,\n",
       "         'الحلوه': 1,\n",
       "         'دخول': 1,\n",
       "         'فصل': 1,\n",
       "         'الشتاءوحلول': 1,\n",
       "         'الاجواءالجميلهينبعث': 1,\n",
       "         'النفس': 1,\n",
       "         'شيءمن': 1,\n",
       "         'الذهوالفرح': 1,\n",
       "         'والسرورتري': 1,\n",
       "         'مسرحيه': 8,\n",
       "         'سوبرماركت': 6,\n",
       "         'بقصتها': 2,\n",
       "         'العجيبه': 2,\n",
       "         'زادت': 3,\n",
       "         'الكل': 2,\n",
       "         'وبنقدم': 2,\n",
       "         'عرض': 16,\n",
       "         'اضافي': 6,\n",
       "         'تلحق': 2,\n",
       "         'عشقنا': 11,\n",
       "         'اليل': 11,\n",
       "         'بالحانه': 11,\n",
       "         'طلال': 19,\n",
       "         'سلامه': 19,\n",
       "         'يسمعنا': 13,\n",
       "         'انغامه': 12,\n",
       "         'لاونج': 19,\n",
       "         'ملك': 19,\n",
       "         'العود': 24,\n",
       "         'طاولتك': 18,\n",
       "         'راي': 1,\n",
       "         'الاكثريه': 2,\n",
       "         'مقدما': 1,\n",
       "         'لكان': 1,\n",
       "         'قوم': 1,\n",
       "         'لوط': 2,\n",
       "         'اولي': 3,\n",
       "         'فرعون': 1,\n",
       "         'موسي': 1,\n",
       "         'وابو': 1,\n",
       "         'جهل': 1,\n",
       "         'معتبره': 1,\n",
       "         'مبهر': 20,\n",
       "         'لحفل': 20,\n",
       "         'هيئه': 21,\n",
       "         'الترفيه': 24,\n",
       "         'وروتانا': 20,\n",
       "         'عرفنا': 8,\n",
       "         'انهم': 8,\n",
       "         'يصيحون': 4,\n",
       "         'يولولون': 4,\n",
       "         'بالامس': 11,\n",
       "         'المكن': 4,\n",
       "         'يكذبون': 4,\n",
       "         'الاخوان': 4,\n",
       "         'المفسدين': 4,\n",
       "         'الوسواس': 4,\n",
       "         'الخ': 4,\n",
       "         'تمت': 1,\n",
       "         'الموافقه': 1,\n",
       "         'الايلتس': 1,\n",
       "         'الغه': 1,\n",
       "         'الانجليزيه': 1,\n",
       "         'الاكثر': 1,\n",
       "         'شعبيه': 2,\n",
       "         'العالم': 6,\n",
       "         'لتعليم': 1,\n",
       "         'والهجره': 1,\n",
       "         'العالميه': 1,\n",
       "         'الزمن': 1,\n",
       "         'يرجع': 2,\n",
       "         'سوال': 1,\n",
       "         'ياكابتن': 1,\n",
       "         'الاعب': 2,\n",
       "         'عمر': 2,\n",
       "         'تراوري': 1,\n",
       "         'لعب': 1,\n",
       "         'نادي': 8,\n",
       "         'ودك': 2,\n",
       "         'يجيبون': 1,\n",
       "         'لاعيبه': 1,\n",
       "         'النصر': 1,\n",
       "         'احدي': 1,\n",
       "         'منطقه': 5,\n",
       "         'خياليه': 9,\n",
       "         'ومتنوعه': 2,\n",
       "         'غني': 1,\n",
       "         'ديو': 1,\n",
       "         'نصاب': 1,\n",
       "         'البرنس': 1,\n",
       "         'ماجد': 1,\n",
       "         'المهندس': 3,\n",
       "         'نجم': 1,\n",
       "         'نجوم': 5,\n",
       "         'الوطن': 2,\n",
       "         'تحميل': 1,\n",
       "         'تطبيق': 2,\n",
       "         'شدو': 1,\n",
       "         'والابداع': 1,\n",
       "         'بموه': 1,\n",
       "         'الجواب': 7,\n",
       "         'تركب': 19,\n",
       "         'برجولات': 19,\n",
       "         'حديد': 19,\n",
       "         'خشبي': 19,\n",
       "         'تقدم': 19,\n",
       "         'شركه': 20,\n",
       "         'ينابيع': 19,\n",
       "         'اعمار': 19,\n",
       "         'فخر': 19,\n",
       "         'صناعه': 19,\n",
       "         'وتركيب': 19,\n",
       "         'البرجولات': 19,\n",
       "         'بكافه': 19,\n",
       "         'انواعها': 19,\n",
       "         'واشكال': 4,\n",
       "         'الكبير': 2,\n",
       "         'يطربنا': 8,\n",
       "         'بكره': 11,\n",
       "         'يفوتكم': 2,\n",
       "         'واحجزوا': 2,\n",
       "         'الحين': 13,\n",
       "         'انهارده': 1,\n",
       "         'اخر': 1,\n",
       "         'المسرحيه': 2,\n",
       "         'المبي': 3,\n",
       "         'الجاهليه': 3,\n",
       "         'قدا': 1,\n",
       "         'مانا': 2,\n",
       "         'مبسوطهاني': 1,\n",
       "         'معاكم': 1,\n",
       "         'بالرياض': 3,\n",
       "         'زعلانه': 2,\n",
       "         'اني': 4,\n",
       "         'هسبكم': 1,\n",
       "         'لح': 1,\n",
       "         'دريك': 1,\n",
       "         'حبيبتي': 1,\n",
       "         'تغضبين': 1,\n",
       "         'فانتي': 1,\n",
       "         'امامي': 1,\n",
       "         'كالسيل': 1,\n",
       "         'الهادر': 1,\n",
       "         'فاما': 1,\n",
       "         'اواجهك': 1,\n",
       "         'واهلك': 1,\n",
       "         'واما': 1,\n",
       "         'اسايرك': 1,\n",
       "         'اصل': 1,\n",
       "         'فرع': 1,\n",
       "         'الامان': 1,\n",
       "         'وانجو': 1,\n",
       "         'زياره': 1,\n",
       "         'وتغطيه': 1,\n",
       "         'اتشرف': 1,\n",
       "         'بمتابعتكم': 1,\n",
       "         'موضوع': 3,\n",
       "         'مدح': 1,\n",
       "         'لازم': 1,\n",
       "         'نتبه': 1,\n",
       "         'لنقطه': 1,\n",
       "         'هامه': 1,\n",
       "         'دولتنا': 1,\n",
       "         'مب': 1,\n",
       "         'قاصرها': 1,\n",
       "         'شي': 3,\n",
       "         'ومقبله': 1,\n",
       "         'احداث': 1,\n",
       "         'عالميه': 3,\n",
       "         'مهمه': 1,\n",
       "         'واولها': 1,\n",
       "         'سعوديه': 3,\n",
       "         'وش': 4,\n",
       "         'بتشوف': 1,\n",
       "         'خريطه': 1,\n",
       "         'تعرفك': 1,\n",
       "         'مناطقها': 1,\n",
       "         'وتفاصيلها': 1,\n",
       "         'موس': 1,\n",
       "         'اجواء': 13,\n",
       "         'مجانا': 5,\n",
       "         'يوجد': 8,\n",
       "         'لحجز': 6,\n",
       "         'قاعد': 1,\n",
       "         'ينقال': 1,\n",
       "         'مجلس': 1,\n",
       "         'التالي': 1,\n",
       "         'طهروا': 1,\n",
       "         'الذنوب': 1,\n",
       "         'بغليل': 1,\n",
       "         'وجاهم': 1,\n",
       "         'نطهر': 1,\n",
       "         'ذنوب': 1,\n",
       "         'بيجينا': 1,\n",
       "         'حفلات': 11,\n",
       "         'وعلي': 9,\n",
       "         'مسرح': 16,\n",
       "         'عبده': 7,\n",
       "         'ارينا': 7,\n",
       "         'اقيم': 7,\n",
       "         'ضخم': 7,\n",
       "         'لفنانه': 7,\n",
       "         'والفنان': 7,\n",
       "         'وسط': 7,\n",
       "         'يبي': 1,\n",
       "         'حق': 2,\n",
       "         'بنفس': 6,\n",
       "         'سعرها': 2,\n",
       "         'تاجلت': 1,\n",
       "         'روحتي': 1,\n",
       "         'لبكره': 1,\n",
       "         'بيكون': 11,\n",
       "         'معزوم': 11,\n",
       "         'المعازيم': 11,\n",
       "         'الصوره': 11,\n",
       "         'بنمر': 1,\n",
       "         'عشنا': 2,\n",
       "         'كومبات': 7,\n",
       "         'فيلد': 7,\n",
       "         'رهيبه': 2,\n",
       "         'مليانه': 2,\n",
       "         'وناسه': 2,\n",
       "         'وجربنا': 2,\n",
       "         'تحديات': 2,\n",
       "         'وحضرنا': 2,\n",
       "         'بطولات': 2,\n",
       "         'جديده': 2,\n",
       "         'صارت': 2,\n",
       "         'عيش': 2,\n",
       "         'الح': 3,\n",
       "         'راس': 1,\n",
       "         'مالي': 1,\n",
       "         'سعودي': 1,\n",
       "         'واني': 1,\n",
       "         'كنت': 1,\n",
       "         'جزء': 1,\n",
       "         'يطول': 1,\n",
       "         'مولاي': 1,\n",
       "         'خادم': 2,\n",
       "         'الحرمين': 3,\n",
       "         'الشريفين': 2,\n",
       "         'وسمو': 1,\n",
       "         'سيد': 1,\n",
       "         'تفوت': 5,\n",
       "         'الحماس': 4,\n",
       "         'واحجز': 8,\n",
       "         'سمعوهم': 1,\n",
       "         'شكلهم': 1,\n",
       "         'فقدوا': 1,\n",
       "         'حاسه': 1,\n",
       "         'الذوق': 1,\n",
       "         'وحتي': 1,\n",
       "         'السمع': 1,\n",
       "         'وشوي': 1,\n",
       "         'يفقدوا': 1,\n",
       "         'العقل': 1,\n",
       "         'حضر': 1,\n",
       "         'ماشاء': 1,\n",
       "         'تبارك': 1,\n",
       "         'تغريده': 9,\n",
       "         'ومازالت': 9,\n",
       "         'طرب': 4,\n",
       "         'العندليب': 6,\n",
       "         'بعيد': 1,\n",
       "         'مغرم': 1,\n",
       "         'فنه': 1,\n",
       "         'ترا': 4,\n",
       "         'الموعد': 1,\n",
       "         'قريب': 1,\n",
       "         'لاول': 4,\n",
       "         'مره': 8,\n",
       "         'مسرحي': 1,\n",
       "         'تامر': 2,\n",
       "         'حسني': 2,\n",
       "         'القطري': 1,\n",
       "         'جاء': 1,\n",
       "         'ويمدح': 1,\n",
       "         'هشتقوه': 1,\n",
       "         'قطر': 1,\n",
       "         'وخونوه': 1,\n",
       "         'رايه': 1,\n",
       "         'بالموسم': 2,\n",
       "         'واكيد': 1,\n",
       "         'ينتظرونه': 1,\n",
       "         'مدري': 1,\n",
       "         'اتعاطف': 1,\n",
       "         'بدات': 1,\n",
       "         'الحرب': 1,\n",
       "         'مواطن': 1,\n",
       "         'قطري': 1,\n",
       "         'غلبان': 1,\n",
       "         'امتدح': 1,\n",
       "         'بام': 2,\n",
       "         'عينه': 1,\n",
       "         'يبدو': 1,\n",
       "         'يمدح': 1,\n",
       "         'فع': 1,\n",
       "         'دحين': 1,\n",
       "         'بكبره': 1,\n",
       "         'حاسدينا': 1,\n",
       "         'ليوم': 3,\n",
       "         'الرائع': 1,\n",
       "         'الفنانه': 21,\n",
       "         'البنانيه': 20,\n",
       "         'مواشرات': 1,\n",
       "         'البحث': 1,\n",
       "         'بحث': 1,\n",
       "         'ترند': 3,\n",
       "         'المل': 1,\n",
       "         'يحجز': 1,\n",
       "         'تخبط': 2,\n",
       "         'الحياه': 2,\n",
       "         'فتثبت': 2,\n",
       "         'صلابه': 2,\n",
       "         'تدرس': 2,\n",
       "         'ثمه': 2,\n",
       "         'نوع': 3,\n",
       "         'الالم': 2,\n",
       "         'يفرخ': 2,\n",
       "         'براعم': 2,\n",
       "         'خضراء': 2,\n",
       "         'رائحه': 2,\n",
       "         'النزيف': 2,\n",
       "         'يترافق': 2,\n",
       "         'تربه': 2,\n",
       "         'باكي': 2,\n",
       "         'يارب': 3,\n",
       "         'ثبتنا': 1,\n",
       "         'دينك': 1,\n",
       "         'وهداك': 1,\n",
       "         'وادحر': 1,\n",
       "         'عنا': 1,\n",
       "         'الفسق': 1,\n",
       "         'والعصيان': 1,\n",
       "         'والفجور': 1,\n",
       "         'والمنكر': 1,\n",
       "         'والفواحش': 1,\n",
       "         'ماظه': 1,\n",
       "         'شرطه': 2,\n",
       "         'الموضه': 2,\n",
       "         'بفستان': 2,\n",
       "         'تصميم': 6,\n",
       "         'ايلي': 2,\n",
       "         'صعب': 2,\n",
       "         'اجرت': 2,\n",
       "         'التعديلات': 2,\n",
       "         'لتفاصيل': 2,\n",
       "         'مشهد': 1,\n",
       "         'الكوميديا': 3,\n",
       "         'بانتظاركم': 3,\n",
       "         'العلي': 4,\n",
       "         'بقي': 1,\n",
       "         'زاره': 1,\n",
       "         'واله': 2,\n",
       "         'يتونس': 1,\n",
       "         'التركي': 1,\n",
       "         'تعليقات': 1,\n",
       "         'الاخونج': 1,\n",
       "         'تحرش': 2,\n",
       "         'الغوا': 2,\n",
       "         'كله': 4,\n",
       "         'الحكومه': 1,\n",
       "         'بالحرم': 1,\n",
       "         'الحج': 1,\n",
       "         'ودولوا': 1,\n",
       "         'عقو': 1,\n",
       "         'سعيد': 10,\n",
       "         'حفله': 6,\n",
       "         'مطعم': 11,\n",
       "         'الصديق': 1,\n",
       "         'المبدع': 1,\n",
       "         'بانز': 1,\n",
       "         'شهر': 1,\n",
       "         'العسل': 1,\n",
       "         'نجيب': 1,\n",
       "         'محفوظ': 1,\n",
       "         'رفوف': 1,\n",
       "         'لكتب': 1,\n",
       "         'كتب': 1,\n",
       "         'نوادر': 1,\n",
       "         'دار': 1,\n",
       "         'المجد': 2,\n",
       "         'مغضوب': 1,\n",
       "         'عليهم': 1,\n",
       "         'وعمايله': 1,\n",
       "         'الصباح': 1,\n",
       "         'مسابقه': 1,\n",
       "         'لصقور': 6,\n",
       "         'نبارك': 2,\n",
       "         'لابن': 1,\n",
       "         'العم': 1,\n",
       "         'سامي': 1,\n",
       "         'العنزي': 1,\n",
       "         'الفوز': 1,\n",
       "         'الجوله': 1,\n",
       "         'انتظار': 3,\n",
       "         'ال': 14,\n",
       "         'اصعب': 1,\n",
       "         'لحظه': 1,\n",
       "         'تروح': 1,\n",
       "         'ياليتها': 1,\n",
       "         'معانا': 2,\n",
       "         'دايما': 1,\n",
       "         'ماقلنا': 1,\n",
       "         'انكم': 1,\n",
       "         'مسوول': 1,\n",
       "         'موقع': 8,\n",
       "         'علاقه': 1,\n",
       "         'بالاطراف': 1,\n",
       "         'الثلاثه': 1,\n",
       "         'الجمال': 1,\n",
       "         'والمتعه': 1,\n",
       "         'البصريه': 1,\n",
       "         'وشفيه': 1,\n",
       "         'معلق': 1,\n",
       "         'واشك': 1,\n",
       "         'فاخر': 1,\n",
       "         'الاصفر': 1,\n",
       "         'السعر': 2,\n",
       "         'الاسعار': 3,\n",
       "         'شامله': 1,\n",
       "         'القيمه': 1,\n",
       "         'المضافه': 1,\n",
       "         'والتوصيل': 1,\n",
       "         'مرتبطه': 1,\n",
       "         'واشكاله': 4,\n",
       "         'وتوجه': 6,\n",
       "         'رساله': 7,\n",
       "         'السعودين': 7,\n",
       "         'فيديوهات': 6,\n",
       "         'واشكالها': 6,\n",
       "         'صوت': 4,\n",
       "         'بيروت': 4,\n",
       "         'انترناشونال': 4,\n",
       "         'احيت': 4,\n",
       "         'حفلا': 5,\n",
       "         'ضخما': 4,\n",
       "         'وعبرت': 4,\n",
       "         'فرحتها': 4,\n",
       "         'بالمشاركه': 4,\n",
       "         'ارت': 4,\n",
       "         'عارضا': 1,\n",
       "         'سيرك': 1,\n",
       "         'دو': 1,\n",
       "         'سوليه': 1,\n",
       "         'يحكون': 1,\n",
       "         'القصه': 1,\n",
       "         'الواقعيه': 1,\n",
       "         'لاعب': 1,\n",
       "         'ليونيل': 2,\n",
       "         'ميسي': 2,\n",
       "         'بعروض': 1,\n",
       "         'سركيه': 1,\n",
       "         'تفوتوا': 1,\n",
       "         'المتعه': 1,\n",
       "         'سحب': 3,\n",
       "         'سريع': 3,\n",
       "         'الرتويت': 3,\n",
       "         'موثق': 3,\n",
       "         'ساعات': 5,\n",
       "         'قتال': 4,\n",
       "         'الروبوتات': 4,\n",
       "         'العالمي': 5,\n",
       "         'يومنا': 4,\n",
       "         'حماسي': 4,\n",
       "         'التحديات': 4,\n",
       "         'مستمره': 4,\n",
       "         'تفوتك': 4,\n",
       "         'والشاب': 11,\n",
       "         'يشعلان': 11,\n",
       "         'ليل': 9,\n",
       "         'ولطلب': 1,\n",
       "         'موقعنا': 3,\n",
       "         'عادي': 1,\n",
       "         'تزوجين': 1,\n",
       "         'مكرش': 1,\n",
       "         'النحيف': 1,\n",
       "         'المواهب': 2,\n",
       "         'رابطه': 1,\n",
       "         'عش': 1,\n",
       "         'لمحبين': 1,\n",
       "         'الون': 1,\n",
       "         'الازرق': 1,\n",
       "         'ايلوبا': 1,\n",
       "         'روز': 1,\n",
       "         'دايم': 1,\n",
       "         'هديه': 2,\n",
       "         'دائم': 1,\n",
       "         'ازرق': 1,\n",
       "         'الهلال': 1,\n",
       "         'اطلب': 5,\n",
       "         'هديتك': 3,\n",
       "         'توصلك': 3,\n",
       "         'جاي': 5,\n",
       "         'الخير': 7,\n",
       "         'حلو': 3,\n",
       "         'بتغريده': 3,\n",
       "         'تريد': 1,\n",
       "         'معرفته': 1,\n",
       "         'جروف': 1,\n",
       "         'ترحالك': 1,\n",
       "         'تخيل': 4,\n",
       "         'اقلكم': 1,\n",
       "         'وسكتنا': 1,\n",
       "         'افقع': 1,\n",
       "         'عينك': 1,\n",
       "         'خلينا': 1,\n",
       "         'نفرج': 1,\n",
       "         'جلست': 7,\n",
       "         'عربي': 7,\n",
       "         'امريكا': 7,\n",
       "         'هوي': 7,\n",
       "         'اخواني': 7,\n",
       "         'لرياض': 10,\n",
       "         'حينما': 7,\n",
       "         'خنقت': 7,\n",
       "         'انفاس': 7,\n",
       "         'حجه': 7,\n",
       "         'فنان': 3,\n",
       "         'تركي': 1,\n",
       "         'العبداله': 1,\n",
       "         'يولع': 1,\n",
       "         'الدنيا': 7,\n",
       "         'سفري': 1,\n",
       "         'يفوز': 1,\n",
       "         'ماجده': 1,\n",
       "         'الرومي': 1,\n",
       "         'متواجده': 1,\n",
       "         'وليش': 1,\n",
       "         'نجي': 2,\n",
       "         'اصلا': 1,\n",
       "         'انت': 4,\n",
       "         'لحالك': 1,\n",
       "         'حميد': 2,\n",
       "         'احلي': 3,\n",
       "         'الاغاني': 8,\n",
       "         'ويطربنا': 1,\n",
       "         'بالانغام': 1,\n",
       "         'ليالي': 10,\n",
       "         'عدن': 9,\n",
       "         'واستمتع': 1,\n",
       "         'با': 1,\n",
       "         'لت': 1,\n",
       "         'جو': 3,\n",
       "         'جديد': 3,\n",
       "         'مختلف': 3,\n",
       "         'زون': 3,\n",
       "         'عشت': 3,\n",
       "         'الجو': 3,\n",
       "         'وقابلت': 3,\n",
       "         'عازف': 3,\n",
       "         'القانون': 3,\n",
       "         'الشهير': 3,\n",
       "         'تش': 3,\n",
       "         'وسالته': 3,\n",
       "         'رشا': 5,\n",
       "         'ابي': 2,\n",
       "         'لحفله': 2,\n",
       "         'الاثنين': 2,\n",
       "         'الثلاثاء': 4,\n",
       "         'المصاريا': 1,\n",
       "         'مسيطرين': 1,\n",
       "         'تفاصيل': 1,\n",
       "         'وحده': 5,\n",
       "         'حجزتها': 1,\n",
       "         'ومقدر': 1,\n",
       "         'احظر': 1,\n",
       "         'الصامل': 2,\n",
       "         'يجي': 6,\n",
       "         'طرق': 2,\n",
       "         'الدفع': 2,\n",
       "         'تحويل': 2,\n",
       "         'تدشين': 3,\n",
       "         'سيارات': 3,\n",
       "         'معرض': 8,\n",
       "         'لسيارات': 8,\n",
       "         'الفتره': 3,\n",
       "         'سباقات': 3,\n",
       "         'الخيل': 3,\n",
       "         'الحفل': 3,\n",
       "         'الشوط': 7,\n",
       "         'الفائز': 3,\n",
       "         'فايقه': 3,\n",
       "         'الوصف': 3,\n",
       "         'ابنه': 3,\n",
       "         'حسين': 3,\n",
       "         'مهدي': 3,\n",
       "         'حيد': 3,\n",
       "         'الدمام': 9,\n",
       "         'البنوك': 7,\n",
       "         'سداد': 22,\n",
       "         'قروض': 8,\n",
       "         'متعثرات': 7,\n",
       "         'سمه': 7,\n",
       "         'ايقاف': 8,\n",
       "         'الخدمات': 8,\n",
       "         'الراجحي': 8,\n",
       "         'راتب': 7,\n",
       "         'لين': 1,\n",
       "         'يطيح': 1,\n",
       "         'براسك': 1,\n",
       "         'ام': 3,\n",
       "         'زين': 1,\n",
       "         'الصدفه': 1,\n",
       "         'جمعتنا': 1,\n",
       "         ...})"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "from arabic_reshaper import arabic_reshaper\n",
    "from bidi.algorithm import get_display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "# using list comprehension\n",
    "listToStr = ' '.join([str(elem) for elem in all_words])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "\n",
    "from flair.embeddings import TransformerDocumentEmbeddings\n",
    "from gensim.models.coherencemodel import CoherenceModel\n",
    "import gensim.corpora as corpora\n",
    "from gensim.models import LdaMulticore\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import arabic_reshaper\n",
    "from bidi.algorithm import get_display\n",
    "from wordcloud import WordCloud\n",
    "#...\n",
    "reshaped_text = arabic_reshaper.reshape(listToStr)\n",
    "data = get_display(reshaped_text) # add this line\n",
    "WordCloud = WordCloud(font_path='arial', background_color='white',\n",
    "                  mode='RGB', max_font_size=50, max_words=100,).generate(data)\n",
    "c=\"سحابة الكلمات \"\n",
    "reshaped_textc = arabic_reshaper.reshape(c)\n",
    "datac = get_display(reshaped_textc) # add this line\n",
    "plt.title(datac,fontsize=25, color='black')\n",
    "plt.imshow(WordCloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sentiment Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict, KFold\n",
    "from sklearn.metrics  import confusion_matrix, classification_report\n",
    "from sklearn import metrics\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove the \"Neutral\" class\n",
    "df=df[df['sentiment'] != \"Neutral\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# change values to numeric\n",
    "df['sentiment'] = df['sentiment'].map({'Positive': 1, 'Negative': 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# idneitfy the data and the labels\n",
    "data= df['clean text']\n",
    "target= df['sentiment']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(986, 2181)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use TfidfVectorizer for feature extraction (TFIDF to convert textual data to numeric form):\n",
    "tf_vec = TfidfVectorizer()\n",
    "X = tf_vec.fit_transform(data)\n",
    "X.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training Phase\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, target, test_size=0.20, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(788, 2181) (198, 2181) (788,) (198,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM accuracy: 0.94\n",
      "LogisticRegression accuracy: 0.90\n",
      "KNN accuracy: 0.88\n"
     ]
    }
   ],
   "source": [
    "# create the classifer and fit the training data and lables\n",
    "classifier_svm1 = svm.SVC(kernel='linear', C=1, probability=True).fit(X_train,y_train)\n",
    "logreg_clf1 = LogisticRegression().fit(X_train,y_train)\n",
    "KNN_model1 = KNeighborsClassifier(n_neighbors=5).fit(X_train,y_train)\n",
    "#gnb = GaussianNB().fit(X_train, y_train)\n",
    "\n",
    "print(\"SVM accuracy: %.2f\"%classifier_svm1.score(X_test, y_test))\n",
    "print(\"LogisticRegression accuracy: %.2f\"%logreg_clf1.score(X_test, y_test))\n",
    "print(\"KNN accuracy: %.2f\"%KNN_model1.score(X_test, y_test))\n",
    "#print(\"NB accuracy: %.2f\"%gnb.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.69      0.82        39\n",
      "           1       0.93      1.00      0.96       159\n",
      "\n",
      "    accuracy                           0.94       198\n",
      "   macro avg       0.96      0.85      0.89       198\n",
      "weighted avg       0.94      0.94      0.93       198\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.49      0.66        39\n",
      "           1       0.89      1.00      0.94       159\n",
      "\n",
      "    accuracy                           0.90       198\n",
      "   macro avg       0.94      0.74      0.80       198\n",
      "weighted avg       0.91      0.90      0.88       198\n",
      "\n",
      "classification_report               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.67      0.68        39\n",
      "           1       0.92      0.93      0.93       159\n",
      "\n",
      "    accuracy                           0.88       198\n",
      "   macro avg       0.81      0.80      0.80       198\n",
      "weighted avg       0.88      0.88      0.88       198\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "svm_pred = classifier_svm1.predict(X_test)\n",
    "logreg_clf_pred=logreg_clf1.predict(X_test)\n",
    "KNN_model_pred=KNN_model1.predict(X_test)\n",
    "\n",
    "print (\"classification_report\", metrics.classification_report(y_test, svm_pred))\n",
    "print (\"classification_report\", metrics.classification_report(y_test, logreg_clf_pred))\n",
    "print (\"classification_report\", metrics.classification_report(y_test, KNN_model_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "texts = [[word for word in str(document).split()] for document in all_words]\n",
    "id2word = corpora.Dictionary(texts)\n",
    "corpus = [id2word.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [],
   "source": [
    "#chang the number of topics here\n",
    "no_topics = 10\n",
    "\n",
    "# run LDA\n",
    "lda = LdaMulticore(corpus, id2word=id2word, num_topics=no_topics ,alpha=0.5, eval_every=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rawan\\Anaconda3\\rawan\\lib\\site-packages\\pyLDAvis\\_prepare.py:246: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  default_term_info = default_term_info.sort_values(\n"
     ]
    }
   ],
   "source": [
    "import pyLDAvis.gensim_models as gensimvis\n",
    "import pickle\n",
    "import os\n",
    "import pyLDAvis# Visualize the topics\n",
    "import pyLDAvis\n",
    "import pyLDAvis.sklearn\n",
    "\n",
    "#Creating Topic Distance Visualization \n",
    "pyLDAvis.enable_notebook()\n",
    "p = gensimvis.prepare(lda, corpus, id2word)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "col": 1,
        "height": 3,
        "row": 33,
        "width": 4
       }
      }
     }
    }
   },
   "source": [
    "# Topic Modeling Using LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "default_view": {
        "hidden": true
       }
      }
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rawan\\Anaconda3\\rawan\\lib\\site-packages\\ipykernel\\ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "\n",
       "\n",
       "<div id=\"ldavis_el2167615896960708161082806501\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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       "\n",
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       "       new LDAvis(\"#\" + \"ldavis_el2167615896960708161082806501\", ldavis_el2167615896960708161082806501_data);\n",
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       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
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       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "5     -0.005929 -0.006519       1        1  10.056834\n",
       "6     -0.008774  0.006648       2        1  10.048946\n",
       "7      0.007500  0.003687       3        1  10.037147\n",
       "8     -0.005101 -0.002538       4        1  10.003915\n",
       "4      0.004875 -0.008292       5        1   9.994832\n",
       "2     -0.000349  0.003292       6        1   9.991169\n",
       "1      0.001297  0.002671       7        1   9.984384\n",
       "9      0.006168  0.002994       8        1   9.983791\n",
       "0     -0.001165 -0.000601       9        1   9.968072\n",
       "3      0.001480 -0.001341      10        1   9.930911, topic_info=         Term        Freq       Total Category  logprob  loglift\n",
       "27      هدايا  279.000000  279.000000  Default  30.0000  30.0000\n",
       "25     جوالين  278.000000  278.000000  Default  29.0000  29.0000\n",
       "1        موسم  439.000000  439.000000  Default  28.0000  28.0000\n",
       "23       علما  278.000000  278.000000  Default  27.0000  27.0000\n",
       "20      اليله  292.000000  292.000000  Default  26.0000  26.0000\n",
       "..        ...         ...         ...      ...      ...      ...\n",
       "90       رياض    5.488817   61.161776  Topic10  -5.4280  -0.1013\n",
       "5        اديل    4.765216   49.940468  Topic10  -5.5693  -0.0400\n",
       "89   بوليفارد    5.555960   64.400993  Topic10  -5.4158  -0.1407\n",
       "158      خالد    3.667859   36.130176  Topic10  -5.8311   0.0220\n",
       "392       طقم    3.363615   43.052321  Topic10  -5.9176  -0.2399\n",
       "\n",
       "[648 rows x 6 columns], token_table=      Topic      Freq   Term\n",
       "term                        \n",
       "92        1  0.080148   احجز\n",
       "92        2  0.080148   احجز\n",
       "92        3  0.080148   احجز\n",
       "92        4  0.080148   احجز\n",
       "92        5  0.080148   احجز\n",
       "...     ...       ...    ...\n",
       "576       7  0.129411   يوجد\n",
       "576       8  0.129411   يوجد\n",
       "576       9  0.129411   يوجد\n",
       "858       1  0.231718  يومنا\n",
       "858      10  0.231718  يومنا\n",
       "\n",
       "[1820 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 7, 8, 9, 5, 3, 2, 10, 1, 4])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
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   "source": [
    "# Thanks"
   ]
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