{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1d635f40",
   "metadata": {},
   "source": [
    "## FastText Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a08afcc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('augmented_Data_working_1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44318d10",
   "metadata": {},
   "outputs": [],
   "source": [
    "masterDataFrame = data.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e695f7a2",
   "metadata": {},
   "source": [
    "## Preprocessed Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "36166efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    __label__POSITIVE \n",
       "1    __label__NEGATIVE \n",
       "2    __label__NEGATIVE \n",
       "3    __label__NEGATIVE \n",
       "4    __label__POSITIVE \n",
       "Name: _sent_label, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_sent_label'] = '__label__' + data['Sentiment'] + \" \"\n",
    "masterDataFrame['_sent_label'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dedf72fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                                      __label__POSITIVE محبت رنگ جانا ا دل دل ملنا\n",
       "1                                                                      __label__NEGATIVE لیکن مشکل دل بڑی مشکل ملنا\n",
       "2                                           __label__NEGATIVE محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا\n",
       "3    __label__NEGATIVE دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب\n",
       "4                                   __label__POSITIVE انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا\n",
       "Name: _text_sent, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_text_sent'] = masterDataFrame['_sent_label']+masterDataFrame['preprocessed_text']\n",
    "masterDataFrame['_text_sent'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41b3ce67",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(masterDataFrame, random_state=2,stratify=masterDataFrame['Sentiment'],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18e96fa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEGATIVE    4874\n",
       "POSITIVE    2724\n",
       "NEUTRAL     2314\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b1e1ee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEGATIVE    1219\n",
       "POSITIVE     681\n",
       "NEUTRAL      579\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63d1f6a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1e8c5388",
   "metadata": {},
   "source": [
    "## on Raw Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b87ef92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                                   __label__POSITIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے —\n",
       "1                                                                           __label__NEGATIVE لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "2         __label__NEGATIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے ——\\n \\n   لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "3    __label__NEGATIVE دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے\\nہر جگہ ڈی چوک سمجھ رکھا ہے\\n🖐️🖐️🖐️\\n\\n#دررسول_جائےادب\n",
       "4                                                       __label__POSITIVE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔\n",
       "Name: _text_sent_raw, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_text_sent_raw'] = masterDataFrame['_sent_label']+masterDataFrame['text']\n",
    "masterDataFrame['_text_sent_raw'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ff7ac3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9d140e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'asasd'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4bb6666a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is a sample text. It has multiple new lines. Let's replace them with a single space.\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "text = \"This is a sample text.\\n\\n\\nIt has multiple new lines.\\n\\n\\nLet's replace them with a single space.\"\n",
    "\n",
    "def replace_newLines(text):\n",
    "    # Replace one or more new lines with a space\n",
    "    return re.sub(r'\\n+', ' ', text)\n",
    "\n",
    "# Print the clean text\n",
    "print(clean_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a582f02b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = r'\\n+'\n",
    "# Apply the regular expression to the '_text_sent_raw' column\n",
    "masterDataFrame['_text_sent_raw'] = masterDataFrame['_text_sent_raw'].apply(lambda x: re.sub(pattern, ' ', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "72e7cf73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                               __label__POSITIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے —\n",
       "1                                                                         __label__NEGATIVE لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "2          __label__NEGATIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے ——      لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "3    __label__NEGATIVE دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے ہر جگہ ڈی چوک سمجھ رکھا ہے 🖐️🖐️🖐️ #دررسول_جائےادب\n",
       "4                                                  __label__POSITIVE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔\n",
       "Name: _text_sent_raw, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_text_sent_raw'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70f70ebe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f2f8666f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(masterDataFrame, random_state=2,stratify=masterDataFrame['Sentiment'],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3b126e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEGATIVE    4874\n",
       "POSITIVE    2724\n",
       "NEUTRAL     2314\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "30810906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEGATIVE    1219\n",
       "POSITIVE     681\n",
       "NEUTRAL      579\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9f77e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('sent_train_raw.csv', columns=['_text_sent_raw'], index=False, header=False)\n",
    "test.to_csv('sent_test_raw.csv', columns=['_text_sent_raw'], index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3159f3c",
   "metadata": {},
   "source": [
    "## Bullying train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f881d4ac",
   "metadata": {},
   "source": [
    "## on Raw Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f56f177a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        __label__NONE \n",
       "1        __label__NONE \n",
       "2        __label__NONE \n",
       "3    __label__BULLYING \n",
       "4        __label__NONE \n",
       "Name: _bullying_label, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_bullying_label'] = '__label__' + data['Bullying'] + \" \"\n",
    "masterDataFrame['_bullying_label'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dedf72fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                                       __label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے —\n",
       "1                                                                               __label__NONE لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "2             __label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے ——\\n \\n   لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "3    __label__BULLYING دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے\\nہر جگہ ڈی چوک سمجھ رکھا ہے\\n🖐️🖐️🖐️\\n\\n#دررسول_جائےادب\n",
       "4                                                           __label__NONE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔\n",
       "Name: _text_bullying_raw, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_text_bullying_raw'] = masterDataFrame['_bullying_label']+masterDataFrame['text']\n",
    "masterDataFrame['_text_bullying_raw'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b72390a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f1f2689f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = r'\\n+'\n",
    "# Apply the regular expression to the '_text_sent_raw' column\n",
    "masterDataFrame['_text_bullying_raw'] = masterDataFrame['_text_bullying_raw'].apply(lambda x: re.sub(pattern, ' ', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "492ec2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                                   __label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے —\n",
       "1                                                                             __label__NONE لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "2              __label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے ——      لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔\n",
       "3    __label__BULLYING دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے ہر جگہ ڈی چوک سمجھ رکھا ہے 🖐️🖐️🖐️ #دررسول_جائےادب\n",
       "4                                                      __label__NONE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔\n",
       "Name: _text_bullying_raw, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_text_bullying_raw'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9f6631a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(masterDataFrame, random_state=2,stratify=masterDataFrame['Bullying'],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "50cc0f72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE                              6878\n",
       "BULLYING                          1226\n",
       "OFFENSIVE                          764\n",
       "OFFENSIVE.BULLYING.NAMECALLING     591\n",
       "PROFANE                            318\n",
       "OFFENSIVE,THREAT                    74\n",
       "OFFENSIVE,CURSE                     61\n",
       "Name: Bullying, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Bullying'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bb0136cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE                              1720\n",
       "BULLYING                           307\n",
       "OFFENSIVE                          191\n",
       "OFFENSIVE.BULLYING.NAMECALLING     148\n",
       "PROFANE                             80\n",
       "OFFENSIVE,THREAT                    18\n",
       "OFFENSIVE,CURSE                     15\n",
       "Name: Bullying, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Bullying'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d6bb604c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('text_bullying_train_raw.csv', columns=['_text_bullying_raw'], index=False, header=False)\n",
    "test.to_csv('text_bullying_test_raw.csv', columns=['_text_bullying_raw'], index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20272a6b",
   "metadata": {},
   "source": [
    "## Preprocessed text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7038ccc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dedf72fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                                          __label__NONE محبت رنگ جانا ا دل دل ملنا\n",
       "1                                                                          __label__NONE لیکن مشکل دل بڑی مشکل ملنا\n",
       "2                                               __label__NONE محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا\n",
       "3    __label__BULLYING دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب\n",
       "4                                       __label__NONE انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا\n",
       "Name: _text_bullying_preprocessed, dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masterDataFrame['_text_bullying_preprocessed'] = masterDataFrame['_bullying_label']+masterDataFrame['preprocessed_text']\n",
    "masterDataFrame['_text_bullying_preprocessed'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eafd7176",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "446f0784",
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(masterDataFrame, random_state=2,stratify=masterDataFrame['Bullying'],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a39d3781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE                              6878\n",
       "BULLYING                          1226\n",
       "OFFENSIVE                          764\n",
       "OFFENSIVE.BULLYING.NAMECALLING     591\n",
       "PROFANE                            318\n",
       "OFFENSIVE,THREAT                    74\n",
       "OFFENSIVE,CURSE                     61\n",
       "Name: Bullying, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Bullying'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "efdf47ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE                              1720\n",
       "BULLYING                           307\n",
       "OFFENSIVE                          191\n",
       "OFFENSIVE.BULLYING.NAMECALLING     148\n",
       "PROFANE                             80\n",
       "OFFENSIVE,THREAT                    18\n",
       "OFFENSIVE,CURSE                     15\n",
       "Name: Bullying, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['Bullying'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "41f1ebcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.to_csv('text_bullying_train_prep.csv', columns=['_text_bullying_preprocessed'], index=False, header=False)\n",
    "test.to_csv('text_bullying_test_prep.csv', columns=['_text_bullying_preprocessed'], index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7c214bba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "from wordcloud import STOPWORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "92b170f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>text</th>\n",
       "      <th>preprocessed_text</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>Bullying</th>\n",
       "      <th>sent_pred</th>\n",
       "      <th>bullying_pred</th>\n",
       "      <th>_sent_label</th>\n",
       "      <th>_text_sent</th>\n",
       "      <th>_text_sent_raw</th>\n",
       "      <th>_bullying_label</th>\n",
       "      <th>_text_bullying_raw</th>\n",
       "      <th>_text_bullying_preprocessed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>@Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے —</td>\n",
       "      <td>محبت رنگ جانا ا دل دل ملنا</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>__label__POSITIVE</td>\n",
       "      <td>__label__POSITIVE محبت رنگ جانا ا دل دل ملنا</td>\n",
       "      <td>__label__POSITIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے —</td>\n",
       "      <td>__label__NONE</td>\n",
       "      <td>__label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے —</td>\n",
       "      <td>__label__NONE محبت رنگ جانا ا دل دل ملنا</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔</td>\n",
       "      <td>لیکن مشکل دل بڑی مشکل ملنا</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE لیکن مشکل دل بڑی مشکل ملنا</td>\n",
       "      <td>__label__NEGATIVE لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔</td>\n",
       "      <td>__label__NONE</td>\n",
       "      <td>__label__NONE لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔</td>\n",
       "      <td>__label__NONE لیکن مشکل دل بڑی مشکل ملنا</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>@Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے ——\\n \\n   لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔</td>\n",
       "      <td>محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا</td>\n",
       "      <td>__label__NEGATIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے ——      لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔</td>\n",
       "      <td>__label__NONE</td>\n",
       "      <td>__label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے ——      لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔</td>\n",
       "      <td>__label__NONE محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے\\nہر جگہ ڈی چوک سمجھ رکھا ہے\\n🖐️🖐️🖐️\\n\\n#دررسول_جائےادب</td>\n",
       "      <td>دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>BULLYING</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>BULLYING</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب</td>\n",
       "      <td>__label__NEGATIVE دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے ہر جگہ ڈی چوک سمجھ رکھا ہے 🖐️🖐️🖐️ #دررسول_جائےادب</td>\n",
       "      <td>__label__BULLYING</td>\n",
       "      <td>__label__BULLYING دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے ہر جگہ ڈی چوک سمجھ رکھا ہے 🖐️🖐️🖐️ #دررسول_جائےادب</td>\n",
       "      <td>__label__BULLYING دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔</td>\n",
       "      <td>انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>NONE</td>\n",
       "      <td>__label__POSITIVE</td>\n",
       "      <td>__label__POSITIVE انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا</td>\n",
       "      <td>__label__POSITIVE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔</td>\n",
       "      <td>__label__NONE</td>\n",
       "      <td>__label__NONE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔</td>\n",
       "      <td>__label__NONE انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12386</th>\n",
       "      <td>12386</td>\n",
       "      <td>12323</td>\n",
       "      <td>تو تو صرف بے-غیرتی کا نمونہ ہے۔۔۔ @khanbaba253</td>\n",
       "      <td>بد غیرتی نمونہ</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>OFFENSIVE</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>OFFENSIVE</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE بد غیرتی نمونہ</td>\n",
       "      <td>__label__NEGATIVE تو تو صرف بے-غیرتی کا نمونہ ہے۔۔۔ @khanbaba253</td>\n",
       "      <td>__label__OFFENSIVE</td>\n",
       "      <td>__label__OFFENSIVE تو تو صرف بے-غیرتی کا نمونہ ہے۔۔۔ @khanbaba253</td>\n",
       "      <td>__label__OFFENSIVE بد غیرتی نمونہ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12387</th>\n",
       "      <td>12387</td>\n",
       "      <td>12324</td>\n",
       "      <td>یہ بزدل شخص صرف میر جعفر ہے۔۔۔</td>\n",
       "      <td>بزدل شخص میر جعفر</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>OFFENSIVE</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>BULLYING</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE بزدل شخص میر جعفر</td>\n",
       "      <td>__label__NEGATIVE یہ بزدل شخص صرف میر جعفر ہے۔۔۔</td>\n",
       "      <td>__label__OFFENSIVE</td>\n",
       "      <td>__label__OFFENSIVE یہ بزدل شخص صرف میر جعفر ہے۔۔۔</td>\n",
       "      <td>__label__OFFENSIVE بزدل شخص میر جعفر</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12388</th>\n",
       "      <td>12388</td>\n",
       "      <td>12325</td>\n",
       "      <td>میر جعفر تو واقعی بے-غیرت ہے۔۔۔ @umerAli786</td>\n",
       "      <td>میر جعفر واقعی بد غیرت</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>OFFENSIVE</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>OFFENSIVE</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE میر جعفر واقعی بد غیرت</td>\n",
       "      <td>__label__NEGATIVE میر جعفر تو واقعی بے-غیرت ہے۔۔۔ @umerAli786</td>\n",
       "      <td>__label__OFFENSIVE</td>\n",
       "      <td>__label__OFFENSIVE میر جعفر تو واقعی بے-غیرت ہے۔۔۔ @umerAli786</td>\n",
       "      <td>__label__OFFENSIVE میر جعفر واقعی بد غیرت</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12389</th>\n",
       "      <td>12389</td>\n",
       "      <td>12326</td>\n",
       "      <td>بے-غیرت بزدلوں میں صرف میر جعفر ہی ہوتا ہے۔۔۔ @khanbaba253</td>\n",
       "      <td>بد غیرت بزدل میر جعفر</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>OFFENSIVE</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>OFFENSIVE</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE بد غیرت بزدل میر جعفر</td>\n",
       "      <td>__label__NEGATIVE بے-غیرت بزدلوں میں صرف میر جعفر ہی ہوتا ہے۔۔۔ @khanbaba253</td>\n",
       "      <td>__label__OFFENSIVE</td>\n",
       "      <td>__label__OFFENSIVE بے-غیرت بزدلوں میں صرف میر جعفر ہی ہوتا ہے۔۔۔ @khanbaba253</td>\n",
       "      <td>__label__OFFENSIVE بد غیرت بزدل میر جعفر</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12390</th>\n",
       "      <td>12390</td>\n",
       "      <td>12327</td>\n",
       "      <td>کیا تیری بچی چود دی ہے کسی نے یا باجی ۔ بھین چود پھدی کے منہ والے</td>\n",
       "      <td>کیا تیرنا بچی چودنا دینا باجی بھین چودنا پھدی منہ</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>PROFANE</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>PROFANE</td>\n",
       "      <td>__label__NEGATIVE</td>\n",
       "      <td>__label__NEGATIVE کیا تیرنا بچی چودنا دینا باجی بھین چودنا پھدی منہ</td>\n",
       "      <td>__label__NEGATIVE کیا تیری بچی چود دی ہے کسی نے یا باجی ۔ بھین چود پھدی کے منہ والے</td>\n",
       "      <td>__label__PROFANE</td>\n",
       "      <td>__label__PROFANE کیا تیری بچی چود دی ہے کسی نے یا باجی ۔ بھین چود پھدی کے منہ والے</td>\n",
       "      <td>__label__PROFANE کیا تیرنا بچی چودنا دینا باجی بھین چودنا پھدی منہ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12391 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0.1  Unnamed: 0  \\\n",
       "0                 0           0   \n",
       "1                 1           1   \n",
       "2                 2           2   \n",
       "3                 3           3   \n",
       "4                 4           5   \n",
       "...             ...         ...   \n",
       "12386         12386       12323   \n",
       "12387         12387       12324   \n",
       "12388         12388       12325   \n",
       "12389         12389       12326   \n",
       "12390         12390       12327   \n",
       "\n",
       "                                                                                                                           text  \\\n",
       "0                                                                     @Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے —   \n",
       "1                                                                             لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔   \n",
       "2           @Ayeshan233 کہ محبت رنگ دے جاتی ے \\nجب دل سے دل ملتا ہے ——\\n \\n   لیکن مشکل یہ ہیں \\n\\n کے دل بڑی مشکل سے ملتا ہیں۔   \n",
       "3      دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے\\nہر جگہ ڈی چوک سمجھ رکھا ہے\\n🖐️🖐️🖐️\\n\\n#دررسول_جائےادب   \n",
       "4                                                          انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔   \n",
       "...                                                                                                                         ...   \n",
       "12386                                                                            تو تو صرف بے-غیرتی کا نمونہ ہے۔۔۔ @khanbaba253   \n",
       "12387                                                                                            یہ بزدل شخص صرف میر جعفر ہے۔۔۔   \n",
       "12388                                                                               میر جعفر تو واقعی بے-غیرت ہے۔۔۔ @umerAli786   \n",
       "12389                                                                بے-غیرت بزدلوں میں صرف میر جعفر ہی ہوتا ہے۔۔۔ @khanbaba253   \n",
       "12390                                                         کیا تیری بچی چود دی ہے کسی نے یا باجی ۔ بھین چود پھدی کے منہ والے   \n",
       "\n",
       "                                                                                  preprocessed_text  \\\n",
       "0                                                                        محبت رنگ جانا ا دل دل ملنا   \n",
       "1                                                                        لیکن مشکل دل بڑی مشکل ملنا   \n",
       "2                                             محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا   \n",
       "3      دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب   \n",
       "4                                     انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا   \n",
       "...                                                                                             ...   \n",
       "12386                                                                                بد غیرتی نمونہ   \n",
       "12387                                                                             بزدل شخص میر جعفر   \n",
       "12388                                                                        میر جعفر واقعی بد غیرت   \n",
       "12389                                                                         بد غیرت بزدل میر جعفر   \n",
       "12390                                             کیا تیرنا بچی چودنا دینا باجی بھین چودنا پھدی منہ   \n",
       "\n",
       "      Sentiment   Bullying sent_pred bullying_pred         _sent_label  \\\n",
       "0      POSITIVE       NONE  POSITIVE          NONE  __label__POSITIVE    \n",
       "1      NEGATIVE       NONE  NEGATIVE          NONE  __label__NEGATIVE    \n",
       "2      NEGATIVE       NONE  NEGATIVE          NONE  __label__NEGATIVE    \n",
       "3      NEGATIVE   BULLYING  NEGATIVE      BULLYING  __label__NEGATIVE    \n",
       "4      POSITIVE       NONE  POSITIVE          NONE  __label__POSITIVE    \n",
       "...         ...        ...       ...           ...                 ...   \n",
       "12386  NEGATIVE  OFFENSIVE  NEGATIVE     OFFENSIVE  __label__NEGATIVE    \n",
       "12387  NEGATIVE  OFFENSIVE  NEGATIVE      BULLYING  __label__NEGATIVE    \n",
       "12388  NEGATIVE  OFFENSIVE  NEGATIVE     OFFENSIVE  __label__NEGATIVE    \n",
       "12389  NEGATIVE  OFFENSIVE  NEGATIVE     OFFENSIVE  __label__NEGATIVE    \n",
       "12390  NEGATIVE    PROFANE  NEGATIVE       PROFANE  __label__NEGATIVE    \n",
       "\n",
       "                                                                                                           _text_sent  \\\n",
       "0                                                                        __label__POSITIVE محبت رنگ جانا ا دل دل ملنا   \n",
       "1                                                                        __label__NEGATIVE لیکن مشکل دل بڑی مشکل ملنا   \n",
       "2                                             __label__NEGATIVE محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا   \n",
       "3      __label__NEGATIVE دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب   \n",
       "4                                     __label__POSITIVE انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا   \n",
       "...                                                                                                               ...   \n",
       "12386                                                                                __label__NEGATIVE بد غیرتی نمونہ   \n",
       "12387                                                                             __label__NEGATIVE بزدل شخص میر جعفر   \n",
       "12388                                                                        __label__NEGATIVE میر جعفر واقعی بد غیرت   \n",
       "12389                                                                         __label__NEGATIVE بد غیرت بزدل میر جعفر   \n",
       "12390                                             __label__NEGATIVE کیا تیرنا بچی چودنا دینا باجی بھین چودنا پھدی منہ   \n",
       "\n",
       "                                                                                                                              _text_sent_raw  \\\n",
       "0                                                                 __label__POSITIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے —   \n",
       "1                                                                           __label__NEGATIVE لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔   \n",
       "2            __label__NEGATIVE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے ——      لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔   \n",
       "3      __label__NEGATIVE دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے ہر جگہ ڈی چوک سمجھ رکھا ہے 🖐️🖐️🖐️ #دررسول_جائےادب   \n",
       "4                                                    __label__POSITIVE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔   \n",
       "...                                                                                                                                      ...   \n",
       "12386                                                                       __label__NEGATIVE تو تو صرف بے-غیرتی کا نمونہ ہے۔۔۔ @khanbaba253   \n",
       "12387                                                                                       __label__NEGATIVE یہ بزدل شخص صرف میر جعفر ہے۔۔۔   \n",
       "12388                                                                          __label__NEGATIVE میر جعفر تو واقعی بے-غیرت ہے۔۔۔ @umerAli786   \n",
       "12389                                                           __label__NEGATIVE بے-غیرت بزدلوں میں صرف میر جعفر ہی ہوتا ہے۔۔۔ @khanbaba253   \n",
       "12390                                                    __label__NEGATIVE کیا تیری بچی چود دی ہے کسی نے یا باجی ۔ بھین چود پھدی کے منہ والے   \n",
       "\n",
       "           _bullying_label  \\\n",
       "0           __label__NONE    \n",
       "1           __label__NONE    \n",
       "2           __label__NONE    \n",
       "3       __label__BULLYING    \n",
       "4           __label__NONE    \n",
       "...                    ...   \n",
       "12386  __label__OFFENSIVE    \n",
       "12387  __label__OFFENSIVE    \n",
       "12388  __label__OFFENSIVE    \n",
       "12389  __label__OFFENSIVE    \n",
       "12390    __label__PROFANE    \n",
       "\n",
       "                                                                                                                          _text_bullying_raw  \\\n",
       "0                                                                     __label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے —   \n",
       "1                                                                               __label__NONE لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔   \n",
       "2                __label__NONE @Ayeshan233 کہ محبت رنگ دے جاتی ے  جب دل سے دل ملتا ہے ——      لیکن مشکل یہ ہیں   کے دل بڑی مشکل سے ملتا ہیں۔   \n",
       "3      __label__BULLYING دھرنے میں پیدا ہونے والوں کو کیا پتہ حضور کے گھر کا احترام کیا ہے ہر جگہ ڈی چوک سمجھ رکھا ہے 🖐️🖐️🖐️ #دررسول_جائےادب   \n",
       "4                                                        __label__NONE  انے والی نسل با شعور ہوگی۔ انشااللہ وه بکنے اور جھکنے والی نہ ہوگی ۔   \n",
       "...                                                                                                                                      ...   \n",
       "12386                                                                      __label__OFFENSIVE تو تو صرف بے-غیرتی کا نمونہ ہے۔۔۔ @khanbaba253   \n",
       "12387                                                                                      __label__OFFENSIVE یہ بزدل شخص صرف میر جعفر ہے۔۔۔   \n",
       "12388                                                                         __label__OFFENSIVE میر جعفر تو واقعی بے-غیرت ہے۔۔۔ @umerAli786   \n",
       "12389                                                          __label__OFFENSIVE بے-غیرت بزدلوں میں صرف میر جعفر ہی ہوتا ہے۔۔۔ @khanbaba253   \n",
       "12390                                                     __label__PROFANE کیا تیری بچی چود دی ہے کسی نے یا باجی ۔ بھین چود پھدی کے منہ والے   \n",
       "\n",
       "                                                                                          _text_bullying_preprocessed  \n",
       "0                                                                            __label__NONE محبت رنگ جانا ا دل دل ملنا  \n",
       "1                                                                            __label__NONE لیکن مشکل دل بڑی مشکل ملنا  \n",
       "2                                                 __label__NONE محبت رنگ جانا ا دل دل ملنا لیکن مشکل دل بڑی مشکل ملنا  \n",
       "3      __label__BULLYING دھرنا پیدا ہونا والا کیا پتہ حضور گھر احترام کیا جگہ ڈی چوک سمجھنا رکھا 🖐 🖐 🖐 دررسول جائےادب  \n",
       "4                                         __label__NONE انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا  \n",
       "...                                                                                                               ...  \n",
       "12386                                                                               __label__OFFENSIVE بد غیرتی نمونہ  \n",
       "12387                                                                            __label__OFFENSIVE بزدل شخص میر جعفر  \n",
       "12388                                                                       __label__OFFENSIVE میر جعفر واقعی بد غیرت  \n",
       "12389                                                                        __label__OFFENSIVE بد غیرت بزدل میر جعفر  \n",
       "12390                                              __label__PROFANE کیا تیرنا بچی چودنا دینا باجی بھین چودنا پھدی منہ  \n",
       "\n",
       "[12391 rows x 14 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "214525ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                           محبت رنگ جانا ا دل دل ملنا\n",
       "4        انا والی نسل با شعور ہونا انشااللہ وه بکنا جھکنا والی نہ ہونا\n",
       "10                                                          انشاء اللہ\n",
       "12                                                       وعلیکم السلام\n",
       "19                                                                آمین\n",
       "                                     ...                              \n",
       "12352                                  عدالتی نظام مستحکم بننا واحد حل\n",
       "12353                       خاندان تعاون واحد حل اسلامی خاندانی نظام 👍\n",
       "12354                         سماجی امن امان واحد حل امن فراہمی نظام 👍\n",
       "12355                           توانائی مسائل واحد حل سستا بجلی نظام 👍\n",
       "12383                       عمیر اللہ تعالی چھوٹنا بڑی مصیبت نجات آمین\n",
       "Name: preprocessed_text, Length: 3405, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_tweets = data['preprocessed_text'][data[\"Sentiment\"] == 'POSITIVE']\n",
    "positive_tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "707e7d09",
   "metadata": {},
   "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": [
    "stop_words = common_words\n",
    "positive_wordcloud = WordCloud(font_path='urdu_font/urdu_font.ttf',max_font_size=50, max_words=50, background_color=\"white\").generate(str(positive_tweets))\n",
    "plt.figure()\n",
    "plt.title(\"Positive Tweets - Wordcloud\")\n",
    "plt.imshow(positive_wordcloud, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f691954b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a7c9fc40",
   "metadata": {},
   "source": [
    "### Random Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93e96eea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fasttext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0ded8c46",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='sent_train.csv', autotuneValidationFile='sent_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f3fcf742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1825, 0.6449315068493151, 0.6449315068493151)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='sent_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "febdc0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1825, 0.6553424657534247, 0.6553424657534247)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='sent_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "34e0fbf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1825, 0.68, 0.68)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='sent_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "378314ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.84352726]))"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('تمہاری تبصرے مجھے بہت اچھے لگتے ہیں')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9e7b1e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.57542932]))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('فتنۂ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "daebfb34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Negative',), array([0.99757844]))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('فتنہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "56e853df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Negative',), array([0.99949563]))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('مرتد')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2c2e11e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Negative'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('تم پٹواری ہو')[0][0].split('__label__')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "99a07331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive', '__label__Neutral', '__label__Negative'),\n",
       " array([0.46102011, 0.39414737, 0.14486252]))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(\"ہم جھکے نہیں ہم بکے نہیں ، جو کھڑے رہے ہیں محاذ پر،  ہمیں اُن صفوں میں تلاش\",k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ed51d4fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.62801576]))"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('الحمد لل ہ ڈالر کا ریٹ پٹرول سے کم ہوگیا یہ ہوتا ہے تجربہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9204fdb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.9999998807907104, 'وظیفہ'),\n",
       " (0.9999995231628418, 'شکوک'),\n",
       " (0.9999995231628418, 'بلندی'),\n",
       " (0.9999992251396179, 'nappywalanizai'),\n",
       " (0.9999991059303284, 'جزائر'),\n",
       " (0.9999989867210388, 'اتے'),\n",
       " (0.9999988675117493, 'اٹیک'),\n",
       " (0.9999973177909851, 'خوشی'),\n",
       " (0.9999969601631165, 'ذائقہ'),\n",
       " (0.9999960660934448, 'استغفراللہ')]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_nearest_neighbors('انشاء اللہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8202a83b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_labels',\n",
       " '_words',\n",
       " 'bucket',\n",
       " 'dim',\n",
       " 'epoch',\n",
       " 'f',\n",
       " 'get_analogies',\n",
       " 'get_dimension',\n",
       " 'get_input_matrix',\n",
       " 'get_input_vector',\n",
       " 'get_label_id',\n",
       " 'get_labels',\n",
       " 'get_line',\n",
       " 'get_meter',\n",
       " 'get_nearest_neighbors',\n",
       " 'get_output_matrix',\n",
       " 'get_sentence_vector',\n",
       " 'get_subword_id',\n",
       " 'get_subwords',\n",
       " 'get_word_id',\n",
       " 'get_word_vector',\n",
       " 'get_words',\n",
       " 'is_quantized',\n",
       " 'label',\n",
       " 'labels',\n",
       " 'loss',\n",
       " 'lr',\n",
       " 'lrUpdateRate',\n",
       " 'maxn',\n",
       " 'minCount',\n",
       " 'minCountLabel',\n",
       " 'minn',\n",
       " 'neg',\n",
       " 'predict',\n",
       " 'pretrainedVectors',\n",
       " 'quantize',\n",
       " 'save_model',\n",
       " 'set_args',\n",
       " 'set_matrices',\n",
       " 't',\n",
       " 'test',\n",
       " 'test_label',\n",
       " 'thread',\n",
       " 'verbose',\n",
       " 'wordNgrams',\n",
       " 'words',\n",
       " 'ws']"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f2563881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BOW',\n",
       " 'EOS',\n",
       " 'EOW',\n",
       " 'FastText',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " 'absolute_import',\n",
       " 'cbow',\n",
       " 'division',\n",
       " 'load_model',\n",
       " 'print_function',\n",
       " 'skipgram',\n",
       " 'supervised',\n",
       " 'tokenize',\n",
       " 'train_supervised',\n",
       " 'train_unsupervised',\n",
       " 'unicode_literals']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(fasttext)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "12e0e8c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.96275067]))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('عبداللہ اچھا بچہ ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "b9fcd911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.87282515]))"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('عبداللہ اچھا بچہ ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bd5ff633",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.838826]))"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('استغفراللہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "5ac2a3e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.66170126]))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict('HEC نے امیجن کپ کی اتنی شاندار تکمیل کے ذریعے بہت اچھا کام کیا ہے۔')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dc7bcceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.65451747]))"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " model.predict('hec نے امیجن کپ کی اتنی شاندار تکمیل کے ذریعے بہت اچھا کام کیا ہے')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9752b2a7",
   "metadata": {},
   "source": [
    "## FAst Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "58251b48",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2 = fasttext.train_supervised(input='sent_train.csv',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "09731574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2525, 0.7318811881188119, 0.7318811881188119)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.test(path='combined_sent_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "3d7ec9cf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.70057917]))"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('تمہاری تبصرے مجھے بہت اچھے لگتے ہیں')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e7e8a1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.98962945]))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('فتنۂ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a95606e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Negative',), array([0.99997711]))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('فتنہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "8ca62c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Negative',), array([0.9993766]))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('مرتد')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e46af712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Negative'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('تم پٹواری ہو')[0][0].split('__label__')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "301360b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive', '__label__Neutral', '__label__Negative'),\n",
       " array([0.88522828, 0.07577535, 0.03902645]))"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict(\"ہم جھکے نہیں ہم بکے نہیں ، جو کھڑے رہے ہیں محاذ پر،  ہمیں اُن صفوں میں تلاش\",k=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71cea054",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.predict('تم پٹواری ہو')[0][0].split('__label__')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1264e0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict(s)[0][0].split('__label__')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "b52ae320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.49642617]))"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('الحمد لل ہ ڈالر کا ریٹ پٹرول سے کم ہوگیا یہ ہوتا ہے تجربہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "59d92b26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.9999492764472961, 'افرین'),\n",
       " (0.9999485611915588, 'ماشاءالله'),\n",
       " (0.9999239444732666, 'جنونی'),\n",
       " (0.9999216198921204, 'واعلیکم'),\n",
       " (0.9999182820320129, 'اسٹریٹ'),\n",
       " (0.9999101161956787, 'ڈارک'),\n",
       " (0.9998976588249207, 'تحيا'),\n",
       " (0.9998923540115356, 'اسٹریٹن'),\n",
       " (0.9998906850814819, 'انائس'),\n",
       " (0.9998758435249329, 'برتھڈے')]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.get_nearest_neighbors('انشاء اللہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "9904cc82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.95730704]))"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('HEC نے امیجن کپ کی اتنی شاندار تکمیل کے ذریعے بہت اچھا کام کیا ہے۔')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "e5bd7a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.65451747]))"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " model.predict('hec نے امیجن کپ کی اتنی شاندار تکمیل کے ذریعے بہت اچھا کام کیا ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "629227da",
   "metadata": {},
   "outputs": [],
   "source": [
    "model3 = fasttext.train_supervised(input='combined_fast_text_r.csv', autotuneValidationFile='combined_sent_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e1b99851",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.84291101]))"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.predict('اچھا بچہ ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "33b5f571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.71113497]))"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.predict('HEC نے امیجن کپ کی اتنی شاندار تکمیل کے ذریعے بہت اچھا کام کیا ہے۔')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5cd966d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.99645984]))"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.predict('اچھا بچہ ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "60cefca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.get_dimension()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "c44c2732",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.get_dimension()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "472b0804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.99994624]))"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('آج موسم خوبصورت اور دلکش ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "997fbaf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12634, 0.8773151812569258, 0.8773151812569258)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.test(path='combined_fast_text_r.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "c3293e90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2525, 0.9845544554455445, 0.9845544554455445)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.test(path='combined_sent_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "e61c146d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.91407257]))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.predict('بہت خوش ہوں کہ ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "69caf47a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.82502848]))"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('میں نے آج کچھ نیا سیکھا ہے۔ بہت خوش ہوں کہ میرے پاس اس موقع کا موجودگی تھی')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f9c13f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.55606526]))"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.predict('آپ کی مدد سے میں نے اس مسئلے کا حل نکال لیا۔ شکریہ، آپ نے میری بہت مدد کی ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "da1084a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.44868371]))"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('آپ کی مدد سے میں نے اس مسئلے کا حل نکال لیا۔ شکریہ، آپ نے میری بہت مدد کی ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d690b9b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model3.save_model(\"model_sent_fastText.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "ad2678a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
     ]
    }
   ],
   "source": [
    "loaded_model = fasttext.load_model('model_sent_fastText.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "b44dcf55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.55606526]))"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict('آپ کی مدد سے میں نے اس مسئلے کا حل نکال لیا۔ شکریہ، آپ نے میری بہت مدد کی ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "970adfc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.99645984]))"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_model.predict('اچھا بچہ ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc66869b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "2853d5d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "e74076bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "اچھا بچہ\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Negative'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment_fast(s, model3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "f8063aa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "s = '-اچھا بچہ ہے'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "4f416e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('-اچھا بچہ ہے', 'اچھا بچہ')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s,urdu_preprocessing(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6c003cb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "29d79428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([0.99744278]))"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.predict('بچہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "980a66c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Negative',), array([0.99797666]))"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.predict('بچہ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "b9862124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12634, 0.8873674212442615, 0.8873674212442615)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.test('combined_fast_text_r.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "0a604db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.save_model('model_sent_fastText_model2.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "97c26918",
   "metadata": {},
   "outputs": [],
   "source": [
    "model4 = fasttext.train_supervised(input='combined_fast_text_r.csv',  lr=0.5, epoch=10, wordNgrams=2, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "fe451795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5915502841356329"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "17e4ab4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.wordNgrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "4ca46c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model3.wordNgrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "51ea9870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wordNgrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "2c4dfea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12634, 0.9820326104163368, 0.9820326104163368)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.test('combined_fast_text_r.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "5bbe7bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Positive',), array([0.98859251]))"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.predict('اچھا بچہ ہے')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "84d5d080",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__Neutral',), array([1.00000989]))"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model4.predict('چ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "b1b9d1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wordNgrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "7083f1bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model4.save_model('model_sent_fastText_model3.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "e2d7b8ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "اچھا بچہ\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Positive'"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment_fast(s, model4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "bd921a2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "اچھا بچہ\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Negative'"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment_fast(s, model3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a29c7d",
   "metadata": {},
   "source": [
    "## Fast Model Final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d533ac65",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/sent_train_f.csv', lr=0.5, epoch=10, wordNgrams=1, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "20517428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2478, 0.7546408393866021, 0.7546408393866021)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/sent_test_f.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50818851",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/sent_train_f.csv', lr=0.5, epoch=10, wordNgrams=2, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de2e8fee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2478, 0.7776432606941082, 0.7776432606941082)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/sent_test_f.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cbedef6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c42f895b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\umair\\\\Documents\\\\Freelance\\\\python\\\\twitter-20220719T170741Z-001\\\\twitter\\\\Updataed_Data'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94b62d92",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5afb80a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_f1_score(precision, recall):\n",
    "    f1_score = 2 * (precision * recall) / (precision + recall)\n",
    "    return f1_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2a06f4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.777"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_f1_score(0.777,0.777)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6ae5082a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/sent_train_f.csv', lr=0.5, epoch=10, wordNgrams=3, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c1510bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2478, 0.774818401937046, 0.774818401937046)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/sent_test_f.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8430fd2d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ccdc7ddf",
   "metadata": {},
   "source": [
    "## Raw Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1dc1789b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/sent_train_raw.csv', lr=0.5, epoch=10, wordNgrams=1, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "19b170b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2402, 0.7431307243963364, 0.7431307243963364)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/sent_test_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d4d0c329",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/sent_train_raw.csv', lr=0.5, epoch=10, wordNgrams=2, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "219de4d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2402, 0.7643630308076603, 0.7643630308076603)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/sent_test_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae77a817",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/sent_train_raw.csv', lr=0.5, epoch=10, wordNgrams=3, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9f0e8efa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2402, 0.7556203164029975, 0.7556203164029975)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/sent_test_raw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96831f41",
   "metadata": {},
   "source": [
    "## Bullying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8b98b5c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/text_bullying_train_raw.csv', lr=0.5, epoch=10, wordNgrams=1, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b7a6990",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2374, 0.8184498736310025, 0.8184498736310025)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/text_bullying_test_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "619061d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/text_bullying_train_raw.csv', lr=0.5, epoch=10, wordNgrams=2, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bc56bb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2374, 0.8167649536647009, 0.8167649536647009)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/text_bullying_test_raw.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8c3d240e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/text_bullying_train_raw.csv', lr=0.5, epoch=10, wordNgrams=3, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "123687e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2374, 0.8121314237573716, 0.8121314237573716)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/text_bullying_test_raw.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ab66b2",
   "metadata": {},
   "source": [
    "### prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "22d7d569",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/text_bullying_train_prep.csv', lr=0.5, epoch=10, wordNgrams=1, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9d980286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2444, 0.8318330605564648, 0.8318330605564648)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/text_bullying_test_prep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "33a29acc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/text_bullying_train_prep.csv', lr=0.5, epoch=10, wordNgrams=2, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "3bb42413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2444, 0.8420621931260229, 0.8420621931260229)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/text_bullying_test_prep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8c06270c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='Final_clean/text_bullying_train_prep.csv', lr=0.5, epoch=10, wordNgrams=3, dim=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cefce923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2444, 0.8379705400981997, 0.8379705400981997)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(path='Final_clean/text_bullying_test_prep.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38b79b0d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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