sentiment_analysis.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n"
   ]
  },
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   "source": [
    "data = pd.read_csv(\"/kaggle/input/flipkart-product-customer-reviews-dataset/Dataset-SA.csv\")\n",
    "df = pd.DataFrame(data)"
   ]
  },
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205052 entries, 0 to 205051\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   product_name   205052 non-null  object\n",
      " 1   product_price  205052 non-null  object\n",
      " 2   Rate           205052 non-null  object\n",
      " 3   Review         180388 non-null  object\n",
      " 4   Summary        205041 non-null  object\n",
      " 5   Sentiment      205052 non-null  object\n",
      "dtypes: object(6)\n",
      "memory usage: 9.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
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       "      <th></th>\n",
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       "      <th>Rate</th>\n",
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       "      <th>Summary</th>\n",
       "      <th>Sentiment</th>\n",
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       "                                             product_name product_price  \\\n",
       "count                                              205052        205052   \n",
       "unique                                                958           525   \n",
       "top     cello Pack of 18 Opalware Cello Dazzle Lush Fi...          1299   \n",
       "freq                                                 6005          9150   \n",
       "\n",
       "          Rate     Review Summary Sentiment  \n",
       "count   205052     180388  205041    205052  \n",
       "unique       8       1324   92923         3  \n",
       "top          5  wonderful    good  positive  \n",
       "freq    118765       9016   17430    166581  "
      ]
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     "execution_count": 4,
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   "source": [
    "data.describe()"
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       "      <td>Candes 12 L Room/Personal Air Cooler??????(Whi...</td>\n",
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       "                                        product_name product_price Rate  \\\n",
       "0  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    5   \n",
       "1  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    5   \n",
       "2  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    3   \n",
       "3  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    1   \n",
       "4  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    3   \n",
       "\n",
       "            Review                                            Summary  \\\n",
       "0           super!  great cooler excellent air flow and for this p...   \n",
       "1          awesome              best budget 2 fit cooler nice cooling   \n",
       "2             fair  the quality is good but the power of air is de...   \n",
       "3  useless product                  very bad product its a only a fan   \n",
       "4             fair                                      ok ok product   \n",
       "\n",
       "  Sentiment  \n",
       "0  positive  \n",
       "1  positive  \n",
       "2  positive  \n",
       "3  negative  \n",
       "4   neutral  "
      ]
     },
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   ],
   "source": [
    "data.head()"
   ]
  },
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "%matplotlib inline"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sn.countplot(x='Sentiment',data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00848760",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:09.362092Z",
     "iopub.status.busy": "2023-02-25T14:34:09.361621Z",
     "iopub.status.idle": "2023-02-25T14:34:09.367265Z",
     "shell.execute_reply": "2023-02-25T14:34:09.365950Z"
    },
    "id": "HtvzzpsKdHkF",
    "papermill": {
     "duration": 0.024176,
     "end_time": "2023-02-25T14:34:09.369764",
     "exception": false,
     "start_time": "2023-02-25T14:34:09.345588",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#from sklearn.model_selection import train_test_split\n",
    "#train_set , test_set = train_test_split(data , test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9258f827",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:09.401026Z",
     "iopub.status.busy": "2023-02-25T14:34:09.400541Z",
     "iopub.status.idle": "2023-02-25T14:34:09.433492Z",
     "shell.execute_reply": "2023-02-25T14:34:09.432245Z"
    },
    "papermill": {
     "duration": 0.052311,
     "end_time": "2023-02-25T14:34:09.436363",
     "exception": false,
     "start_time": "2023-02-25T14:34:09.384052",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = data[data.Sentiment.isin(['neutral']) == False]\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7fe189ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:09.467414Z",
     "iopub.status.busy": "2023-02-25T14:34:09.466989Z",
     "iopub.status.idle": "2023-02-25T14:34:09.804616Z",
     "shell.execute_reply": "2023-02-25T14:34:09.803309Z"
    },
    "id": "MK7GPfOZdHj5",
    "outputId": "d1cabe4d-f8e8-4c1a-d59c-4bd6bc349dac",
    "papermill": {
     "duration": 0.356086,
     "end_time": "2023-02-25T14:34:09.807223",
     "exception": false,
     "start_time": "2023-02-25T14:34:09.451137",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sn.countplot(x='Sentiment',data=data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dec3ab47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:09.839099Z",
     "iopub.status.busy": "2023-02-25T14:34:09.838036Z",
     "iopub.status.idle": "2023-02-25T14:34:09.856569Z",
     "shell.execute_reply": "2023-02-25T14:34:09.855663Z"
    },
    "id": "-I-GriztdHkJ",
    "outputId": "a9f6d306-62cb-4489-ad9a-17f3b449037f",
    "papermill": {
     "duration": 0.037173,
     "end_time": "2023-02-25T14:34:09.859162",
     "exception": false,
     "start_time": "2023-02-25T14:34:09.821989",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "positive    166581\n",
       "negative     28232\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0298e1bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:09.890584Z",
     "iopub.status.busy": "2023-02-25T14:34:09.889888Z",
     "iopub.status.idle": "2023-02-25T14:34:10.102309Z",
     "shell.execute_reply": "2023-02-25T14:34:10.101084Z"
    },
    "id": "bOz3RZwCdHkL",
    "outputId": "3ca3a56a-2dc8-42f8-f0c5-44b0249de3a8",
    "papermill": {
     "duration": 0.231028,
     "end_time": "2023-02-25T14:34:10.104850",
     "exception": false,
     "start_time": "2023-02-25T14:34:09.873822",
     "status": "completed"
    },
    "tags": []
   },
   "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>product_name</th>\n",
       "      <th>product_price</th>\n",
       "      <th>Rate</th>\n",
       "      <th>Review</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Candes 12 L Room/Personal Air Cooler??????(Whi...</td>\n",
       "      <td>3999</td>\n",
       "      <td>5</td>\n",
       "      <td>super!</td>\n",
       "      <td>great cooler excellent air flow and for this p...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Candes 12 L Room/Personal Air Cooler??????(Whi...</td>\n",
       "      <td>3999</td>\n",
       "      <td>5</td>\n",
       "      <td>awesome</td>\n",
       "      <td>best budget 2 fit cooler nice cooling</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Candes 12 L Room/Personal Air Cooler??????(Whi...</td>\n",
       "      <td>3999</td>\n",
       "      <td>3</td>\n",
       "      <td>fair</td>\n",
       "      <td>the quality is good but the power of air is de...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Candes 12 L Room/Personal Air Cooler??????(Whi...</td>\n",
       "      <td>3999</td>\n",
       "      <td>1</td>\n",
       "      <td>useless product</td>\n",
       "      <td>very bad product its a only a fan</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Candes 12 L Room/Personal Air Cooler??????(Whi...</td>\n",
       "      <td>3999</td>\n",
       "      <td>5</td>\n",
       "      <td>awesome</td>\n",
       "      <td>the cooler is really fantastic and provides go...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        product_name product_price Rate  \\\n",
       "0  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    5   \n",
       "1  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    5   \n",
       "2  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    3   \n",
       "3  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    1   \n",
       "5  Candes 12 L Room/Personal Air Cooler??????(Whi...          3999    5   \n",
       "\n",
       "            Review                                            Summary  \\\n",
       "0           super!  great cooler excellent air flow and for this p...   \n",
       "1          awesome              best budget 2 fit cooler nice cooling   \n",
       "2             fair  the quality is good but the power of air is de...   \n",
       "3  useless product                  very bad product its a only a fan   \n",
       "5          awesome  the cooler is really fantastic and provides go...   \n",
       "\n",
       "  Sentiment  \n",
       "0  positive  \n",
       "1  positive  \n",
       "2  positive  \n",
       "3  negative  \n",
       "5  positive  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(df.mode().iloc[0])\n",
    "df.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "007a7056",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:10.138656Z",
     "iopub.status.busy": "2023-02-25T14:34:10.137585Z",
     "iopub.status.idle": "2023-02-25T14:34:10.214751Z",
     "shell.execute_reply": "2023-02-25T14:34:10.213305Z"
    },
    "id": "m8eHczYjdHkP",
    "outputId": "edf173bb-3f0f-42f3-d430-1cc67d6b562a",
    "papermill": {
     "duration": 0.097383,
     "end_time": "2023-02-25T14:34:10.217365",
     "exception": false,
     "start_time": "2023-02-25T14:34:10.119982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 194813 entries, 0 to 205050\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count   Dtype \n",
      "---  ------         --------------   ----- \n",
      " 0   product_name   194813 non-null  object\n",
      " 1   product_price  194813 non-null  object\n",
      " 2   Rate           194813 non-null  object\n",
      " 3   Review         194813 non-null  object\n",
      " 4   Summary        194813 non-null  object\n",
      " 5   Sentiment      194813 non-null  object\n",
      "dtypes: object(6)\n",
      "memory usage: 14.4+ MB\n"
     ]
    }
   ],
   "source": [
    "data = df\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "43e8d770",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:10.249956Z",
     "iopub.status.busy": "2023-02-25T14:34:10.249003Z",
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     "shell.execute_reply": "2023-02-25T14:34:11.266944Z"
    },
    "id": "-V1-BbjjdHkR",
    "outputId": "800ff802-424d-41fe-e40e-af09f8ab75d3",
    "papermill": {
     "duration": 1.038932,
     "end_time": "2023-02-25T14:34:11.271679",
     "exception": false,
     "start_time": "2023-02-25T14:34:10.232747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of features:  1257\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vectorizer = CountVectorizer()\n",
    "feature_vector = count_vectorizer.fit(data.Review)\n",
    "features = feature_vector.get_feature_names()\n",
    "print(\"Total number of features: \",len(features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "589aa5ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:11.305640Z",
     "iopub.status.busy": "2023-02-25T14:34:11.305181Z",
     "iopub.status.idle": "2023-02-25T14:34:12.157175Z",
     "shell.execute_reply": "2023-02-25T14:34:12.155951Z"
    },
    "id": "iPnviLutdHkT",
    "papermill": {
     "duration": 0.871886,
     "end_time": "2023-02-25T14:34:12.160250",
     "exception": false,
     "start_time": "2023-02-25T14:34:11.288364",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_features = count_vectorizer.transform(data.Review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ce855fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:12.193076Z",
     "iopub.status.busy": "2023-02-25T14:34:12.192454Z",
     "iopub.status.idle": "2023-02-25T14:34:12.199766Z",
     "shell.execute_reply": "2023-02-25T14:34:12.198510Z"
    },
    "id": "txjpqFQVdHkV",
    "outputId": "b4bd8d96-308e-441d-c39a-012f71abce52",
    "papermill": {
     "duration": 0.026231,
     "end_time": "2023-02-25T14:34:12.202322",
     "exception": false,
     "start_time": "2023-02-25T14:34:12.176091",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(194813, 1257)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d6816c01",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:12.235104Z",
     "iopub.status.busy": "2023-02-25T14:34:12.234413Z",
     "iopub.status.idle": "2023-02-25T14:34:12.241941Z",
     "shell.execute_reply": "2023-02-25T14:34:12.240584Z"
    },
    "id": "_DqSBNv7dHkX",
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    "papermill": {
     "duration": 0.026317,
     "end_time": "2023-02-25T14:34:12.244434",
     "exception": false,
     "start_time": "2023-02-25T14:34:12.218117",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "359542"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_features.getnnz()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "823a0c7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:12.277343Z",
     "iopub.status.busy": "2023-02-25T14:34:12.276955Z",
     "iopub.status.idle": "2023-02-25T14:34:12.282989Z",
     "shell.execute_reply": "2023-02-25T14:34:12.281645Z"
    },
    "id": "RI9BIxZadHkY",
    "outputId": "62998cff-9d27-4eb1-a280-7c48bf8913d3",
    "papermill": {
     "duration": 0.025882,
     "end_time": "2023-02-25T14:34:12.285956",
     "exception": false,
     "start_time": "2023-02-25T14:34:12.260074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density of the matrix:  0.1468237857832545\n"
     ]
    }
   ],
   "source": [
    "print(\"Density of the matrix: \",\n",
    "    data_features.getnnz()*100 /  \n",
    "    (data_features.shape[0]* data_features.shape[1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e3667aca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:12.319095Z",
     "iopub.status.busy": "2023-02-25T14:34:12.318673Z",
     "iopub.status.idle": "2023-02-25T14:34:13.294850Z",
     "shell.execute_reply": "2023-02-25T14:34:13.293389Z"
    },
    "id": "nOMJt8zpdHka",
    "papermill": {
     "duration": 0.996212,
     "end_time": "2023-02-25T14:34:13.297904",
     "exception": false,
     "start_time": "2023-02-25T14:34:12.301692",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame(data_features.todense())\n",
    "data_df.columns = features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a954475",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:13.332708Z",
     "iopub.status.busy": "2023-02-25T14:34:13.332291Z",
     "iopub.status.idle": "2023-02-25T14:34:13.347066Z",
     "shell.execute_reply": "2023-02-25T14:34:13.345719Z"
    },
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    "papermill": {
     "duration": 0.036323,
     "end_time": "2023-02-25T14:34:13.350364",
     "exception": false,
     "start_time": "2023-02-25T14:34:13.314041",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "                                        product_name product_price Rate  \\\n",
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       "   Review                                            Summary Sentiment  \n",
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    },
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   },
   "outputs": [
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    "data_df.iloc[:1,150:157]"
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  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4b847b56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:13.434569Z",
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   "outputs": [],
   "source": [
    "features_counts = np.sum(data_features.toarray(), axis = 0)\n",
    "features_counts_df = pd.DataFrame(dict(features = features , counts = features_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f7e73bf1",
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    "execution": {
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    },
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    {
     "data": {
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       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 23,
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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,5))\n",
    "plt.hist(features_counts_df.counts , bins=50 , range = (0 ,5000))\n",
    "plt.xlabel('Frequency of words')\n",
    "plt.ylabel('Density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80ae1d1f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:15.323145Z",
     "iopub.status.busy": "2023-02-25T14:34:15.322696Z",
     "iopub.status.idle": "2023-02-25T14:34:15.334320Z",
     "shell.execute_reply": "2023-02-25T14:34:15.333024Z"
    },
    "id": "tOA5EJTnh7eA",
    "outputId": "3786a7f3-777a-480e-d97d-c79d1fe12d4a",
    "papermill": {
     "duration": 0.03196,
     "end_time": "2023-02-25T14:34:15.337141",
     "exception": false,
     "start_time": "2023-02-25T14:34:15.305181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "680"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(features_counts_df[features_counts_df.counts == 1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d71ca62a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:15.371862Z",
     "iopub.status.busy": "2023-02-25T14:34:15.371366Z",
     "iopub.status.idle": "2023-02-25T14:34:18.415226Z",
     "shell.execute_reply": "2023-02-25T14:34:18.413845Z"
    },
    "id": "ogx4Hj_NiVpk",
    "outputId": "352a33f1-7bb4-4d2f-f85a-3cb68fd3eec0",
    "papermill": {
     "duration": 3.064464,
     "end_time": "2023-02-25T14:34:18.418155",
     "exception": false,
     "start_time": "2023-02-25T14:34:15.353691",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "count_vectorizer = CountVectorizer(max_features=10000)\n",
    "feature_vector = count_vectorizer.fit(data.Review)\n",
    "features = feature_vector.get_feature_names()\n",
    "data_features = count_vectorizer.transform(data.Review)\n",
    "features_counts = np.sum(data_features.toarray() , axis = 0)\n",
    "feature_counts = pd.DataFrame(dict(features = features , counts = features_counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1373c9d1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:18.452548Z",
     "iopub.status.busy": "2023-02-25T14:34:18.452136Z",
     "iopub.status.idle": "2023-02-25T14:34:18.465479Z",
     "shell.execute_reply": "2023-02-25T14:34:18.464341Z"
    },
    "id": "RQRU3cYAjo5t",
    "outputId": "23b05586-2827-497e-dac6-1e51dadd2339",
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       "      <th>counts</th>\n",
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       "      <th>1227</th>\n",
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       "      <td>8652</td>\n",
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       "      <th>1048</th>\n",
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       "      <td>7850</td>\n",
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       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>just</td>\n",
       "      <td>7022</td>\n",
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       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>recommended</td>\n",
       "      <td>6516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>great</td>\n",
       "      <td>5607</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         features  counts\n",
       "1227    wonderful   32043\n",
       "882       product   28478\n",
       "494          good   16158\n",
       "110       awesome   11073\n",
       "773           not   10860\n",
       "892      purchase   10779\n",
       "1105     terrific   10773\n",
       "1110          the   10496\n",
       "737         money    8866\n",
       "1237        worth    8788\n",
       "767          nice    8652\n",
       "1048    specified    7850\n",
       "621          just    7022\n",
       "918   recommended    6516\n",
       "500         great    5607"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_counts.sort_values('counts',ascending = False)[0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd9cd59e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:18.502820Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from nltk.corpus import stopwords\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "\n",
    "english_stop_words = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "187e9744",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:19.457714Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             super!\n",
       "1            awesome\n",
       "2               fair\n",
       "3    useless product\n",
       "5            awesome\n",
       "Name: Review, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Review'][0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "08ef9f12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:19.502475Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "count_vectorizer = CountVectorizer(stop_words=english_stop_words , max_features = 10000)\n",
    "feature_vector = count_vectorizer.fit(data.Review)\n",
    "data_features = count_vectorizer.transform(data.Review)\n",
    "features = feature_vector.get_feature_names()\n",
    "features_counts = np.sum(data_features.toarray() , axis=0)\n",
    "feature_counts = pd.DataFrame(dict(features = features , counts = features_counts))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b0239ee2",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2023-02-25T14:34:22.602153Z",
     "iopub.status.idle": "2023-02-25T14:34:22.616037Z",
     "shell.execute_reply": "2023-02-25T14:34:22.615060Z"
    },
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    "outputId": "14dff0ad-8527-42ed-a87b-61cd08ba1f86",
    "papermill": {
     "duration": 0.034393,
     "end_time": "2023-02-25T14:34:22.618283",
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     "start_time": "2023-02-25T14:34:22.583890",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<div>\n",
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       "      <td>5557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>brilliant</td>\n",
       "      <td>5538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>perfect</td>\n",
       "      <td>5511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         features  counts\n",
       "1145    wonderful   32043\n",
       "824       product   28478\n",
       "461          good   16158\n",
       "99        awesome   11073\n",
       "834      purchase   10779\n",
       "1042     terrific   10773\n",
       "694         money    8866\n",
       "1155        worth    8788\n",
       "721          nice    8652\n",
       "986     specified    7850\n",
       "860   recommended    6516\n",
       "467         great    5607\n",
       "131          best    5557\n",
       "155     brilliant    5538\n",
       "778       perfect    5511"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_counts.sort_values(\"counts\",ascending = False)[0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6b9bd263",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:22.654357Z",
     "iopub.status.busy": "2023-02-25T14:34:22.653950Z",
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     "shell.execute_reply": "2023-02-25T14:34:22.659317Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.stem.snowball import PorterStemmer\n",
    "stemmer = PorterStemmer()\n",
    "analyzer = CountVectorizer().build_analyzer()\n",
    "def stemmed_words(doc):\n",
    "  stemmed_words = [stemmer.stem(w) for w in analyzer(doc)]\n",
    "  non_stop_words = [word for word in stemmed_words ]\n",
    "  return non_stop_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "44b3f215",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:22.699709Z",
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     "shell.execute_reply": "2023-02-25T14:34:48.228158Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "count_vectorizer = CountVectorizer(analyzer = stemmed_words , max_features = 10000)\n",
    "feature_vector = count_vectorizer.fit(data.Review)\n",
    "data_features = count_vectorizer.transform(data.Review)\n",
    "features = feature_vector.get_feature_names()\n",
    "features_counts = np.sum(data_features.toarray() , axis=0)\n",
    "feature_counts = pd.DataFrame(dict(features = features , counts = features_counts))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "dfc8e95d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:48.268659Z",
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     "iopub.status.idle": "2023-02-25T14:34:48.282303Z",
     "shell.execute_reply": "2023-02-25T14:34:48.280882Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       features  counts\n",
       "1107     wonder   32045\n",
       "798     product   28479\n",
       "450        good   16158\n",
       "107      awesom   11073\n",
       "699         not   10860\n",
       "806     purchas   10780\n",
       "996      terrif   10773\n",
       "1000        the   10496\n",
       "665       money    8866\n",
       "1114      worth    8789\n",
       "693        nice    8652\n",
       "944     specifi    7850\n",
       "562        just    7022\n",
       "828   recommend    6518\n",
       "456       great    5608"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_counts.sort_values(\"counts\",ascending = False)[0:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "72e70b7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:48.321642Z",
     "iopub.status.busy": "2023-02-25T14:34:48.321199Z",
     "iopub.status.idle": "2023-02-25T14:34:49.049427Z",
     "shell.execute_reply": "2023-02-25T14:34:49.048061Z"
    },
    "id": "eD15OzHxsQgi",
    "papermill": {
     "duration": 0.749889,
     "end_time": "2023-02-25T14:34:49.052420",
     "exception": false,
     "start_time": "2023-02-25T14:34:48.302531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data_df = pd.DataFrame(data_features.todense())\n",
    "data_df.columns = features\n",
    "data_df['Sentiment'] = data.Sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "cc913c0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:49.088556Z",
     "iopub.status.busy": "2023-02-25T14:34:49.088142Z",
     "iopub.status.idle": "2023-02-25T14:34:49.131019Z",
     "shell.execute_reply": "2023-02-25T14:34:49.129981Z"
    },
    "id": "GfEK4HKXsq-9",
    "papermill": {
     "duration": 0.064115,
     "end_time": "2023-02-25T14:34:49.133839",
     "exception": false,
     "start_time": "2023-02-25T14:34:49.069724",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train , X_test , Y_train , Y_test = train_test_split(data_features,data.Sentiment,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "629691a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:49.170520Z",
     "iopub.status.busy": "2023-02-25T14:34:49.170118Z",
     "iopub.status.idle": "2023-02-25T14:34:55.199570Z",
     "shell.execute_reply": "2023-02-25T14:34:55.198311Z"
    },
    "id": "wvZ8ceabuKaU",
    "papermill": {
     "duration": 6.051182,
     "end_time": "2023-02-25T14:34:55.202263",
     "exception": false,
     "start_time": "2023-02-25T14:34:49.151081",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "nb_clf1 = BernoulliNB()\n",
    "nb_clf1.fit(X_train.toarray(),Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "26ed7000",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:55.238670Z",
     "iopub.status.busy": "2023-02-25T14:34:55.238216Z",
     "iopub.status.idle": "2023-02-25T14:34:55.955264Z",
     "shell.execute_reply": "2023-02-25T14:34:55.953114Z"
    },
    "id": "3X2c8Gt6ugA0",
    "papermill": {
     "duration": 0.741217,
     "end_time": "2023-02-25T14:34:55.960734",
     "exception": false,
     "start_time": "2023-02-25T14:34:55.219517",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_predicted = nb_clf1.predict(X_test.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bd9d8e88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:56.045691Z",
     "iopub.status.busy": "2023-02-25T14:34:56.045272Z",
     "iopub.status.idle": "2023-02-25T14:34:57.452497Z",
     "shell.execute_reply": "2023-02-25T14:34:57.450849Z"
    },
    "papermill": {
     "duration": 1.449518,
     "end_time": "2023-02-25T14:34:57.455823",
     "exception": false,
     "start_time": "2023-02-25T14:34:56.006305",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.91      0.66      0.76      5690\n",
      "    positive       0.94      0.99      0.97     33273\n",
      "\n",
      "    accuracy                           0.94     38963\n",
      "   macro avg       0.93      0.82      0.86     38963\n",
      "weighted avg       0.94      0.94      0.94     38963\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(Y_test,test_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db6aa90b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:57.494867Z",
     "iopub.status.busy": "2023-02-25T14:34:57.494125Z",
     "iopub.status.idle": "2023-02-25T14:34:57.942869Z",
     "shell.execute_reply": "2023-02-25T14:34:57.941590Z"
    },
    "papermill": {
     "duration": 0.470322,
     "end_time": "2023-02-25T14:34:57.945388",
     "exception": false,
     "start_time": "2023-02-25T14:34:57.475066",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k9pd5bW2tqquqFR0T5RZ7y5B4bd6xSlmrFurxpx5Q8+Z+xrUDB0q1edNWDb1tkJo185WXl5fuvGuoCguL9X1u3nGf3faC1goNC9HSL7KMc1VV1Vrx9WpdFX2lJOmKKyLl4+OjL38VU1BQpI0btujq/8SgEblqLTvS0tKMeQ8/H2lpaacegsulCRMm6JprrlFkZKRxvn///po3b56++OILPf/881qzZo2uv/564/d6QUGBfHx8FBgY6Ha/0NBQFRQUGDEhIXXb1iEhIW4xoaHu7bvAwED5+PgYMafS4CWse/fu1ezZs7VixQoVFBTIZrMpNDRUXbt21ahRo9S6deuG3hL1cEmHi/Teojdkt/vop4pKjR12v7b9sKNO3KChA1RRXqElC7+sc+22u25RyqPj5OfXTNt+2KG7/zRG1dVHj/u81hecpz/fM1TPPjbzhGNq+Z82zf7iA27n9xcfUHjrMCOmylmlQ2WH68SY2zyAlbb8sF27d+3VI49N1MTkR/VTRaXuHXuXQsNCFBrW0oj7v3/+W7t27VVRYbE6dLxEjzw2QX+IvFS3DLrLiLn5xrv0znsva+e+71RbW6viov0aOvieOr+vfxYSEixJKi7a73a+uGi/zm9z7KfCkNBgOZ1VKjt4yD2muMR4PX4bUlNTNWHCBLdzdrv9lK8bO3as1q5dq6ysLLfzP7dAJCkyMlKdO3dW27ZttXDhQg0ePPiE93O5XLLZbMbXv/71fxNzMg1KMrKystS/f3+1bt1asbGxio2NlcvlUlFRkT788EO9+OKLWrx4sbp163bS+zidzjqlolpXrc6xUVg5kR1bd+mm629XQIC/YuOu1zMvTlbioJF1Eo2bb4vXJ/MzVOWsO/v33/+3WCuWrlLL0GDdPfrPmvlamm6Lu6dObEhosF5L/5syPv5M/zfvxBWRn9WZ1mOznXpCtc1W93WAhY4ePaq7Esdp5qwp2rb7Gx09elTLlq5Q5pJlbnFz3/qn8etNG7do+7ad+nz5Al12eUet/X6DJGnq9MdUXHJAcX0TdOTIEf35zj/p3X++qj49blZhYfEJx2D+PW6rx+97m/izcUawcMdPu91er6Ti18aNG6ePP/5Yy5cv1/nnn3/S2FatWqlt27basmWLJCksLExVVVUqLS11q2YUFRWpa9euRkxhYd1KdnFxsVG9CAsL06pVq9yul5aWqrq6uk6F40Qa9K/6fffdp3vuuUcbNmzQzJkzlZqaqoceekgzZ87U+vXrNXz4cCUnJ5/yPscrHR34Kb8hQ/ndqa4+qt079irv+42a/vRL2rRhi+4YcatbTFT0Fbqw/QX61wkSg/LDFdq1Y4++yf5Ofxn+gNpdfIH63NDDLSYkNFhvLXhFud+s06MTp5x0TD//lGauSLQIDtT+4v1GjI/dRwEO/+PEuFdAAKt9n7tePa+5Ue3Ov1J/aN9NQwffo6Cgc7V7196TvqaqqkoXXnSBJOna7jGK7ddTSXcla/Wqb7X2+w2aNOFxHTlyREMTbjruPX6eDBoS6l6RCG4ZZPy5KSoskd3uI8e5AaaYFioudq+A4H/PVVtr2dGg57pcGjt2rD744AN98cUXateu3Slfs3//fu3Zs0etWh2bYxcVFSVvb29lZmYaMfn5+crLyzOSjJiYGJWVlWn16tVGzKpVq1RWVuYWk5eXp/z8X/59XrJkiex2u6Ki3FuOJ9KgJCMvL0+jRo064fWRI0cqL+/4PcpfS01NVVlZmdsR1KxVQ4byu2eTzeg5/+yW229UXu4GbV6/pX73sLnfIySspd7+8BVtWLtJD41/4pQ/Te3d9aOKCkvUtccvE4e8vZvoqq5X6rs1ayVJ67/fqKqqanXt/ktMy5AWan/pRUYM4GmHD5Vr//5SXXhRW13xx0i3FShml3ZoLx8fHxUWHJtI18y3qSTJZfrJtra2Vuecc/y/Qnft3KPCgiL16PlLVdfb21tdu12tNau+lSTl5uapqqrKLSY0tKU6dGyv1f+Jwe/PmDFj9M477+jdd9+Vv7+/CgoKVFBQoMrKY6uhysvLlZKSopUrV2rnzp1aunSpBg4cqODgYN1007Gk1+FwaPjw4Zo4caI+//xzfffdd/rzn/+sTp06qXfvY8u8O3TooH79+ikpKUnZ2dnKzs5WUlKS4uLiFBERIUmKjY1Vx44dlZiYqO+++06ff/65UlJSlJSUVK+VJVID2yWtWrXSihUrjAGYrVy50sikTuZ4pSNaJSd230OjtfzzFSrYVyi/5s10w6BYXd3tSiXdOt6I8Wvup74De+nZyTPrvP78tufphhv76Oul2Tqwv1ShrUJ0z7g75DxyRMs+/1rSsZ+43v7wFeXvLdSzk19QUPAvJbaSX/WVF339L01/+iV9tmipJOntv7+nkX+5S7u279Gu7Xs08i/DdKTyiD6Z/6mkY9WT+e9+pAceT9bB0jKVlZZp0uRk/bBxm1Ys+yWDBk6Hn18ztbvwl30v2l5wviI7dVBp6UH9uDdf8YP6aX/JAe3dm6+OHS/R088+rEWffKalXxz7fX9Bu9a6ZUi8PluyTPv3lyri0ov1xNMPam3ueq3KPvYP/ZrVuTp48JBmvfKspj37ko4cOaLEO4eoTdvzlfnpUuPZK7/J0JOTn9eiT4799PjKy28peeIobdu2S9u37dR9KaNUWVmp+f/6RNKxxGfe2/+nJ55+UAcOHNTB0oN6/KkHtWH9D1r25Yr/0XcQJ9RIH5A2e/ZsSVKPHj3czr/55psaNmyYvLy8tG7dOr399ts6ePCgWrVqpZ49e+r999+Xv/8vFeMZM2aoSZMmGjJkiCorK9WrVy/NmTNHXl5eRsy8efM0fvx4YxVKfHy8Zs2aZVz38vLSwoULNXr0aHXr1k2+vr5KSEjQtGnT6v1+GpRkpKSkaNSoUcrJyVGfPn0UGhoqm82mgoICZWZm6h//+IdmzpzZkFuiHlq0DNJzLz2ulqHBOnyoXJs3blXSrePd/pEecFOsbDabFn7waZ3XVx1xKqrLFbpj5K0KcARof/EBfZP9nW4bcI8OlJRKkrr16KILLmyjCy5so+Vr3Td1+fUeJhe2v0D+/s2Nr//x4ttq2tSuR599QA6Hv9Z+u17Dh4wz9siQpLS/zlDN0RrNfG2K7E2bKvurNbr3z4+zDwD+a1f8MVIfLXrH+PqptIckSe/N+0Dj7n1QoWEt9eSUVLUMaaHCgmK9n/6hnn/2ZSO+qqpa13WP0Yh775Cfn59+/DFfmZ8u1dRnZhm/Pw8cKNXQwcP10KP3acEnb8m7ibc2bdqixNtGa33eJuNe7S+5UAEBv/zZeHHma/L1baqp0x+T41yHvv3me90y6G5jjwxJeiR1io7W1Oj1t2aqadOm+mrZSo0d+gB/Ns4Ersb5b3CqCrKvr68+/bTu3/NmTZs21YsvvqgXX3zxhDFBQUF65513Tnhdktq0aaNPPvnklM87kQZvxvX+++9rxowZysnJMXYO8/LyUlRUlCZMmKAhQ4ac1kDYjAuoi824gOPz9GZcFU/cbtm9/B6dZ9m9zjYNXsI6dOhQDR06VNXV1SopOTa5KTg4WN7e3pYPDgAAnL1O+6Pevb296zX/AgCAsw4tK0ucdpIBAMBvViNN/PytYUkHAADwCCoZAACYNdLqkt8akgwAAMxol1iCdgkAAPAIKhkAAJg09DNHcHwkGQAAmNEusQTtEgAA4BFUMgAAMKOSYQmSDAAAzFjCagmSDAAAzKhkWII5GQAAwCOoZAAAYOKikmEJkgwAAMxIMixBuwQAAHgElQwAAMzY8dMSJBkAAJjRLrEE7RIAAOARVDIAADCjkmEJkgwAAExcLpIMK9AuAQAAHkElAwAAM9olliDJAADAjCTDEiQZAACYsK24NZiTAQAAPIJKBgAAZlQyLEGSAQCAGbuKW4J2CQAA8AgqGQAAmDDx0xokGQAAmJFkWIJ2CQAA8AgqGQAAmDHx0xIkGQAAmDAnwxq0SwAAgEdQyQAAwIx2iSVIMgAAMKFdYg2SDAAAzKhkWII5GQAAwCOoZAAAYOKikmEJkgwAAMxIMixBuwQAAHgElQwAAExol1iDJAMAADOSDEvQLgEA4AyRlpamq666Sv7+/goJCdGgQYO0efNmtxiXy6XJkycrPDxcvr6+6tGjh9avX+8W43Q6NW7cOAUHB8vPz0/x8fHau3evW0xpaakSExPlcDjkcDiUmJiogwcPusXs3r1bAwcOlJ+fn4KDgzV+/HhVVVXV+/2QZAAAYOKqte5oiGXLlmnMmDHKzs5WZmamjh49qtjYWFVUVBgxzz33nKZPn65Zs2ZpzZo1CgsLU58+fXT48GEjJjk5WQsWLFB6erqysrJUXl6uuLg41dTUGDEJCQnKzc1VRkaGMjIylJubq8TERON6TU2NBgwYoIqKCmVlZSk9PV3z58/XxIkT6/1+bC6X64zY1uzSkKsaewjAGafkSFljDwE4I5Uc+sGj9y/q1d2ye4V8vuy0X1tcXKyQkBAtW7ZM1113nVwul8LDw5WcnKwHHnhA0rGqRWhoqJ599lmNHDlSZWVlatmypebOnauhQ4dKkvbt26fWrVtr0aJF6tu3rzZu3KiOHTsqOztb0dHRkqTs7GzFxMRo06ZNioiI0OLFixUXF6c9e/YoPDxckpSenq5hw4apqKhIAQEBpxw/lQwAAEysrGQ4nU4dOnTI7XA6nfUaR1nZsR80goKCJEk7duxQQUGBYmNjjRi73a7u3btrxYoVkqScnBxVV1e7xYSHhysyMtKIWblypRwOh5FgSFKXLl3kcDjcYiIjI40EQ5L69u0rp9OpnJyceo2fJAMAAA9KS0sz5j38fKSlpZ3ydS6XSxMmTNA111yjyMhISVJBQYEkKTQ01C02NDTUuFZQUCAfHx8FBgaeNCYkJKTOM0NCQtxizM8JDAyUj4+PEXMqrC4BAMDMZbPsVqmpqZowYYLbObvdfsrXjR07VmvXrlVWVladazab+/hcLledc2bmmOPFn07MyVDJAADAxMp2id1uV0BAgNtxqiRj3Lhx+vjjj/Xll1/q/PPPN86HhYVJUp1KQlFRkVF1CAsLU1VVlUpLS08aU1hYWOe5xcXFbjHm55SWlqq6urpOheNESDIAADhDuFwujR07Vh988IG++OILtWvXzu16u3btFBYWpszMTONcVVWVli1bpq5du0qSoqKi5O3t7RaTn5+vvLw8IyYmJkZlZWVavXq1EbNq1SqVlZW5xeTl5Sk/P9+IWbJkiex2u6Kiour1fmiXAABg4qq1rl3SEGPGjNG7776rjz76SP7+/kYlweFwyNfXVzabTcnJyZoyZYrat2+v9u3ba8qUKWrWrJkSEhKM2OHDh2vixIlq0aKFgoKClJKSok6dOql3796SpA4dOqhfv35KSkrSq6++KkkaMWKE4uLiFBERIUmKjY1Vx44dlZiYqKlTp+rAgQNKSUlRUlJSvVaWSCQZAADU0Vjbis+ePVuS1KNHD7fzb775poYNGyZJmjRpkiorKzV69GiVlpYqOjpaS5Yskb+/vxE/Y8YMNWnSREOGDFFlZaV69eqlOXPmyMvLy4iZN2+exo8fb6xCiY+P16xZs4zrXl5eWrhwoUaPHq1u3brJ19dXCQkJmjZtWr3fD/tkAGcw9skAjs/T+2Ts69rTsnuFr/jSsnudbahkAABg4rJwdcnvGUkGAAAmfAqrNVhdAgAAPIJKBgAAJo21uuS3hiQDAACTM2NJxNmPJAMAABMqGdZgTgYAAPAIKhkAAJhQybAGSQYAACbMybAG7RIAAOARVDIAADChXWINkgwAAEzYVtwatEsAAIBHUMkAAMCEzy6xBkkGAAAmtbRLLEG7BAAAeASVDAAATJj4aQ2SDAAATFjCag2SDAAATNjx0xrMyQAAAB5BJQMAABPaJdYgyQAAwIQlrNagXQIAADyCSgYAACYsYbUGSQYAACasLrEG7RIAAOARVDIAADBh4qc1SDIAADBhToY1aJcAAACPoJIBAIAJEz+tQZIBAIAJczKsccYkGVsP7mvsIQBnnMp9XzX2EIDfJeZkWIM5GQAAwCPOmEoGAABnCtol1iDJAADAhHmf1qBdAgAAPIJKBgAAJrRLrEGSAQCACatLrEG7BAAAeASVDAAATGobewC/ESQZAACYuES7xAq0SwAAgEdQyQAAwKSWjTIsQZIBAIBJLe0SS5BkAABgwpwMazAnAwAAeARJBgAAJrUWHg2xfPlyDRw4UOHh4bLZbPrwww/drg8bNkw2m83t6NKli1uM0+nUuHHjFBwcLD8/P8XHx2vv3r1uMaWlpUpMTJTD4ZDD4VBiYqIOHjzoFrN7924NHDhQfn5+Cg4O1vjx41VVVdWg90OSAQCAiUs2y46GqKio0OWXX65Zs2adMKZfv37Kz883jkWLFrldT05O1oIFC5Senq6srCyVl5crLi5ONTU1RkxCQoJyc3OVkZGhjIwM5ebmKjEx0bheU1OjAQMGqKKiQllZWUpPT9f8+fM1ceLEBr0f5mQAAHCG6N+/v/r373/SGLvdrrCwsONeKysr0+uvv665c+eqd+/ekqR33nlHrVu31meffaa+fftq48aNysjIUHZ2tqKjoyVJr732mmJiYrR582ZFRERoyZIl2rBhg/bs2aPw8HBJ0vPPP69hw4bp6aefVkBAQL3eD5UMAABMrGyXOJ1OHTp0yO1wOp2nPbalS5cqJCREl1xyiZKSklRUVGRcy8nJUXV1tWJjY41z4eHhioyM1IoVKyRJK1eulMPhMBIMSerSpYscDodbTGRkpJFgSFLfvn3ldDqVk5NT77GSZAAAYGJlkpGWlmbMffj5SEtLO61x9e/fX/PmzdMXX3yh559/XmvWrNH1119vJC0FBQXy8fFRYGCg2+tCQ0NVUFBgxISEhNS5d0hIiFtMaGio2/XAwED5+PgYMfVBuwQAAA9KTU3VhAkT3M7Z7fbTutfQoUONX0dGRqpz585q27atFi5cqMGDB5/wdS6XSzbbL/NDfv3r/ybmVKhkAABgYuXET7vdroCAALfjdJMMs1atWqlt27basmWLJCksLExVVVUqLS11iysqKjIqE2FhYSosLKxzr+LiYrcYc8WitLRU1dXVdSocJ0OSAQCASa3NusOT9u/frz179qhVq1aSpKioKHl7eyszM9OIyc/PV15enrp27SpJiomJUVlZmVavXm3ErFq1SmVlZW4xeXl5ys/PN2KWLFkiu92uqKioeo+PdgkAAGeI8vJybd261fh6x44dys3NVVBQkIKCgjR58mTdfPPNatWqlXbu3KmHHnpIwcHBuummmyRJDodDw4cP18SJE9WiRQsFBQUpJSVFnTp1MlabdOjQQf369VNSUpJeffVVSdKIESMUFxeniIgISVJsbKw6duyoxMRETZ06VQcOHFBKSoqSkpLqvbJEIskAAKCOxvrskm+++UY9e/Y0vv55Lsedd96p2bNna926dXr77bd18OBBtWrVSj179tT7778vf39/4zUzZsxQkyZNNGTIEFVWVqpXr16aM2eOvLy8jJh58+Zp/PjxxiqU+Ph4t705vLy8tHDhQo0ePVrdunWTr6+vEhISNG3atAa9H5vL5TojPmuuic95jT0E4IxTue+rxh4CcEbyDr7Qo/f/MCzBsnsNKnjXsnudbahkAABg0tDtwHF8TPwEAAAeQSUDAACT2gbsBYETI8kAAMDkjJis+BtAuwQAAHgElQwAAEyY+GkNkgwAAEw8vVPn7wXtEgAA4BFUMgAAMGmsHT9/a0gyAAAwYXWJNWiXAAAAj6CSAQCACRM/rUGSAQCACUtYrUGSAQCACXMyrMGcDAAA4BFUMgAAMGFOhjVIMgAAMGFOhjVolwAAAI+gkgEAgAmVDGuQZAAAYOJiToYlaJcAAACPoJIBAIAJ7RJrkGQAAGBCkmEN2iUAAMAjqGQAAGDCtuLWIMkAAMCEHT+tQZIBAIAJczKswZwMAADgEVQyAAAwoZJhDZIMAABMmPhpDdolAADAI6hkAABgwuoSa5BkAABgwpwMa9AuAQAAHkElAwAAEyZ+WoMkAwAAk1rSDEvQLgEAAB5BJQMAABMmflqDJAMAABOaJdYgyQAAwIRKhjWYkwEAADyCSgYAACbs+GkNkgwAAExYwmoN2iUAAMAjqGQAAGBCHcMaJBkAAJiwusQatEsAADhDLF++XAMHDlR4eLhsNps+/PBDt+sul0uTJ09WeHi4fH191aNHD61fv94txul0aty4cQoODpafn5/i4+O1d+9et5jS0lIlJibK4XDI4XAoMTFRBw8edIvZvXu3Bg4cKD8/PwUHB2v8+PGqqqpq0PshyQAAwKRWLsuOhqioqNDll1+uWbNmHff6c889p+nTp2vWrFlas2aNwsLC1KdPHx0+fNiISU5O1oIFC5Senq6srCyVl5crLi5ONTU1RkxCQoJyc3OVkZGhjIwM5ebmKjEx0bheU1OjAQMGqKKiQllZWUpPT9f8+fM1ceLEBr0fm8vlOiNaT018zmvsIQBnnMp9XzX2EIAzknfwhR69/6QLbrPsXs/tfO+0Xmez2bRgwQINGjRI0rEqRnh4uJKTk/XAAw9IOla1CA0N1bPPPquRI0eqrKxMLVu21Ny5czV06FBJ0r59+9S6dWstWrRIffv21caNG9WxY0dlZ2crOjpakpSdna2YmBht2rRJERERWrx4seLi4rRnzx6Fh4dLktLT0zVs2DAVFRUpICCgXu+BSgYAAB7kdDp16NAht8PpdDb4Pjt27FBBQYFiY2ONc3a7Xd27d9eKFSskSTk5OaqurnaLCQ8PV2RkpBGzcuVKORwOI8GQpC5dusjhcLjFREZGGgmGJPXt21dOp1M5OTn1HjNJBgAAJrUWHmlpacbch5+PtLS0Bo+poKBAkhQaGup2PjQ01LhWUFAgHx8fBQYGnjQmJCSkzv1DQkLcYszPCQwMlI+PjxFTH6wuAQDAxMrNuFJTUzVhwgS3c3a7/bTvZ7O5b0fqcrnqnDMzxxwv/nRiToVKBgAAJi4LD7vdroCAALfjdJKMsLAwSapTSSgqKjKqDmFhYaqqqlJpaelJYwoLC+vcv7i42C3G/JzS0lJVV1fXqXCcDEkGAABngXbt2iksLEyZmZnGuaqqKi1btkxdu3aVJEVFRcnb29stJj8/X3l5eUZMTEyMysrKtHr1aiNm1apVKisrc4vJy8tTfn6+EbNkyRLZ7XZFRUXVe8y0SwAAMGmszbjKy8u1detW4+sdO3YoNzdXQUFBatOmjZKTkzVlyhS1b99e7du315QpU9SsWTMlJCRIkhwOh4YPH66JEyeqRYsWCgoKUkpKijp16qTevXtLkjp06KB+/fopKSlJr776qiRpxIgRiouLU0REhCQpNjZWHTt2VGJioqZOnaoDBw4oJSVFSUlJ9V5ZIpFkAABQh6uRNhb/5ptv1LNnT+Prn+dy3HnnnZozZ44mTZqkyspKjR49WqWlpYqOjtaSJUvk7+9vvGbGjBlq0qSJhgwZosrKSvXq1Utz5syRl5eXETNv3jyNHz/eWIUSHx/vtjeHl5eXFi5cqNGjR6tbt27y9fVVQkKCpk2b1qD3wz4ZwBmMfTKA4/P0PhnjLxhq2b3+tvN9y+51tqGSAQCACZ9dYg2SDAAATKxcwvp7xuoSAADgEVQyAAAwoY5hDSoZZ6mRI+7QtzmZOlCySQdKNilr+cfq1/eXGclHq3487jFxwigj5uWXntXmjV/rcNlW5f+4Vh/Mf0MRERed8tmjRt6pLZtXqvzQNq3KXqxrul1dJ+bRv07Q7p05Oly2VZ9n/ksdO15izRvH71b6gk900x33KrrPYEX3GazbR9ynr1aukSRVHz2q6S+/rpsS79VVvQapZ/ztSn1ymoqK97vdY/fefRqf+oSuHTBU0X0Ga+Jfp6jkgPumRWWHDuvBJ6aqS+zN6hJ7sx58YqoOHS53i8kvKNKYSY/pql6DdM0NQzVlxmxVV1efdPxVVVWaMv1lXXPDUF3Va5DGTpqsgqLiBj8b/xuN9SmsvzUkGWepH3/M18MPpyk65gZFx9ygL5d+rQ/mv2H8Y35e6yvcjuH33Kfa2lp9sGCRcY9vv12re5ImKPKyHrphQIJsNpsWL3xP55xz4t8Wf/pTvKY/P1lpz/xNna/uq6ys1frk3++odetfPkTn/pTRSv7LCI1PfkRdug5QQWGxMha9p+bN/Tz3DcFvXljLYN036i69//rf9P7rf9PVUZdr3INPaOv2XTpyxKkNm7dp5LDb9M83ZmnmlEe0a/dejX3gceP1P1Ue0Yj7HpZNNr3+t2c095XnVV19VGMnTVZt7S/T/B54/Dlt3rJdr0x/Sq9Mf0qbt2xX6pNTjes1NTUaff9jqqx06u3Z0zT18Qf12dKvNfXF1046/mdeeFWfL1+hqY8/qLdnT9NPlUc05v7Jbh+/fapnA2cblrD+hhQV5OmBB5/Sm3PS61yb/3+vy795c8X2O/GyrE6dOui7nM90yaVdtX37ruPGrMj6t779Lk9jx6Ua59atXaqPP87Qw488I0nas+tb/e3Ff2jqtJclST4+Ptq3N1epD03Ra/945795i787LGE9ua79/qSJY+7RzQP71rm2buNm3XZPsjLnv6VWYSH6elWO7k15VCsy/qnmfscS3rJDh9Wt/xC9NnOKYq76o7bt3K0bbx+pd/8+Q5f94VJJ0vd5G3X7yAn697uvqV3b8/XVyjUaM2myPvvgbYW0bCFJWvTZUj3y9HQt/+Q9496/dri8QtcOuFVpf01R/97dJUlFxfvVe/Admj3tCXWLjqrXs/ELTy9hTbrgT5bd67Wd/7LsXmcbKhm/Aeecc46GDImXn18zZa+q+xG8ISHBuqF/L70x570T3qNZM18Nu2Ootm/fpT179h03xtvbW1deeZkyP1vmdj4zc5liunSWJLVr10atWoW6xVRVVWn5V9mKiel8Om8PqKOmpkaLPluqyiNHdEXkpceNKS//STabTf7+x/7Rr66uls0m+Xh7GzF2u4/OOeccfbt2vaRj/6j7N/cz/pGXpMsjO8i/uZ9y8zYYMRdf2NZIMCSp29VRqqqq1oZNv+zU+GsbNm/R0aNH1fXqK41zIS1b6OIL2+q7db/c91TPxv+Oy8L//Z4x8fMsFhl5qbKWf6ymTe0qL6/QLX+6Rxs3bqkTd0fin3T4cLkWLFhc59qokXfqmbSH1by5nzZu2qJ+N9x2wt5ycHCQmjRpoqLCErfzRUUlCg079rHBYaHH/r/QFFNYWKy2bfhJDP+dH7bt0O0jJ6iqqkrNfH31wpS/6qJ2bevEOZ1VmjH7Td3Qp4dRWbjsD5fKt2lTTX/5Df1l1DC5XNKMl99QbW2tSvYfkCSV7C9VUOC5de4XFHiuSvYfm7tRcqBULUwxjgB/eXs3qTO/42cl+0vl7d1EjgB/t/MtAs/V/v+8pj7Pxv8O+2RYw/JKxp49e3T33XefNMbpdOrQoUNuxxnStTmrbN68TVFXxarbNQP16t/f1huvz1SHDu3rxA0bdqvefW+BnE5nnWvvvveBOl/dVz2vH6ytW3fovXdfOeWnA5r/W9lstjrn6hMDNFS7Nudr/pyXNO/VGRoyaIAefvp5bdvh3tqrPnpU9z/2jFyuWv01ZYxxPijwXD3/5ENa+vUqXd17sGL63qzDFRXqGHGx2zyk432Idf0+AvsELz6JY38kfnXfejwbOJtYnmQcOHBAb7311klj0tLS5HA43A5X7WGrh/KbV11drW3bdirn27V6+JFntHbtBo0be49bzDXdrtalERfrjTeP3yo5dOiwtm7doa+yVmnI0BG6NOJiDRrU77ixJSUHdPToUYWGtXQ737JlCxUVHpslX1BYJEkKM8WEhASrsMi9ugE0lLe3t9qcH67IDpfovnvvUsTFF+qdf31kXK8+elQT/zpFe/ML9NrMKXXmR3SLjlLGv97U8k/e01cL39czj96vwuL9Oq/VsY/QDm4RqP2lB+s8t/RgmVoEnXssJijwuCtSjh49quDAwOOOO7hFoKqrj6rskPvfcwcOHvzlvvV4Nv53aJdYo8Htko8//vik17dv337Ke6Smphof+vKzwBbH76ui/mw2m+x2H7dzd911m77J+V5r19avp2uz2WT3OX4lo7q6Wt9+u1a9e12njz7KMM737n2d/v3vTyVJO3bsVn5+oXr3uk65ucf63N7e3rru2i5KfWjK6bwt4IRcLpeqqo61935OMHbv2ac3XnxG5zpO/EmRgec6JEmrcnJ1oPSgel7TRdKxORCHyyu0bsNmdep47NMo167fpMPlFboisqMR8/e331dxyQG1DA6SJK1Y/a18fLzV8dKLj/u8jhHt1aRJE61c85369bpOklRcckBbt+/SxNHD6/1s/O/QLrFGg5OMQYMGnbL0farSnt1ur1OSpxzYME89+aAyMr7Qnr375O/fXEOH3Kju3WM0IO52I8bfv7luuTlO9096os7r27VroyF/ildm5jIVl+zXeeGtdP/9o1VZeUSLMz434pZkvK8PP1qsl2fPkSTNeOE1vfXmC8rJ+V7Zq3KUNPzPatP6PL3697nGa/724j/04APjtGXrDm3dukMPPjBOP/1UqffSF3juG4LfvJmvzNG1XTorLLSlKn76SYs/W6Y1363TK88/qaNHazTh4ae14Yeteum5x93mWRybL3FssueChUt0YdvWCjzXoe/Xb9IzM1/RHUNvMlZuXHRBG13TpbMee/YFPXb/OEnS5Of+pu7drjZiul59pS66oI1Sn5yqiWOGq+zQYU176R+6ZWA/o3JSWFyie8anaspfU9SpY4T8m/tpcFysps56Tec6/OUI8Ne0Wf9Q+wsvUJfOV9T72cDZpsFJRqtWrfTSSy9p0KBBx72em5urqKio/3ZcOIWQkGDNefNvatUqRGVlh7Vu3UYNiLtdn33+y5LHoUNulM1mU/r7H9Z5/ZEjTl3T7WqNH3ePAgMdKiws0VdZ2bq2+40q/tUGRhde2FbB//lpTZL+9a+P1SIoUI88fJ9atQpR3vrNGhifqN27fzRipk57Wb6+TTXrb1MUGOjQ6tXfqf+ABJWXV3jmm4Hfhf2lpUp9cqqK9x+Qv5+fLrm4nV55/kl1vfpK/ZhfqC+zsiVJtwwb4/a6N158VldfeZkkaefuvZr5yhyVHTqs81qFasSdt+qOoTe5xT/72CRNmTFbI+57WJLU45ouenjCaOO6l5eXXp76uJ58/iUljkqR3e6jAX16KOVXrcqjR2u0Y/deVR75ZR7UA+NHqomXlyb+NU1OZ5WiO1+uWQ9PdPv47VM9G/87tcwhs0SD98mIj4/XFVdcoSeeqPvTsSR9//33+uMf/+i2uU19sE8GUBf7ZADH5+l9Mv7cdrBl93pn1weW3ets0+BKxv3336+KihP/RHrxxRfryy+//K8GBQAAzn4NTjKuvfbak1738/NT9+7dT3tAAAA0tt/7Z45Yhc24AAAw+b0vPbUK24oDAACPoJIBAIAJ+2RYgyQDAAAT5mRYgyQDAAAT5mRYgzkZAADAI6hkAABgwpwMa5BkAABg0sDNsHECtEsAAIBHUMkAAMCE1SXWIMkAAMCEORnWoF0CAAA8gkoGAAAm7JNhDZIMAABMmJNhDdolAADAI6hkAABgwj4Z1iDJAADAhNUl1iDJAADAhImf1mBOBgAA8AgqGQAAmLC6xBokGQAAmDDx0xq0SwAAgEdQyQAAwIR2iTVIMgAAMGF1iTVolwAAAI+gkgEAgEktEz8tQZIBAIAJKYY1aJcAAACPoJIBAIAJq0usQSUDAACTWrksOxpi8uTJstlsbkdYWJhx3eVyafLkyQoPD5evr6969Oih9evXu93D6XRq3LhxCg4Olp+fn+Lj47V37163mNLSUiUmJsrhcMjhcCgxMVEHDx487e/XiZBkAABg4nK5LDsa6g9/+IPy8/ONY926dca15557TtOnT9esWbO0Zs0ahYWFqU+fPjp8+LARk5ycrAULFig9PV1ZWVkqLy9XXFycampqjJiEhATl5uYqIyNDGRkZys3NVWJi4n/3TTsO2iUAAJxBmjRp4la9+JnL5dLMmTP18MMPa/DgwZKkt956S6GhoXr33Xc1cuRIlZWV6fXXX9fcuXPVu3dvSdI777yj1q1b67PPPlPfvn21ceNGZWRkKDs7W9HR0ZKk1157TTExMdq8ebMiIiIsey9UMgAAMLGyXeJ0OnXo0CG3w+l0nvDZW7ZsUXh4uNq1a6dbb71V27dvlyTt2LFDBQUFio2NNWLtdru6d++uFStWSJJycnJUXV3tFhMeHq7IyEgjZuXKlXI4HEaCIUldunSRw+EwYqxCkgEAgInLwv+lpaUZcx9+PtLS0o773OjoaL399tv69NNP9dprr6mgoEBdu3bV/v37VVBQIEkKDQ11e01oaKhxraCgQD4+PgoMDDxpTEhISJ1nh4SEGDFWoV0CAIAHpaamasKECW7n7Hb7cWP79+9v/LpTp06KiYnRRRddpLfeektdunSRJNlsNrfXuFyuOufMzDHHi6/PfRqKSgYAACZWTvy02+0KCAhwO06UZJj5+fmpU6dO2rJlizFPw1xtKCoqMqobYWFhqqqqUmlp6UljCgsL6zyruLi4TpXkv0WSAQCASWMtYTVzOp3auHGjWrVqpXbt2iksLEyZmZnG9aqqKi1btkxdu3aVJEVFRcnb29stJj8/X3l5eUZMTEyMysrKtHr1aiNm1apVKisrM2KsQrsEAIAzREpKigYOHKg2bdqoqKhITz31lA4dOqQ777xTNptNycnJmjJlitq3b6/27dtrypQpatasmRISEiRJDodDw4cP18SJE9WiRQsFBQUpJSVFnTp1MlabdOjQQf369VNSUpJeffVVSdKIESMUFxdn6coSiSQDAIA6Tmd/Cyvs3btXt912m0pKStSyZUt16dJF2dnZatu2rSRp0qRJqqys1OjRo1VaWqro6GgtWbJE/v7+xj1mzJihJk2aaMiQIaqsrFSvXr00Z84ceXl5GTHz5s3T+PHjjVUo8fHxmjVrluXvx+ZqrO+kSROf8xp7CMAZp3LfV409BOCM5B18oUfvf3mYdW2D7wusXRZ6NmFOBgAA8AjaJQAAmLj4gDRLkGQAAGBSe2bMJDjrkWQAAGBCJcMazMkAAAAeQSUDAAAT2iXWIMkAAMCEdok1aJcAAACPoJIBAIAJ7RJrkGQAAGBCu8QatEsAAIBHUMkAAMCEdok1SDIAADChXWIN2iUAAMAjqGQAAGDictU29hB+E0gyAAAwqaVdYgmSDAAATFxM/LQEczIAAIBHUMkAAMCEdok1SDIAADChXWIN2iUAAMAjqGQAAGDCjp/WIMkAAMCEHT+tQbsEAAB4BJUMAABMmPhpDZIMAABMWMJqDdolAADAI6hkAABgQrvEGiQZAACYsITVGiQZAACYUMmwBnMyAACAR1DJAADAhNUl1iDJAADAhHaJNWiXAAAAj6CSAQCACatLrEGSAQCACR+QZg3aJQAAwCOoZAAAYEK7xBokGQAAmLC6xBq0SwAAgEdQyQAAwISJn9YgyQAAwIR2iTVIMgAAMCHJsAZzMgAAgEdQyQAAwIQ6hjVsLmpC+BWn06m0tDSlpqbKbrc39nCAMwJ/LoDTQ5IBN4cOHZLD4VBZWZkCAgIaezjAGYE/F8DpYU4GAADwCJIMAADgESQZAADAI0gy4MZut+uxxx5jchvwK/y5AE4PEz8BAIBHUMkAAAAeQZIBAAA8giQDAAB4BEkGAADwCJIMGF5++WW1a9dOTZs2VVRUlL766qvGHhLQqJYvX66BAwcqPDxcNptNH374YWMPCTirkGRAkvT+++8rOTlZDz/8sL777jtde+216t+/v3bv3t3YQwMaTUVFhS6//HLNmjWrsYcCnJVYwgpJUnR0tK688krNnj3bONehQwcNGjRIaWlpjTgy4Mxgs9m0YMECDRo0qLGHApw1qGRAVVVVysnJUWxsrNv52NhYrVixopFGBQA425FkQCUlJaqpqVFoaKjb+dDQUBUUFDTSqAAAZzuSDBhsNpvb1y6Xq845AADqiyQDCg4OlpeXV52qRVFRUZ3qBgAA9UWSAfn4+CgqKkqZmZlu5zMzM9W1a9dGGhUA4GzXpLEHgDPDhAkTlJiYqM6dOysmJkZ///vftXv3bo0aNaqxhwY0mvLycm3dutX4eseOHcrNzVVQUJDatGnTiCMDzg4sYYXh5Zdf1nPPPaf8/HxFRkZqxowZuu666xp7WECjWbp0qXr27Fnn/J133qk5c+b87wcEnGVIMgAAgEcwJwMAAHgESQYAAPAIkgwAAOARJBkAAMAjSDIAAIBHkGQAAACPIMkAAAAeQZIBAAA8giQDAAB4BEkGAADwCJIMAADgESQZAADAI/4fdMxvzrhKwV0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(Y_test,test_predicted)\n",
    "sn.heatmap(cm, annot=True , fmt='.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c29abbb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:34:57.983707Z",
     "iopub.status.busy": "2023-02-25T14:34:57.983232Z",
     "iopub.status.idle": "2023-02-25T14:35:22.145013Z",
     "shell.execute_reply": "2023-02-25T14:35:22.143327Z"
    },
    "papermill": {
     "duration": 24.183688,
     "end_time": "2023-02-25T14:35:22.147648",
     "exception": false,
     "start_time": "2023-02-25T14:34:57.963960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer(analyzer = stemmed_words , max_features = 10000)\n",
    "feature_vector = tfidf_vectorizer.fit(data.Review)\n",
    "data_features = tfidf_vectorizer.transform(data.Review)\n",
    "features = feature_vector.get_feature_names()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "93eb39aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:22.186214Z",
     "iopub.status.busy": "2023-02-25T14:35:22.185735Z",
     "iopub.status.idle": "2023-02-25T14:35:22.191795Z",
     "shell.execute_reply": "2023-02-25T14:35:22.190383Z"
    },
    "papermill": {
     "duration": 0.028741,
     "end_time": "2023-02-25T14:35:22.194583",
     "exception": false,
     "start_time": "2023-02-25T14:35:22.165842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "352c4788",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:22.233285Z",
     "iopub.status.busy": "2023-02-25T14:35:22.232876Z",
     "iopub.status.idle": "2023-02-25T14:35:22.329839Z",
     "shell.execute_reply": "2023-02-25T14:35:22.328612Z"
    },
    "papermill": {
     "duration": 0.119174,
     "end_time": "2023-02-25T14:35:22.332633",
     "exception": false,
     "start_time": "2023-02-25T14:35:22.213459",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model: 94.01740112414342%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of the model: {0}%'.format(accuracy_score(Y_test,test_predicted)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "79608e2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:22.371219Z",
     "iopub.status.busy": "2023-02-25T14:35:22.370776Z",
     "iopub.status.idle": "2023-02-25T14:35:22.410090Z",
     "shell.execute_reply": "2023-02-25T14:35:22.408566Z"
    },
    "papermill": {
     "duration": 0.062022,
     "end_time": "2023-02-25T14:35:22.413086",
     "exception": false,
     "start_time": "2023-02-25T14:35:22.351064",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "X_train , X_test , Y_train , Y_test = train_test_split(data_features,data.Sentiment,test_size=0.2,random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "257bd075",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:22.452847Z",
     "iopub.status.busy": "2023-02-25T14:35:22.452408Z",
     "iopub.status.idle": "2023-02-25T14:35:26.602750Z",
     "shell.execute_reply": "2023-02-25T14:35:26.601354Z"
    },
    "papermill": {
     "duration": 4.173551,
     "end_time": "2023-02-25T14:35:26.605464",
     "exception": false,
     "start_time": "2023-02-25T14:35:22.431913",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb_clf2 = GaussianNB()\n",
    "nb_clf2.fit(X_train.toarray(),Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "20fc1840",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:26.644492Z",
     "iopub.status.busy": "2023-02-25T14:35:26.644050Z",
     "iopub.status.idle": "2023-02-25T14:35:27.667594Z",
     "shell.execute_reply": "2023-02-25T14:35:27.666274Z"
    },
    "id": "3X2c8Gt6ugA0",
    "papermill": {
     "duration": 1.046935,
     "end_time": "2023-02-25T14:35:27.670644",
     "exception": false,
     "start_time": "2023-02-25T14:35:26.623709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_predicted = nb_clf2.predict(X_test.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0bda1932",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:27.712650Z",
     "iopub.status.busy": "2023-02-25T14:35:27.712203Z",
     "iopub.status.idle": "2023-02-25T14:35:29.112084Z",
     "shell.execute_reply": "2023-02-25T14:35:29.110663Z"
    },
    "papermill": {
     "duration": 1.423422,
     "end_time": "2023-02-25T14:35:29.114575",
     "exception": false,
     "start_time": "2023-02-25T14:35:27.691153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.15      1.00      0.25      5690\n",
      "    positive       0.86      0.00      0.01     33273\n",
      "\n",
      "    accuracy                           0.15     38963\n",
      "   macro avg       0.50      0.50      0.13     38963\n",
      "weighted avg       0.75      0.15      0.04     38963\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(Y_test,test_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "83e43fa3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:29.153342Z",
     "iopub.status.busy": "2023-02-25T14:35:29.152915Z",
     "iopub.status.idle": "2023-02-25T14:35:29.601708Z",
     "shell.execute_reply": "2023-02-25T14:35:29.600464Z"
    },
    "papermill": {
     "duration": 0.471613,
     "end_time": "2023-02-25T14:35:29.604432",
     "exception": false,
     "start_time": "2023-02-25T14:35:29.132819",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(Y_test,test_predicted)\n",
    "sn.heatmap(cm, annot=True , fmt='.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ac8c4849",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:29.645313Z",
     "iopub.status.busy": "2023-02-25T14:35:29.644898Z",
     "iopub.status.idle": "2023-02-25T14:35:29.738667Z",
     "shell.execute_reply": "2023-02-25T14:35:29.737752Z"
    },
    "papermill": {
     "duration": 0.116961,
     "end_time": "2023-02-25T14:35:29.741202",
     "exception": false,
     "start_time": "2023-02-25T14:35:29.624241",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model: 14.834586659138157%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of the model: {0}%'.format(accuracy_score(Y_test,test_predicted)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "50a982f6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:29.783063Z",
     "iopub.status.busy": "2023-02-25T14:35:29.782502Z",
     "iopub.status.idle": "2023-02-25T14:35:29.790118Z",
     "shell.execute_reply": "2023-02-25T14:35:29.788936Z"
    },
    "papermill": {
     "duration": 0.032148,
     "end_time": "2023-02-25T14:35:29.792729",
     "exception": false,
     "start_time": "2023-02-25T14:35:29.760581",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from nltk.stem import PorterStemmer\n",
    "import re\n",
    "stemmer = PorterStemmer()\n",
    "def get_stemmed_tokens(doc):\n",
    "  all_tokens = [word for word in nltk.word_tokenize(doc)]\n",
    "  clean_tokens=[]\n",
    "  for each_token in all_tokens:\n",
    "    if re.search('[a-zA-Z]',each_token):\n",
    "        clean_tokens.append(each_token)\n",
    "  stemmed_tokens = [stemmer.stem(t) for t in clean_tokens]\n",
    "  return stemmed_tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "688e4d1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:35:29.832838Z",
     "iopub.status.busy": "2023-02-25T14:35:29.832326Z",
     "iopub.status.idle": "2023-02-25T14:36:32.792557Z",
     "shell.execute_reply": "2023-02-25T14:36:32.791137Z"
    },
    "papermill": {
     "duration": 63.002617,
     "end_time": "2023-02-25T14:36:32.814206",
     "exception": false,
     "start_time": "2023-02-25T14:35:29.811589",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:517: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  \"The parameter 'token_pattern' will not be used\"\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:401: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['abov', 'afterward', 'alon', 'alreadi', 'alway', 'ani', 'anoth', 'anyon', 'anyth', 'anywher', 'becam', 'becaus', 'becom', 'befor', 'besid', 'cri', 'describ', 'dure', 'els', 'elsewher', 'empti', 'everi', 'everyon', 'everyth', 'everywher', 'fifti', 'formerli', 'forti', 'ha', 'henc', 'hereaft', 'herebi', 'hi', 'howev', 'hundr', 'inde', 'latterli', 'mani', 'meanwhil', 'moreov', 'mostli', 'nobodi', 'noon', 'noth', 'nowher', 'onc', 'onli', 'otherwis', 'ourselv', 'perhap', 'pleas', 'seriou', 'sever', 'sinc', 'sincer', 'sixti', 'someon', 'someth', 'sometim', 'somewher', 'themselv', 'thenc', 'thereaft', 'therebi', 'therefor', 'thi', 'thu', 'togeth', 'twelv', 'twenti', 'veri', 'wa', 'whatev', 'whenc', 'whenev', 'wherea', 'whereaft', 'wherebi', 'wherev', 'whi', 'yourselv'] not in stop_words.\n",
      "  % sorted(inconsistent)\n",
      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(stop_words ='english' , max_features = 10000 , tokenizer=get_stemmed_tokens , ngram_range=(1,2))\n",
    "feature_vector = tfidf_vectorizer.fit(data.Review)\n",
    "data_features = tfidf_vectorizer.transform(data.Review)\n",
    "features = feature_vector.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a7defdbd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:36:32.854531Z",
     "iopub.status.busy": "2023-02-25T14:36:32.854107Z",
     "iopub.status.idle": "2023-02-25T14:36:43.168061Z",
     "shell.execute_reply": "2023-02-25T14:36:43.166800Z"
    },
    "papermill": {
     "duration": 10.337173,
     "end_time": "2023-02-25T14:36:43.170503",
     "exception": false,
     "start_time": "2023-02-25T14:36:32.833330",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.90      0.62      0.73      5690\n",
      "    positive       0.94      0.99      0.96     33273\n",
      "\n",
      "    accuracy                           0.93     38963\n",
      "   macro avg       0.92      0.80      0.85     38963\n",
      "weighted avg       0.93      0.93      0.93     38963\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train , X_test , Y_train , Y_test = train_test_split(data_features,data.Sentiment,test_size=0.2,random_state=42)\n",
    "nb_clf3 = BernoulliNB()\n",
    "nb_clf3.fit(X_train.toarray(),Y_train)\n",
    "test_predicted = nb_clf3.predict(X_test.toarray())\n",
    "print(metrics.classification_report(Y_test,test_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2df4243b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:36:43.212505Z",
     "iopub.status.busy": "2023-02-25T14:36:43.212018Z",
     "iopub.status.idle": "2023-02-25T14:36:43.659766Z",
     "shell.execute_reply": "2023-02-25T14:36:43.658534Z"
    },
    "papermill": {
     "duration": 0.472072,
     "end_time": "2023-02-25T14:36:43.662220",
     "exception": false,
     "start_time": "2023-02-25T14:36:43.190148",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(Y_test,test_predicted)\n",
    "sn.heatmap(cm, annot=True , fmt='.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "be268a69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:36:43.704267Z",
     "iopub.status.busy": "2023-02-25T14:36:43.703811Z",
     "iopub.status.idle": "2023-02-25T14:36:43.801247Z",
     "shell.execute_reply": "2023-02-25T14:36:43.799641Z"
    },
    "papermill": {
     "duration": 0.121894,
     "end_time": "2023-02-25T14:36:43.804054",
     "exception": false,
     "start_time": "2023-02-25T14:36:43.682160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the model: 93.40143212791622%\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy of the model: {0}%'.format(accuracy_score(Y_test,test_predicted)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8cba0799",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:36:43.846692Z",
     "iopub.status.busy": "2023-02-25T14:36:43.846280Z",
     "iopub.status.idle": "2023-02-25T14:36:43.856675Z",
     "shell.execute_reply": "2023-02-25T14:36:43.855212Z"
    },
    "papermill": {
     "duration": 0.035326,
     "end_time": "2023-02-25T14:36:43.859452",
     "exception": false,
     "start_time": "2023-02-25T14:36:43.824126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                  super!\n",
       "1                 awesome\n",
       "2                    fair\n",
       "3         useless product\n",
       "5                 awesome\n",
       "               ...       \n",
       "205046     classy product\n",
       "205047          must buy!\n",
       "205048             super!\n",
       "205049               nice\n",
       "205050          just wow!\n",
       "Name: Review, Length: 194813, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Review"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ce47d8b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:36:43.903738Z",
     "iopub.status.busy": "2023-02-25T14:36:43.903232Z",
     "iopub.status.idle": "2023-02-25T14:36:43.914749Z",
     "shell.execute_reply": "2023-02-25T14:36:43.913915Z"
    },
    "papermill": {
     "duration": 0.037199,
     "end_time": "2023-02-25T14:36:43.917061",
     "exception": false,
     "start_time": "2023-02-25T14:36:43.879862",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<38963x3038 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 83513 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3857c9bf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-02-25T14:36:43.965613Z",
     "iopub.status.busy": "2023-02-25T14:36:43.964323Z",
     "iopub.status.idle": "2023-02-25T14:36:43.972597Z",
     "shell.execute_reply": "2023-02-25T14:36:43.971351Z"
    },
    "papermill": {
     "duration": 0.032946,
     "end_time": "2023-02-25T14:36:43.975072",
     "exception": false,
     "start_time": "2023-02-25T14:36:43.942126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<194813x3038 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 417369 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_features"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 169.177822,
   "end_time": "2023-02-25T14:36:45.121222",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-02-25T14:33:55.943400",
   "version": "2.3.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}