sentiment-analysis-on-amazon-kindle.ipynb

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    "# Sentiment Analysis with Logistic Regression\n",
    "\n",
    "![](https://miro.medium.com/max/1400/0*q3wC98LKNGFSFwdG.gif)\n",
    "\n",
    "We will do the following operations to train a sentiment analysis model:\n",
    "- Load the the necessary modules\n",
    "- Preprocessing our data-set\n",
    "- EDA\n",
    "- Split the dataset\n",
    "- Train the model\n",
    "- Fine-tuned Model\n",
    "- Conclusion"
   ]
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   "source": [
    "![](https://i.gifer.com/UDdW.gif)"
   ]
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   },
   "source": [
    "# Importing necessary modules"
   ]
  },
  {
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   "source": [
    "import re\n",
    "import string\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import ConfusionMatrixDisplay,confusion_matrix\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "# Loading our data"
   ]
  },
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   "source": [
    "# loading our data using pandas read_csv\n",
    "\n",
    "data = pd.read_csv(\"../input/amazon-kindle-book-review-for-sentiment-analysis/preprocessed_kindle_review .csv\")\n",
    "df = data"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(12000, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing dim of the data\n",
    "\n",
    "data.shape"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'rating', 'reviewText', 'summary'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying all the columns of the dataset\n",
    "\n",
    "data.columns"
   ]
  },
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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</th>\n",
       "      <th>rating</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>This book was the very first bookmobile book I...</td>\n",
       "      <td>50 + years ago...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>When I read the description for this book, I c...</td>\n",
       "      <td>Boring! Boring! Boring!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>I just had to edit this review. This book is a...</td>\n",
       "      <td>Wiggleliscious/new toy ready/!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>I don't normally buy 'mystery' novels because ...</td>\n",
       "      <td>Very good read.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>This isn't the kind of book I normally read, a...</td>\n",
       "      <td>Great Story!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   Unnamed: 0  rating                                         reviewText  \\\n",
       "0           0       5  This book was the very first bookmobile book I...   \n",
       "1           1       1  When I read the description for this book, I c...   \n",
       "2           2       5  I just had to edit this review. This book is a...   \n",
       "3           3       5  I don't normally buy 'mystery' novels because ...   \n",
       "4           4       5  This isn't the kind of book I normally read, a...   \n",
       "\n",
       "                           summary  \n",
       "0                50 + years ago...  \n",
       "1          Boring! Boring! Boring!  \n",
       "2  Wiggleliscious/new toy ready/!!  \n",
       "3                  Very good read.  \n",
       "4                     Great Story!  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# quick review of the dataset\n",
    "\n",
    "data.head()"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "'This book was the very first bookmobile book I bought when I was in the school book club. I loved the story then and I bet a dollar to a donut I will love it again. If my memory serves, I bought this book in 5th grade. That would have been about 1961. I am looking forward to reliving the memories.'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing the first review from the dataset\n",
    "\n",
    "data.reviewText[0]"
   ]
  },
  {
   "cell_type": "code",
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   "id": "42778d18",
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       "5    3000\n",
       "4    3000\n",
       "1    2000\n",
       "3    2000\n",
       "2    2000\n",
       "Name: rating, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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   ],
   "source": [
    "# value_counts() function returns object containing counts of unique values. \n",
    "# The resulting object will be in descending order so that the first element is the most frequently-occurring element.\n",
    "\n",
    "a=data.rating.value_counts()\n",
    "a"
   ]
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      "text/plain": [
       "Unnamed: 0    0\n",
       "rating        0\n",
       "reviewText    0\n",
       "summary       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "# checking for null values\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
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     "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": [
    "# PLotting Rating histogram\n",
    "\n",
    "data.rating.hist()\n",
    "plt.title(\"Distribution of rating using Matplotlib\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5fb2d983",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:28.808895Z",
     "iopub.status.busy": "2022-03-20T07:58:28.807947Z",
     "iopub.status.idle": "2022-03-20T07:58:28.903135Z",
     "shell.execute_reply": "2022-03-20T07:58:28.903848Z",
     "shell.execute_reply.started": "2022-03-20T07:14:38.875492Z"
    },
    "papermill": {
     "duration": 0.16652,
     "end_time": "2022-03-20T07:58:28.904065",
     "exception": false,
     "start_time": "2022-03-20T07:58:28.737545",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.9.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
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of the Rating using ploty\"}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLotting Rating using ploty\n",
    "\n",
    "fig = go.Figure([go.Bar(x=a.index, y=a.values,text=a.values)])\n",
    "fig.update_layout(title='Distribution of the Rating using ploty')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f5e507",
   "metadata": {
    "papermill": {
     "duration": 0.070161,
     "end_time": "2022-03-20T07:58:29.043083",
     "exception": false,
     "start_time": "2022-03-20T07:58:28.972922",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b8c94f",
   "metadata": {
    "papermill": {
     "duration": 0.066099,
     "end_time": "2022-03-20T07:58:29.178768",
     "exception": false,
     "start_time": "2022-03-20T07:58:29.112669",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Dropping columns that are not needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ec7bb72",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:29.320365Z",
     "iopub.status.busy": "2022-03-20T07:58:29.319362Z",
     "iopub.status.idle": "2022-03-20T07:58:29.325068Z",
     "shell.execute_reply": "2022-03-20T07:58:29.325534Z",
     "shell.execute_reply.started": "2022-03-20T07:14:38.895572Z"
    },
    "papermill": {
     "duration": 0.076895,
     "end_time": "2022-03-20T07:58:29.325735",
     "exception": false,
     "start_time": "2022-03-20T07:58:29.248840",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'rating', 'reviewText', 'summary'], dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8385b33c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:29.464886Z",
     "iopub.status.busy": "2022-03-20T07:58:29.463881Z",
     "iopub.status.idle": "2022-03-20T07:58:29.473449Z",
     "shell.execute_reply": "2022-03-20T07:58:29.472748Z",
     "shell.execute_reply.started": "2022-03-20T07:14:38.906808Z"
    },
    "papermill": {
     "duration": 0.079725,
     "end_time": "2022-03-20T07:58:29.473616",
     "exception": false,
     "start_time": "2022-03-20T07:58:29.393891",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = data.drop(['Unnamed: 0', 'summary'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f5c139f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:29.620443Z",
     "iopub.status.busy": "2022-03-20T07:58:29.619460Z",
     "iopub.status.idle": "2022-03-20T07:58:29.622983Z",
     "shell.execute_reply": "2022-03-20T07:58:29.623484Z",
     "shell.execute_reply.started": "2022-03-20T07:14:38.923662Z"
    },
    "papermill": {
     "duration": 0.082058,
     "end_time": "2022-03-20T07:58:29.623657",
     "exception": false,
     "start_time": "2022-03-20T07:58:29.541599",
     "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>rating</th>\n",
       "      <th>reviewText</th>\n",
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       "  </thead>\n",
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       "      <td>5</td>\n",
       "      <td>This book was the very first bookmobile book I...</td>\n",
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       "      <th>2</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>I don't normally buy 'mystery' novels because ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>This isn't the kind of book I normally read, a...</td>\n",
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       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "   rating                                         reviewText\n",
       "0       5  This book was the very first bookmobile book I...\n",
       "1       1  When I read the description for this book, I c...\n",
       "2       5  I just had to edit this review. This book is a...\n",
       "3       5  I don't normally buy 'mystery' novels because ...\n",
       "4       5  This isn't the kind of book I normally read, a..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview of the dataset\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeadd6b6",
   "metadata": {
    "papermill": {
     "duration": 0.068527,
     "end_time": "2022-03-20T07:58:29.761377",
     "exception": false,
     "start_time": "2022-03-20T07:58:29.692850",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- converting rating to 0 and 1 from 1-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e734994",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:29.908497Z",
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    "papermill": {
     "duration": 0.100621,
     "end_time": "2022-03-20T07:58:29.930184",
     "exception": false,
     "start_time": "2022-03-20T07:58:29.829563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>11997</th>\n",
       "      <td>0</td>\n",
       "      <td>Dragon Knights is a world where Knights ride d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>0</td>\n",
       "      <td>Since this story is very short, it's hard to s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>0</td>\n",
       "      <td>from 1922 an amazing collection of info on sym...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating                                         reviewText\n",
       "0           0  This book was the very first bookmobile book I...\n",
       "1           1  When I read the description for this book, I c...\n",
       "2           0  I just had to edit this review. This book is a...\n",
       "3           0  I don't normally buy 'mystery' novels because ...\n",
       "4           0  This isn't the kind of book I normally read, a...\n",
       "...       ...                                                ...\n",
       "11995       1  Had to read certain passages twice--typos.  Wi...\n",
       "11996       0  Not what i expected. yet a very interesting bo...\n",
       "11997       0  Dragon Knights is a world where Knights ride d...\n",
       "11998       0  Since this story is very short, it's hard to s...\n",
       "11999       0  from 1922 an amazing collection of info on sym...\n",
       "\n",
       "[12000 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if rating is above 3 we will consider it as 1 else 0.\n",
    "\n",
    "data[\"rating\"] = data[\"rating\"].apply(lambda x: 1 if x < 3  else 0) # positive as 0 and negative as 1\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fef57785",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:30.074119Z",
     "iopub.status.busy": "2022-03-20T07:58:30.072650Z",
     "iopub.status.idle": "2022-03-20T07:58:30.107091Z",
     "shell.execute_reply": "2022-03-20T07:58:30.107671Z",
     "shell.execute_reply.started": "2022-03-20T07:14:38.97415Z"
    },
    "papermill": {
     "duration": 0.10552,
     "end_time": "2022-03-20T07:58:30.107860",
     "exception": false,
     "start_time": "2022-03-20T07:58:30.002340",
     "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>rating</th>\n",
       "      <th>reviewText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>this book was the very first bookmobile book i...</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>when i read the description for this book, i c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>i just had to edit this review. this book is a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>i don't normally buy 'mystery' novels because ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>this isn't the kind of book i normally read, a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating                                         reviewText\n",
       "0       0  this book was the very first bookmobile book i...\n",
       "1       1  when i read the description for this book, i c...\n",
       "2       0  i just had to edit this review. this book is a...\n",
       "3       0  i don't normally buy 'mystery' novels because ...\n",
       "4       0  this isn't the kind of book i normally read, a..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lowering the text of the review\n",
    "\n",
    "data[\"reviewText\"] = data[\"reviewText\"].str.lower()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1e0424aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:30.256915Z",
     "iopub.status.busy": "2022-03-20T07:58:30.255910Z",
     "iopub.status.idle": "2022-03-20T07:58:30.353164Z",
     "shell.execute_reply": "2022-03-20T07:58:30.353820Z",
     "shell.execute_reply.started": "2022-03-20T07:14:39.007674Z"
    },
    "papermill": {
     "duration": 0.175173,
     "end_time": "2022-03-20T07:58:30.354004",
     "exception": false,
     "start_time": "2022-03-20T07:58:30.178831",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating</th>\n",
       "      <th>reviewText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>this book was the very first bookmobile book i...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>i just had to edit this review this book is an...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>i dont normally buy mystery novels because i j...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>this isnt the kind of book i normally read alt...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating                                         reviewText\n",
       "0       0  this book was the very first bookmobile book i...\n",
       "1       1  when i read the description for this book i co...\n",
       "2       0  i just had to edit this review this book is an...\n",
       "3       0  i dont normally buy mystery novels because i j...\n",
       "4       0  this isnt the kind of book i normally read alt..."
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing punctuation\n",
    "\n",
    "PUNCT_TO_REMOVE = string.punctuation\n",
    "def remove_punctuation(text):\n",
    "    \"\"\"custom function to remove the punctuation\"\"\"\n",
    "    return text.translate(str.maketrans('', '', PUNCT_TO_REMOVE))\n",
    "\n",
    "data[\"reviewText\"] = data[\"reviewText\"].apply(lambda text: remove_punctuation(text))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ed1f3c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:30.498134Z",
     "iopub.status.busy": "2022-03-20T07:58:30.497103Z",
     "iopub.status.idle": "2022-03-20T07:58:30.797752Z",
     "shell.execute_reply": "2022-03-20T07:58:30.798351Z",
     "shell.execute_reply.started": "2022-03-20T07:14:39.108919Z"
    },
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     "exception": false,
     "start_time": "2022-03-20T07:58:30.422685",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>rating</th>\n",
       "      <th>reviewText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>book first bookmobile book bought school book ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>read description book couldnt wait read downlo...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>edit review book believe got right updated rew...</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>dont normally buy mystery novels dont like how...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>isnt kind book normally read although try limi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating                                         reviewText\n",
       "0       0  book first bookmobile book bought school book ...\n",
       "1       1  read description book couldnt wait read downlo...\n",
       "2       0  edit review book believe got right updated rew...\n",
       "3       0  dont normally buy mystery novels dont like how...\n",
       "4       0  isnt kind book normally read although try limi..."
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing stop words from the dataset\n",
    "\n",
    "STOPWORDS = set(stopwords.words('english'))\n",
    "def remove_stopwords(text):\n",
    "    \"\"\"custom function to remove the stopwords\"\"\"\n",
    "    return \" \".join([word for word in str(text).split() if word not in STOPWORDS])\n",
    "\n",
    "data[\"reviewText\"] = data[\"reviewText\"].apply(lambda text: remove_stopwords(text))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b0687b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:58:30.947158Z",
     "iopub.status.busy": "2022-03-20T07:58:30.946385Z",
     "iopub.status.idle": "2022-03-20T07:59:30.140712Z",
     "shell.execute_reply": "2022-03-20T07:59:30.141219Z",
     "shell.execute_reply.started": "2022-03-20T07:14:39.402716Z"
    },
    "papermill": {
     "duration": 59.270035,
     "end_time": "2022-03-20T07:59:30.141398",
     "exception": false,
     "start_time": "2022-03-20T07:58:30.871363",
     "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>rating</th>\n",
       "      <th>reviewText</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>book first bookmobile book buy school book clu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>read description book couldnt wait read downlo...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>edit review book believe get right update rewr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>dont normally buy mystery novels dont like how...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>isnt kind book normally read although try limi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating                                         reviewText\n",
       "0       0  book first bookmobile book buy school book clu...\n",
       "1       1  read description book couldnt wait read downlo...\n",
       "2       0  edit review book believe get right update rewr...\n",
       "3       0  dont normally buy mystery novels dont like how...\n",
       "4       0  isnt kind book normally read although try limi..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lemmatizer = WordNetLemmatizer()\n",
    "wordnet_map = {\"N\":wordnet.NOUN, \"V\":wordnet.VERB, \"J\":wordnet.ADJ, \"R\":wordnet.ADV}\n",
    "def lemmatize_words(text):\n",
    "    pos_tagged_text = nltk.pos_tag(text.split())\n",
    "    return \" \".join([lemmatizer.lemmatize(word, wordnet_map.get(pos[0], wordnet.NOUN)) for word, pos in pos_tagged_text])\n",
    "\n",
    "data[\"reviewText\"] = data[\"reviewText\"].apply(lambda text: lemmatize_words(text))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce1e2619",
   "metadata": {
    "papermill": {
     "duration": 0.071659,
     "end_time": "2022-03-20T07:59:30.286318",
     "exception": false,
     "start_time": "2022-03-20T07:59:30.214659",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Visualization on Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f0a583d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:30.439082Z",
     "iopub.status.busy": "2022-03-20T07:59:30.438329Z",
     "iopub.status.idle": "2022-03-20T07:59:30.625676Z",
     "shell.execute_reply": "2022-03-20T07:59:30.625064Z",
     "shell.execute_reply.started": "2022-03-20T07:15:36.728832Z"
    },
    "papermill": {
     "duration": 0.265264,
     "end_time": "2022-03-20T07:59:30.625836",
     "exception": false,
     "start_time": "2022-03-20T07:59:30.360572",
     "status": "completed"
    },
    "tags": []
   },
   "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": [
    "# PLotting Rating histogram\n",
    "\n",
    "data.rating.hist()\n",
    "plt.title(\"Distribution of rating using Matplotlib\")\n",
    "plt.show()"
   ]
  },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLotting Rating using ploty\n",
    "a  = data.rating.value_counts()\n",
    "fig = go.Figure([go.Bar(x=a.index, y=a.values,text=a.values)])\n",
    "fig.update_layout(title='Distribution of the Rating using ploty')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c75048",
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   },
   "source": [
    "- Plotting word cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "557469b7",
   "metadata": {
    "_kg_hide-input": true,
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:31.108689Z",
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     "shell.execute_reply": "2022-03-20T07:59:31.571651Z",
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     "start_time": "2022-03-20T07:59:31.016248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "text = \" \".join(cat.split()[0] for cat in data.reviewText)\n",
    "word_cloud = WordCloud(collocations = False, background_color = 'white').generate(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8d350b65",
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    "execution": {
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     "start_time": "2022-03-20T07:59:31.649155",
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   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(word_cloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dbb05334",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:32.150866Z",
     "iopub.status.busy": "2022-03-20T07:59:32.150174Z",
     "iopub.status.idle": "2022-03-20T07:59:32.490596Z",
     "shell.execute_reply": "2022-03-20T07:59:32.489917Z",
     "shell.execute_reply.started": "2022-03-20T07:15:37.659789Z"
    },
    "papermill": {
     "duration": 0.429166,
     "end_time": "2022-03-20T07:59:32.490756",
     "exception": false,
     "start_time": "2022-03-20T07:59:32.061590",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('book', 15397),\n",
       " ('story', 11027),\n",
       " ('read', 10023),\n",
       " ('like', 6207),\n",
       " ('one', 5949),\n",
       " ('character', 5677),\n",
       " ('get', 5453),\n",
       " ('love', 5059),\n",
       " ('good', 4763),\n",
       " ('would', 4093)]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "cnt = Counter()\n",
    "for text in data[\"reviewText\"].values:\n",
    "    for word in text.split():\n",
    "        cnt[word] += 1\n",
    "        \n",
    "cnt.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "492f2acd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:32.649375Z",
     "iopub.status.busy": "2022-03-20T07:59:32.648737Z",
     "iopub.status.idle": "2022-03-20T07:59:32.651828Z",
     "shell.execute_reply": "2022-03-20T07:59:32.652296Z",
     "shell.execute_reply.started": "2022-03-20T07:15:38.02384Z"
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    "papermill": {
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     "exception": false,
     "start_time": "2022-03-20T07:59:32.569002",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15397"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt.get(\"book\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "13768f78",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:32.812814Z",
     "iopub.status.busy": "2022-03-20T07:59:32.812135Z",
     "iopub.status.idle": "2022-03-20T07:59:32.825054Z",
     "shell.execute_reply": "2022-03-20T07:59:32.824452Z",
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     "exception": false,
     "start_time": "2022-03-20T07:59:32.729448",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cn = cnt.most_common(10)\n",
    "w = []\n",
    "c = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c9735e23",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:32.992488Z",
     "iopub.status.busy": "2022-03-20T07:59:32.991815Z",
     "iopub.status.idle": "2022-03-20T07:59:32.995738Z",
     "shell.execute_reply": "2022-03-20T07:59:32.995186Z",
     "shell.execute_reply.started": "2022-03-20T07:15:38.055676Z"
    },
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     "exception": false,
     "start_time": "2022-03-20T07:59:32.905757",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i in cn:\n",
    "    w.append(i[0])\n",
    "    c.append(i[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd62712c",
   "metadata": {
    "papermill": {
     "duration": 0.080133,
     "end_time": "2022-03-20T07:59:33.153921",
     "exception": false,
     "start_time": "2022-03-20T07:59:33.073788",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Pie chart for Most Frequent Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aac2c0cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:33.318918Z",
     "iopub.status.busy": "2022-03-20T07:59:33.318238Z",
     "iopub.status.idle": "2022-03-20T07:59:34.432686Z",
     "shell.execute_reply": "2022-03-20T07:59:34.432057Z",
     "shell.execute_reply.started": "2022-03-20T07:15:38.072739Z"
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     "exception": false,
     "start_time": "2022-03-20T07:59:33.232934",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.pie(data, values=c, names=w, color_discrete_sequence=px.colors.sequential.RdBu)\n",
    "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
    "fig.update_layout(title=\"Most Frequent Words\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a74fe93",
   "metadata": {
    "papermill": {
     "duration": 0.079718,
     "end_time": "2022-03-20T07:59:34.589748",
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     "start_time": "2022-03-20T07:59:34.510030",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Displaying Rarewords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c572227a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:34.759575Z",
     "iopub.status.busy": "2022-03-20T07:59:34.758925Z",
     "iopub.status.idle": "2022-03-20T07:59:34.783640Z",
     "shell.execute_reply": "2022-03-20T07:59:34.783046Z",
     "shell.execute_reply.started": "2022-03-20T07:15:38.137804Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2022-03-20T07:59:34.673040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "n_rare_words = 11\n",
    "RAREWORDS = set([w for (w, wc) in cnt.most_common()[:-n_rare_words-1:-1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "95eb00a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:34.953805Z",
     "iopub.status.busy": "2022-03-20T07:59:34.953102Z",
     "iopub.status.idle": "2022-03-20T07:59:34.957365Z",
     "shell.execute_reply": "2022-03-20T07:59:34.957921Z",
     "shell.execute_reply.started": "2022-03-20T07:15:38.166701Z"
    },
    "papermill": {
     "duration": 0.094202,
     "end_time": "2022-03-20T07:59:34.958162",
     "exception": false,
     "start_time": "2022-03-20T07:59:34.863960",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1922',\n",
       " 'backgroung',\n",
       " 'don8216t',\n",
       " 'firedrake',\n",
       " 'gryphon',\n",
       " 'helos',\n",
       " 'insite',\n",
       " 'meaness',\n",
       " 'relm',\n",
       " 'symbols',\n",
       " 'twicetypos'}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RAREWORDS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "700d6fc4",
   "metadata": {
    "papermill": {
     "duration": 0.080879,
     "end_time": "2022-03-20T07:59:35.122844",
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     "start_time": "2022-03-20T07:59:35.041965",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Splitting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ad481306",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:35.295153Z",
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     "start_time": "2022-03-20T07:59:35.203883",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>book first bookmobile book buy school book clu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>read description book couldnt wait read downlo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>edit review book believe get right update rewr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>dont normally buy mystery novels dont like how...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>isnt kind book normally read although try limi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11995</th>\n",
       "      <td>1</td>\n",
       "      <td>read certain passage twicetypos wish build rel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>0</td>\n",
       "      <td>expect yet interesting book usually don8216t r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>0</td>\n",
       "      <td>dragon knight world knight ride dragon slay wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>0</td>\n",
       "      <td>since story short hard say much without give a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>0</td>\n",
       "      <td>1922 amazing collection info symbols culture a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rating                                         reviewText\n",
       "0           0  book first bookmobile book buy school book clu...\n",
       "1           1  read description book couldnt wait read downlo...\n",
       "2           0  edit review book believe get right update rewr...\n",
       "3           0  dont normally buy mystery novels dont like how...\n",
       "4           0  isnt kind book normally read although try limi...\n",
       "...       ...                                                ...\n",
       "11995       1  read certain passage twicetypos wish build rel...\n",
       "11996       0  expect yet interesting book usually don8216t r...\n",
       "11997       0  dragon knight world knight ride dragon slay wi...\n",
       "11998       0  since story short hard say much without give a...\n",
       "11999       0  1922 amazing collection info symbols culture a...\n",
       "\n",
       "[12000 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview of data\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3a908a71",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:35.467967Z",
     "iopub.status.busy": "2022-03-20T07:59:35.467262Z",
     "iopub.status.idle": "2022-03-20T07:59:35.478514Z",
     "shell.execute_reply": "2022-03-20T07:59:35.477551Z",
     "shell.execute_reply.started": "2022-03-20T07:15:38.199753Z"
    },
    "papermill": {
     "duration": 0.098499,
     "end_time": "2022-03-20T07:59:35.478792",
     "exception": false,
     "start_time": "2022-03-20T07:59:35.380293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train, test = train_test_split(data, test_size = 0.3, stratify = data['rating'], random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "30cbc2a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:35.640427Z",
     "iopub.status.busy": "2022-03-20T07:59:35.639765Z",
     "iopub.status.idle": "2022-03-20T07:59:37.043837Z",
     "shell.execute_reply": "2022-03-20T07:59:37.044370Z",
     "shell.execute_reply.started": "2022-03-20T07:15:38.218081Z"
    },
    "papermill": {
     "duration": 1.486582,
     "end_time": "2022-03-20T07:59:37.044547",
     "exception": false,
     "start_time": "2022-03-20T07:59:35.557965",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cv= CountVectorizer(binary=True, min_df = 10, max_df = 0.95)\n",
    "cv.fit_transform(train['reviewText'].values)\n",
    "train_feature_set=cv.transform(train['reviewText'].values)\n",
    "test_feature_set=cv.transform(test['reviewText'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff258d6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:37.213344Z",
     "iopub.status.busy": "2022-03-20T07:59:37.212477Z",
     "iopub.status.idle": "2022-03-20T07:59:37.216406Z",
     "shell.execute_reply": "2022-03-20T07:59:37.215770Z",
     "shell.execute_reply.started": "2022-03-20T07:15:39.668494Z"
    },
    "papermill": {
     "duration": 0.091403,
     "end_time": "2022-03-20T07:59:37.216541",
     "exception": false,
     "start_time": "2022-03-20T07:59:37.125138",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<8400x3773 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 322980 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_feature_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fd5e7199",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:37.380758Z",
     "iopub.status.busy": "2022-03-20T07:59:37.379940Z",
     "iopub.status.idle": "2022-03-20T07:59:37.383224Z",
     "shell.execute_reply": "2022-03-20T07:59:37.383729Z",
     "shell.execute_reply.started": "2022-03-20T07:15:39.679571Z"
    },
    "papermill": {
     "duration": 0.089302,
     "end_time": "2022-03-20T07:59:37.383901",
     "exception": false,
     "start_time": "2022-03-20T07:59:37.294599",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3773"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_feature_set.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8fa91488",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:37.552866Z",
     "iopub.status.busy": "2022-03-20T07:59:37.549782Z",
     "iopub.status.idle": "2022-03-20T07:59:37.555028Z",
     "shell.execute_reply": "2022-03-20T07:59:37.555530Z",
     "shell.execute_reply.started": "2022-03-20T07:15:39.698212Z"
    },
    "papermill": {
     "duration": 0.09047,
     "end_time": "2022-03-20T07:59:37.555718",
     "exception": false,
     "start_time": "2022-03-20T07:59:37.465248",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "422"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv.vocabulary_['book']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f06bf20a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:37.723921Z",
     "iopub.status.busy": "2022-03-20T07:59:37.723003Z",
     "iopub.status.idle": "2022-03-20T07:59:37.726475Z",
     "shell.execute_reply": "2022-03-20T07:59:37.725959Z",
     "shell.execute_reply.started": "2022-03-20T07:15:39.712792Z"
    },
    "papermill": {
     "duration": 0.09009,
     "end_time": "2022-03-20T07:59:37.726618",
     "exception": false,
     "start_time": "2022-03-20T07:59:37.636528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_train = train['rating'].values\n",
    "y_test = test['rating'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b032ec",
   "metadata": {
    "papermill": {
     "duration": 0.079472,
     "end_time": "2022-03-20T07:59:37.884844",
     "exception": false,
     "start_time": "2022-03-20T07:59:37.805372",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Building our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e3ff904c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:38.055560Z",
     "iopub.status.busy": "2022-03-20T07:59:38.054343Z",
     "iopub.status.idle": "2022-03-20T07:59:38.363162Z",
     "shell.execute_reply": "2022-03-20T07:59:38.362508Z",
     "shell.execute_reply.started": "2022-03-20T07:15:39.726191Z"
    },
    "papermill": {
     "duration": 0.396173,
     "end_time": "2022-03-20T07:59:38.363304",
     "exception": false,
     "start_time": "2022-03-20T07:59:37.967131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.824\n",
      "F1:  0.726\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(random_state = 42, max_iter=1000)\n",
    "lr.fit(train_feature_set,y_train)\n",
    "y_pred = lr.predict(test_feature_set)\n",
    "print(\"Accuracy: \",round(metrics.accuracy_score(y_test,y_pred),3))\n",
    "print(\"F1: \",round(metrics.f1_score(y_test, y_pred),3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a6ba593d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:38.527770Z",
     "iopub.status.busy": "2022-03-20T07:59:38.527087Z",
     "iopub.status.idle": "2022-03-20T07:59:38.533225Z",
     "shell.execute_reply": "2022-03-20T07:59:38.532408Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.085932Z"
    },
    "papermill": {
     "duration": 0.09086,
     "end_time": "2022-03-20T07:59:38.533382",
     "exception": false,
     "start_time": "2022-03-20T07:59:38.442522",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2126,  274],\n",
       "       [ 360,  840]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm1 = confusion_matrix(y_test, y_pred)\n",
    "cm1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "83a85746",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:38.704483Z",
     "iopub.status.busy": "2022-03-20T07:59:38.703796Z",
     "iopub.status.idle": "2022-03-20T07:59:38.709360Z",
     "shell.execute_reply": "2022-03-20T07:59:38.709953Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.098395Z"
    },
    "papermill": {
     "duration": 0.093376,
     "end_time": "2022-03-20T07:59:38.710130",
     "exception": false,
     "start_time": "2022-03-20T07:59:38.616754",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.88583333, 0.11416667],\n",
       "       [0.3       , 0.7       ]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm2 = confusion_matrix(y_test, y_pred,normalize='true')\n",
    "cm2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "66628ed0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:38.885283Z",
     "iopub.status.busy": "2022-03-20T07:59:38.884540Z",
     "iopub.status.idle": "2022-03-20T07:59:39.107530Z",
     "shell.execute_reply": "2022-03-20T07:59:39.106770Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.115035Z"
    },
    "papermill": {
     "duration": 0.312782,
     "end_time": "2022-03-20T07:59:39.107719",
     "exception": false,
     "start_time": "2022-03-20T07:59:38.794937",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm1,display_labels=lr.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2c69bbae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:39.283374Z",
     "iopub.status.busy": "2022-03-20T07:59:39.282370Z",
     "iopub.status.idle": "2022-03-20T07:59:39.510860Z",
     "shell.execute_reply": "2022-03-20T07:59:39.510168Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.35264Z"
    },
    "papermill": {
     "duration": 0.316839,
     "end_time": "2022-03-20T07:59:39.511026",
     "exception": false,
     "start_time": "2022-03-20T07:59:39.194187",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm2,display_labels=lr.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "25506eca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:39.685546Z",
     "iopub.status.busy": "2022-03-20T07:59:39.684865Z",
     "iopub.status.idle": "2022-03-20T07:59:39.694903Z",
     "shell.execute_reply": "2022-03-20T07:59:39.695523Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.596464Z"
    },
    "papermill": {
     "duration": 0.100155,
     "end_time": "2022-03-20T07:59:39.695721",
     "exception": false,
     "start_time": "2022-03-20T07:59:39.595566",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature:  099 Score:  0.17009113456126124\n",
      "Feature:  10 Score:  0.32618786733952304\n",
      "Feature:  100 Score:  0.48837010936314723\n",
      "Feature:  11 Score:  -0.38814976764855386\n",
      "Feature:  12 Score:  -0.46210531282054784\n",
      "Feature:  13 Score:  0.8430789988824341\n",
      "Feature:  14 Score:  -0.6044090334662856\n",
      "Feature:  15 Score:  0.9184474143072441\n",
      "Feature:  16 Score:  0.4628201502148334\n",
      "Feature:  17 Score:  0.41937803852545696\n"
     ]
    }
   ],
   "source": [
    "feature_importance = lr.coef_[0][:10]\n",
    "for i,v in enumerate(feature_importance):\n",
    "    print('Feature: ', list(cv.vocabulary_.keys())[list(cv.vocabulary_.values()).index(i)], 'Score: ', v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b18c9f22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:39.869519Z",
     "iopub.status.busy": "2022-03-20T07:59:39.868823Z",
     "iopub.status.idle": "2022-03-20T07:59:39.871560Z",
     "shell.execute_reply": "2022-03-20T07:59:39.872207Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.617997Z"
    },
    "papermill": {
     "duration": 0.092145,
     "end_time": "2022-03-20T07:59:39.872399",
     "exception": false,
     "start_time": "2022-03-20T07:59:39.780254",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "feature_importance = lr.coef_[0]\n",
    "sorted_idx = np.argsort(feature_importance)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc3b9dc",
   "metadata": {
    "papermill": {
     "duration": 0.085057,
     "end_time": "2022-03-20T07:59:40.042453",
     "exception": false,
     "start_time": "2022-03-20T07:59:39.957396",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Top words for the positive class (negative sentiment):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "ddf6b2be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:40.220520Z",
     "iopub.status.busy": "2022-03-20T07:59:40.214439Z",
     "iopub.status.idle": "2022-03-20T07:59:40.229947Z",
     "shell.execute_reply": "2022-03-20T07:59:40.229233Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.625683Z"
    },
    "papermill": {
     "duration": 0.103009,
     "end_time": "2022-03-20T07:59:40.230115",
     "exception": false,
     "start_time": "2022-03-20T07:59:40.127106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['waste', 'delete', 'cardboard', 'sorry', 'depress', 'boring', '25', 'thin', 'ugh', 'weird']\n"
     ]
    }
   ],
   "source": [
    "top_10_pos_w = [list(cv.vocabulary_.keys())[list(cv.vocabulary_.values()).index(w)] for w in sorted_idx[range(-1,-11, -1)]]\n",
    "print(top_10_pos_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "155de8e1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:40.411207Z",
     "iopub.status.busy": "2022-03-20T07:59:40.410519Z",
     "iopub.status.idle": "2022-03-20T07:59:40.700137Z",
     "shell.execute_reply": "2022-03-20T07:59:40.700647Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.649081Z"
    },
    "papermill": {
     "duration": 0.383032,
     "end_time": "2022-03-20T07:59:40.700846",
     "exception": false,
     "start_time": "2022-03-20T07:59:40.317814",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x=top_10_pos_w, y=feature_importance[sorted_idx[range(-1,-11, -1)]])\n",
    "plt.title(\"Most Important Words Used for Negative Sentiment\",fontsize = 20)\n",
    "x_locs,x_labels = plt.xticks()\n",
    "plt.setp(x_labels, rotation = 40)\n",
    "plt.ylabel('Feature Importance', fontsize = 12)\n",
    "plt.xlabel('Word', fontsize = 12);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2099fb1",
   "metadata": {
    "papermill": {
     "duration": 0.086429,
     "end_time": "2022-03-20T07:59:40.875074",
     "exception": false,
     "start_time": "2022-03-20T07:59:40.788645",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Top words for the negative class (positive sentiment):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cb0cc9b4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:41.059870Z",
     "iopub.status.busy": "2022-03-20T07:59:41.059144Z",
     "iopub.status.idle": "2022-03-20T07:59:41.066175Z",
     "shell.execute_reply": "2022-03-20T07:59:41.065500Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.972635Z"
    },
    "papermill": {
     "duration": 0.106417,
     "end_time": "2022-03-20T07:59:41.066319",
     "exception": false,
     "start_time": "2022-03-20T07:59:40.959902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['enjoyed', 'verne', 'enjoyable', 'shot', 'fun', 'thumb', 'hunter', 'thrill', 'loved', 'hot']\n"
     ]
    }
   ],
   "source": [
    "top_10_neg_w = [list(cv.vocabulary_.keys())[list(cv.vocabulary_.values()).index(w)] for w in sorted_idx[:10]]\n",
    "print(top_10_neg_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d8d0f948",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:41.244972Z",
     "iopub.status.busy": "2022-03-20T07:59:41.244237Z",
     "iopub.status.idle": "2022-03-20T07:59:41.529675Z",
     "shell.execute_reply": "2022-03-20T07:59:41.529041Z",
     "shell.execute_reply.started": "2022-03-20T07:15:40.990334Z"
    },
    "papermill": {
     "duration": 0.376773,
     "end_time": "2022-03-20T07:59:41.529862",
     "exception": false,
     "start_time": "2022-03-20T07:59:41.153089",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = sns.barplot(x=top_10_neg_w, y=feature_importance[sorted_idx[:10]])\n",
    "plt.title(\"Most Important Words Used for Positive Sentiment\",fontsize = 20)\n",
    "x_locs,x_labels = plt.xticks()\n",
    "plt.setp(x_labels, rotation = 40)\n",
    "plt.ylabel('Feature Importance', fontsize = 12)\n",
    "plt.xlabel('Word', fontsize = 12);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e5ab03",
   "metadata": {
    "papermill": {
     "duration": 0.091944,
     "end_time": "2022-03-20T07:59:41.713185",
     "exception": false,
     "start_time": "2022-03-20T07:59:41.621241",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Prediction on our sentences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "1fceaef9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:41.900086Z",
     "iopub.status.busy": "2022-03-20T07:59:41.899383Z",
     "iopub.status.idle": "2022-03-20T07:59:41.902679Z",
     "shell.execute_reply": "2022-03-20T07:59:41.903244Z",
     "shell.execute_reply.started": "2022-03-20T07:15:41.312777Z"
    },
    "papermill": {
     "duration": 0.099359,
     "end_time": "2022-03-20T07:59:41.903420",
     "exception": false,
     "start_time": "2022-03-20T07:59:41.804061",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.classes_#negative class first, positive class next"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ab13f918",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-20T07:59:42.084785Z",
     "iopub.status.busy": "2022-03-20T07:59:42.084099Z",
     "iopub.status.idle": "2022-03-20T07:59:42.093527Z",
     "shell.execute_reply": "2022-03-20T07:59:42.092223Z",
     "shell.execute_reply.started": "2022-03-20T07:15:41.320499Z"
    },
    "papermill": {
     "duration": 0.101989,
     "end_time": "2022-03-20T07:59:42.093729",
     "exception": false,
     "start_time": "2022-03-20T07:59:41.991740",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prob are: [[0.78784978 0.21215022]]\n",
      "prediction are: [0]\n"
     ]
    }
   ],
   "source": [
    "test_review = cv.transform([\"I did not enjoy the book\"])\n",
    "p = lr.predict_proba(test_review)\n",
    "s = lr.predict(test_review)\n",
    "print(\"prob are:\",p)\n",
    "print(\"prediction are:\",s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12b8f89a",
   "metadata": {
    "papermill": {
     "duration": 0.089132,
     "end_time": "2022-03-20T07:59:42.274417",
     "exception": false,
     "start_time": "2022-03-20T07:59:42.185285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- hyperparameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2d3fda80",
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     "status": "completed"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "******** For i = 0.3 ******\n",
      "F1: 0.735\n",
      "\n",
      "******** For i = 0.4 ******\n",
      "F1: 0.733\n",
      "\n",
      "******** For i = 0.45 ******\n",
      "F1: 0.733\n",
      "\n",
      "******** For i = 0.5 ******\n",
      "F1: 0.726\n"
     ]
    }
   ],
   "source": [
    "pred_proba_df = pd.DataFrame(lr.predict_proba(test_feature_set))\n",
    "threshold_list = [0.3,0.4,0.45,0.5]\n",
    "for i in threshold_list:\n",
    "    print ('\\n******** For i = {} ******'.format(i))\n",
    "    Y_test_pred = pred_proba_df.applymap(lambda x: 1 if x>i else 0)\n",
    "    test_f1 = round(metrics.f1_score(y_test, Y_test_pred.loc[:,1].values),3)\n",
    "    print('F1: {}'.format(test_f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0104721",
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     "status": "completed"
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    "tags": []
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   "source": [
    "# Recap: \n",
    "- We performed Sentiment Analysis on Amazon Kindle Review using Logistic Regression.\n",
    "- We performed EDA.\n",
    "- We performed Preprocessing steps for cleaning our dataset.\n",
    "- Then we spilt our dataset into training and testing.\n",
    "- We build our logistic regression model.\n",
    "- Then we train our model with training dataset.\n",
    "- We also evaulated our model using testing dataset and also used various evualation metrics.\n",
    "- And then tried our own sentence to test our model.\n",
    "- Then we plotted Most Important Words Used for Negative Sentiment as well as Most Important Words Used for Postive Sentiment\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cef66a1",
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    },
    "tags": []
   },
   "source": [
    "# [View my verious sentiment analysis notebook on the same topic](https://www.kaggle.com/code/meetnagadia/amazon-kindle-book-sentiment-analysis)"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
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   "source": [
    "![](https://c.tenor.com/WGo9iYM9V2kAAAAC/the-end-background.gif)"
   ]
  }
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