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Sentimental_Analysis_WV.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f69cc67d56ccf08c4208706785844efee4f62b8c40bd81679463144c43b5dd9a
+size 66702354

Sentimental_Analysis_Word2Vec.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3e8bfe2042ff742ab8bd443620b3cca1f910ee3e69d8c15a01ef3a762b4be2d1
+size 62052951

nlp-bow-ngrams-word2vec.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:16.293152Z",
+     "iopub.status.busy": "2024-05-28T14:52:16.292540Z",
+     "iopub.status.idle": "2024-05-28T14:52:17.111429Z",
+     "shell.execute_reply": "2024-05-28T14:52:17.110616Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:16.293118Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:17.113325Z",
+     "iopub.status.busy": "2024-05-28T14:52:17.112978Z",
+     "iopub.status.idle": "2024-05-28T14:52:18.294078Z",
+     "shell.execute_reply": "2024-05-28T14:52:18.293195Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:17.113302Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(\"/kaggle/input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:25:43.527134Z",
+     "iopub.status.busy": "2024-05-28T14:25:43.526863Z",
+     "iopub.status.idle": "2024-05-28T14:25:43.531421Z",
+     "shell.execute_reply": "2024-05-28T14:25:43.530388Z",
+     "shell.execute_reply.started": "2024-05-28T14:25:43.527112Z"
+    }
+   },
+   "source": [
+    "df=df.head(10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:18.295520Z",
+     "iopub.status.busy": "2024-05-28T14:52:18.295184Z",
+     "iopub.status.idle": "2024-05-28T14:52:18.320913Z",
+     "shell.execute_reply": "2024-05-28T14:52:18.320001Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:18.295492Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sentiment\n",
+       "positive    25000\n",
+       "negative    25000\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['sentiment'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:18.324117Z",
+     "iopub.status.busy": "2024-05-28T14:52:18.323673Z",
+     "iopub.status.idle": "2024-05-28T14:52:18.340689Z",
+     "shell.execute_reply": "2024-05-28T14:52:18.339777Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:18.324090Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "review       0\n",
+       "sentiment    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:18.342289Z",
+     "iopub.status.busy": "2024-05-28T14:52:18.341941Z",
+     "iopub.status.idle": "2024-05-28T14:52:18.511426Z",
+     "shell.execute_reply": "2024-05-28T14:52:18.510510Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:18.342257Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "418"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:18.513001Z",
+     "iopub.status.busy": "2024-05-28T14:52:18.512633Z",
+     "iopub.status.idle": "2024-05-28T14:52:18.662976Z",
+     "shell.execute_reply": "2024-05-28T14:52:18.662258Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:18.512969Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df=df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:18.664321Z",
+     "iopub.status.busy": "2024-05-28T14:52:18.664037Z",
+     "iopub.status.idle": "2024-05-28T14:52:18.811830Z",
+     "shell.execute_reply": "2024-05-28T14:52:18.810966Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:18.664297Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Removing HTML Tags**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:18.813219Z",
+     "iopub.status.busy": "2024-05-28T14:52:18.812944Z",
+     "iopub.status.idle": "2024-05-28T14:52:18.817734Z",
+     "shell.execute_reply": "2024-05-28T14:52:18.816871Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:18.813195Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "def remove_tags(text):\n",
+    "    return re.sub(re.compile('<.*?>'),'',text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:18.819205Z",
+     "iopub.status.busy": "2024-05-28T14:52:18.818864Z",
+     "iopub.status.idle": "2024-05-28T14:52:19.094654Z",
+     "shell.execute_reply": "2024-05-28T14:52:19.093668Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:18.819173Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['review']=df['review'].apply(remove_tags)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:19.099305Z",
+     "iopub.status.busy": "2024-05-28T14:52:19.099017Z",
+     "iopub.status.idle": "2024-05-28T14:52:19.112472Z",
+     "shell.execute_reply": "2024-05-28T14:52:19.111531Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:19.099280Z"
+    }
+   },
+   "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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>One of the other reviewers has mentioned that ...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>A wonderful little production. The filming tec...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Basically there's a family where a little boy ...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              review sentiment\n",
+       "0  One of the other reviewers has mentioned that ...  positive\n",
+       "1  A wonderful little production. The filming tec...  positive\n",
+       "2  I thought this was a wonderful way to spend ti...  positive\n",
+       "3  Basically there's a family where a little boy ...  negative\n",
+       "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Lowercase**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:19.113977Z",
+     "iopub.status.busy": "2024-05-28T14:52:19.113619Z",
+     "iopub.status.idle": "2024-05-28T14:52:19.299489Z",
+     "shell.execute_reply": "2024-05-28T14:52:19.298706Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:19.113944Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['review']=df['review'].apply(lambda x:x.lower())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:19.300853Z",
+     "iopub.status.busy": "2024-05-28T14:52:19.300570Z",
+     "iopub.status.idle": "2024-05-28T14:52:19.311359Z",
+     "shell.execute_reply": "2024-05-28T14:52:19.310381Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:19.300827Z"
+    }
+   },
+   "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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>one of the other reviewers has mentioned that ...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>a wonderful little production. the filming tec...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>i thought this was a wonderful way to spend ti...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>basically there's a family where a little boy ...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>petter mattei's \"love in the time of money\" is...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              review sentiment\n",
+       "0  one of the other reviewers has mentioned that ...  positive\n",
+       "1  a wonderful little production. the filming tec...  positive\n",
+       "2  i thought this was a wonderful way to spend ti...  positive\n",
+       "3  basically there's a family where a little boy ...  negative\n",
+       "4  petter mattei's \"love in the time of money\" is...  positive"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Removing Stopwords**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:19.313147Z",
+     "iopub.status.busy": "2024-05-28T14:52:19.312754Z",
+     "iopub.status.idle": "2024-05-28T14:52:20.687218Z",
+     "shell.execute_reply": "2024-05-28T14:52:20.686234Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:19.313113Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from nltk.corpus import stopwords"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:20.688722Z",
+     "iopub.status.busy": "2024-05-28T14:52:20.688422Z",
+     "iopub.status.idle": "2024-05-28T14:52:20.695776Z",
+     "shell.execute_reply": "2024-05-28T14:52:20.694869Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:20.688690Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sw_list=stopwords.words('english')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:20.697662Z",
+     "iopub.status.busy": "2024-05-28T14:52:20.696910Z",
+     "iopub.status.idle": "2024-05-28T14:52:41.103845Z",
+     "shell.execute_reply": "2024-05-28T14:52:41.103006Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:20.697626Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['review']=df['review'].apply(lambda x:[item for item in x.split() if item not in sw_list]).apply(lambda x:\" \".join(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:41.105206Z",
+     "iopub.status.busy": "2024-05-28T14:52:41.104945Z",
+     "iopub.status.idle": "2024-05-28T14:52:41.115077Z",
+     "shell.execute_reply": "2024-05-28T14:52:41.114149Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:41.105183Z"
+    }
+   },
+   "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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>one reviewers mentioned watching 1 oz episode ...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>wonderful little production. filming technique...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>thought wonderful way spend time hot summer we...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>basically there's family little boy (jake) thi...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>petter mattei's \"love time money\" visually stu...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              review sentiment\n",
+       "0  one reviewers mentioned watching 1 oz episode ...  positive\n",
+       "1  wonderful little production. filming technique...  positive\n",
+       "2  thought wonderful way spend time hot summer we...  positive\n",
+       "3  basically there's family little boy (jake) thi...  negative\n",
+       "4  petter mattei's \"love time money\" visually stu...  positive"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Removing Numbers**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:41.116693Z",
+     "iopub.status.busy": "2024-05-28T14:52:41.116370Z",
+     "iopub.status.idle": "2024-05-28T14:52:42.350128Z",
+     "shell.execute_reply": "2024-05-28T14:52:42.349082Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:41.116666Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['review']=df['review'].apply(lambda x:' '.join([i for i in x.split() if not i.isdigit()]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:42.352200Z",
+     "iopub.status.busy": "2024-05-28T14:52:42.351476Z",
+     "iopub.status.idle": "2024-05-28T14:52:42.361695Z",
+     "shell.execute_reply": "2024-05-28T14:52:42.360686Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:42.352154Z"
+    }
+   },
+   "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",
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+       "\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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>one reviewers mentioned watching oz episode ho...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>wonderful little production. filming technique...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>thought wonderful way spend time hot summer we...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>basically there's family little boy (jake) thi...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>petter mattei's \"love time money\" visually stu...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              review sentiment\n",
+       "0  one reviewers mentioned watching oz episode ho...  positive\n",
+       "1  wonderful little production. filming technique...  positive\n",
+       "2  thought wonderful way spend time hot summer we...  positive\n",
+       "3  basically there's family little boy (jake) thi...  negative\n",
+       "4  petter mattei's \"love time money\" visually stu...  positive"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Removing Punctuation**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:42.363205Z",
+     "iopub.status.busy": "2024-05-28T14:52:42.362921Z",
+     "iopub.status.idle": "2024-05-28T14:52:42.369773Z",
+     "shell.execute_reply": "2024-05-28T14:52:42.368884Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:42.363181Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import string\n",
+    "PUNCT_TO_REMOVE = string.punctuation\n",
+    "def remove_punctuation(text):\n",
+    "    return text.translate(str.maketrans('', '', PUNCT_TO_REMOVE))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:42.371097Z",
+     "iopub.status.busy": "2024-05-28T14:52:42.370761Z",
+     "iopub.status.idle": "2024-05-28T14:52:43.206495Z",
+     "shell.execute_reply": "2024-05-28T14:52:43.205666Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:42.371071Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['review']=df['review'].apply(remove_punctuation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:43.207858Z",
+     "iopub.status.busy": "2024-05-28T14:52:43.207590Z",
+     "iopub.status.idle": "2024-05-28T14:52:43.217082Z",
+     "shell.execute_reply": "2024-05-28T14:52:43.216131Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:43.207835Z"
+    }
+   },
+   "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>review</th>\n",
+       "      <th>sentiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>one reviewers mentioned watching oz episode ho...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>wonderful little production filming technique ...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>thought wonderful way spend time hot summer we...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>basically theres family little boy jake thinks...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>petter matteis love time money visually stunni...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                              review sentiment\n",
+       "0  one reviewers mentioned watching oz episode ho...  positive\n",
+       "1  wonderful little production filming technique ...  positive\n",
+       "2  thought wonderful way spend time hot summer we...  positive\n",
+       "3  basically theres family little boy jake thinks...  negative\n",
+       "4  petter matteis love time money visually stunni...  positive"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# **Removing Contractions**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:43.218467Z",
+     "iopub.status.busy": "2024-05-28T14:52:43.218186Z",
+     "iopub.status.idle": "2024-05-28T14:52:56.917289Z",
+     "shell.execute_reply": "2024-05-28T14:52:56.916286Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:43.218441Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: contractions in /opt/conda/lib/python3.10/site-packages (0.1.73)\n",
+      "Requirement already satisfied: textsearch>=0.0.21 in /opt/conda/lib/python3.10/site-packages (from contractions) (0.0.24)\n",
+      "Requirement already satisfied: anyascii in /opt/conda/lib/python3.10/site-packages (from textsearch>=0.0.21->contractions) (0.3.2)\n",
+      "Requirement already satisfied: pyahocorasick in /opt/conda/lib/python3.10/site-packages (from textsearch>=0.0.21->contractions) (2.1.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install contractions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:56.919161Z",
+     "iopub.status.busy": "2024-05-28T14:52:56.918839Z",
+     "iopub.status.idle": "2024-05-28T14:52:56.944515Z",
+     "shell.execute_reply": "2024-05-28T14:52:56.943830Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:56.919130Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import contractions\n",
+    "def remove_contractions(text):\n",
+    "    return contractions.fix(text)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:52:56.945783Z",
+     "iopub.status.busy": "2024-05-28T14:52:56.945518Z",
+     "iopub.status.idle": "2024-05-28T14:53:00.977406Z",
+     "shell.execute_reply": "2024-05-28T14:53:00.976592Z",
+     "shell.execute_reply.started": "2024-05-28T14:52:56.945760Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df['review']=df['review'].apply(remove_contractions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:00.978856Z",
+     "iopub.status.busy": "2024-05-28T14:53:00.978551Z",
+     "iopub.status.idle": "2024-05-28T14:53:00.991438Z",
+     "shell.execute_reply": "2024-05-28T14:53:00.990549Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:00.978830Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<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",
+       "    .dataframe thead th {\n",
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+       "  <thead>\n",
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+       "      <th></th>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>wonderful little production filming technique ...</td>\n",
+       "      <td>positive</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>thought wonderful way spend time hot summer we...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>basically there is family little boy jake thin...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>petter matteis love time money visually stunni...</td>\n",
+       "      <td>positive</td>\n",
+       "    </tr>\n",
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+       "    <tr>\n",
+       "      <th>49995</th>\n",
+       "      <td>thought movie right good job creative original...</td>\n",
+       "      <td>positive</td>\n",
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+       "    <tr>\n",
+       "      <th>49996</th>\n",
+       "      <td>bad plot bad dialogue bad acting idiotic direc...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49997</th>\n",
+       "      <td>catholic taught parochial elementary schools n...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49998</th>\n",
+       "      <td>i am going disagree previous comment side malt...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49999</th>\n",
+       "      <td>one expects star trek movies high art fans exp...</td>\n",
+       "      <td>negative</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>49582 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  review sentiment\n",
+       "0      one reviewers mentioned watching oz episode ho...  positive\n",
+       "1      wonderful little production filming technique ...  positive\n",
+       "2      thought wonderful way spend time hot summer we...  positive\n",
+       "3      basically there is family little boy jake thin...  negative\n",
+       "4      petter matteis love time money visually stunni...  positive\n",
+       "...                                                  ...       ...\n",
+       "49995  thought movie right good job creative original...  positive\n",
+       "49996  bad plot bad dialogue bad acting idiotic direc...  negative\n",
+       "49997  catholic taught parochial elementary schools n...  negative\n",
+       "49998  i am going disagree previous comment side malt...  negative\n",
+       "49999  one expects star trek movies high art fans exp...  negative\n",
+       "\n",
+       "[49582 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:00.993229Z",
+     "iopub.status.busy": "2024-05-28T14:53:00.992777Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.001576Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.000876Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:00.993151Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x=df.drop(columns='sentiment')\n",
+    "y=df['sentiment']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.003284Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.002910Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.014799Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.013876Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.003260Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "      <th></th>\n",
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+       "    <tr>\n",
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+       "      <th>49995</th>\n",
+       "      <td>thought movie right good job creative original...</td>\n",
+       "    </tr>\n",
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+       "      <th>49996</th>\n",
+       "      <td>bad plot bad dialogue bad acting idiotic direc...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49997</th>\n",
+       "      <td>catholic taught parochial elementary schools n...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49998</th>\n",
+       "      <td>i am going disagree previous comment side malt...</td>\n",
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+       "      <th>49999</th>\n",
+       "      <td>one expects star trek movies high art fans exp...</td>\n",
+       "    </tr>\n",
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+       "</table>\n",
+       "<p>49582 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  review\n",
+       "0      one reviewers mentioned watching oz episode ho...\n",
+       "1      wonderful little production filming technique ...\n",
+       "2      thought wonderful way spend time hot summer we...\n",
+       "3      basically there is family little boy jake thin...\n",
+       "4      petter matteis love time money visually stunni...\n",
+       "...                                                  ...\n",
+       "49995  thought movie right good job creative original...\n",
+       "49996  bad plot bad dialogue bad acting idiotic direc...\n",
+       "49997  catholic taught parochial elementary schools n...\n",
+       "49998  i am going disagree previous comment side malt...\n",
+       "49999  one expects star trek movies high art fans exp...\n",
+       "\n",
+       "[49582 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.022597Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.022330Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.030503Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.029518Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.022574Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        positive\n",
+       "1        positive\n",
+       "2        positive\n",
+       "3        negative\n",
+       "4        positive\n",
+       "           ...   \n",
+       "49995    positive\n",
+       "49996    negative\n",
+       "49997    negative\n",
+       "49998    negative\n",
+       "49999    negative\n",
+       "Name: sentiment, Length: 49582, dtype: object"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.032109Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.031731Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.037243Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.036374Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.032084Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.038703Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.038403Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.057333Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.056511Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.038679Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y=LabelEncoder().fit_transform(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.058600Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.058345Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.067016Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.066135Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.058579Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, ..., 0, 0, 0])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.068374Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.068086Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.099626Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.098771Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.068341Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=3,stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.101218Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.100870Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.106109Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.105161Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.101184Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(39665, 1) (9917, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(x_train.shape,x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bag of Word"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.107866Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.107441Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.113900Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.112939Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.107829Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.115218Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.114952Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.121742Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.120751Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.115195Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cv=CountVectorizer(max_features=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.123124Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.122830Z",
+     "iopub.status.idle": "2024-05-28T14:53:01.134108Z",
+     "shell.execute_reply": "2024-05-28T14:53:01.133166Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.123101Z"
+    }
+   },
+   "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>review</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17185</th>\n",
+       "      <td>watching avalon which decent nice digital fx s...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12989</th>\n",
+       "      <td>rarely denzil washington make bad movie come t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31628</th>\n",
+       "      <td>think movie reasonbaly good kind of weird olse...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12399</th>\n",
+       "      <td>movie is horrible wonderful time first saw yea...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33230</th>\n",
+       "      <td>watching the bodyguard last night felt compell...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31515</th>\n",
+       "      <td>good cast with one major exception pushes way ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19133</th>\n",
+       "      <td>seldom see short comments written imdb filmgoe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47930</th>\n",
+       "      <td>say without shadow doubt going overboard singl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35145</th>\n",
+       "      <td>wife watched dvring encore action past week wo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32654</th>\n",
+       "      <td>pokemon little three four episodes tv series s...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>39665 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  review\n",
+       "17185  watching avalon which decent nice digital fx s...\n",
+       "12989  rarely denzil washington make bad movie come t...\n",
+       "31628  think movie reasonbaly good kind of weird olse...\n",
+       "12399  movie is horrible wonderful time first saw yea...\n",
+       "33230  watching the bodyguard last night felt compell...\n",
+       "...                                                  ...\n",
+       "31515  good cast with one major exception pushes way ...\n",
+       "19133  seldom see short comments written imdb filmgoe...\n",
+       "47930  say without shadow doubt going overboard singl...\n",
+       "35145  wife watched dvring encore action past week wo...\n",
+       "32654  pokemon little three four episodes tv series s...\n",
+       "\n",
+       "[39665 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:01.135399Z",
+     "iopub.status.busy": "2024-05-28T14:53:01.135133Z",
+     "iopub.status.idle": "2024-05-28T14:53:10.557535Z",
+     "shell.execute_reply": "2024-05-28T14:53:10.556708Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:01.135377Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train=cv.fit_transform(x_train['review']).toarray()\n",
+    "x_test=cv.transform(x_test['review']).toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:10.559169Z",
+     "iopub.status.busy": "2024-05-28T14:53:10.558796Z",
+     "iopub.status.idle": "2024-05-28T14:53:10.565563Z",
+     "shell.execute_reply": "2024-05-28T14:53:10.564604Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:10.559135Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(39665, 10000)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Applying NaiveBayes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:10.567583Z",
+     "iopub.status.busy": "2024-05-28T14:53:10.566619Z",
+     "iopub.status.idle": "2024-05-28T14:53:16.857125Z",
+     "shell.execute_reply": "2024-05-28T14:53:16.856146Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:10.567557Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "GaussianNB()"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "gnb=GaussianNB()\n",
+    "gnb.fit(x_train,y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:16.860063Z",
+     "iopub.status.busy": "2024-05-28T14:53:16.858411Z",
+     "iopub.status.idle": "2024-05-28T14:53:18.286607Z",
+     "shell.execute_reply": "2024-05-28T14:53:18.285709Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:16.860034Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y_pred=gnb.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:18.288847Z",
+     "iopub.status.busy": "2024-05-28T14:53:18.288416Z",
+     "iopub.status.idle": "2024-05-28T14:53:18.293343Z",
+     "shell.execute_reply": "2024-05-28T14:53:18.292339Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:18.288789Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score,confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:18.295253Z",
+     "iopub.status.busy": "2024-05-28T14:53:18.294781Z",
+     "iopub.status.idle": "2024-05-28T14:53:18.305801Z",
+     "shell.execute_reply": "2024-05-28T14:53:18.304884Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:18.295215Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7354038519713623"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:18.307732Z",
+     "iopub.status.busy": "2024-05-28T14:53:18.307121Z",
+     "iopub.status.idle": "2024-05-28T14:53:18.316221Z",
+     "shell.execute_reply": "2024-05-28T14:53:18.315238Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:18.307697Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4276,  664],\n",
+       "       [1960, 3017]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:53:18.317650Z",
+     "iopub.status.busy": "2024-05-28T14:53:18.317380Z",
+     "iopub.status.idle": "2024-05-28T14:55:29.097680Z",
+     "shell.execute_reply": "2024-05-28T14:55:29.096708Z",
+     "shell.execute_reply.started": "2024-05-28T14:53:18.317628Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8426943632146818"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "rf=RandomForestClassifier()\n",
+    "rf.fit(x_train,y_train)\n",
+    "y_pred=rf.predict(x_test)\n",
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:55:29.099391Z",
+     "iopub.status.busy": "2024-05-28T14:55:29.099103Z",
+     "iopub.status.idle": "2024-05-28T14:55:29.108811Z",
+     "shell.execute_reply": "2024-05-28T14:55:29.107863Z",
+     "shell.execute_reply.started": "2024-05-28T14:55:29.099364Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4152,  788],\n",
+       "       [ 772, 4205]])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# N_Grams"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:55:29.110803Z",
+     "iopub.status.busy": "2024-05-28T14:55:29.110043Z",
+     "iopub.status.idle": "2024-05-28T14:55:29.372362Z",
+     "shell.execute_reply": "2024-05-28T14:55:29.371484Z",
+     "shell.execute_reply.started": "2024-05-28T14:55:29.110765Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=3,stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:55:29.373855Z",
+     "iopub.status.busy": "2024-05-28T14:55:29.373553Z",
+     "iopub.status.idle": "2024-05-28T14:55:29.393260Z",
+     "shell.execute_reply": "2024-05-28T14:55:29.392224Z",
+     "shell.execute_reply.started": "2024-05-28T14:55:29.373828Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cv=CountVectorizer(ngram_range=(1,2),max_features=10000)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:55:29.394786Z",
+     "iopub.status.busy": "2024-05-28T14:55:29.394486Z",
+     "iopub.status.idle": "2024-05-28T14:56:00.578967Z",
+     "shell.execute_reply": "2024-05-28T14:56:00.577883Z",
+     "shell.execute_reply.started": "2024-05-28T14:55:29.394758Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train=cv.fit_transform(x_train['review']).toarray()\n",
+    "x_test=cv.transform(x_test['review']).toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:56:00.580808Z",
+     "iopub.status.busy": "2024-05-28T14:56:00.580266Z",
+     "iopub.status.idle": "2024-05-28T14:58:18.070996Z",
+     "shell.execute_reply": "2024-05-28T14:58:18.069821Z",
+     "shell.execute_reply.started": "2024-05-28T14:56:00.580771Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.846324493294343"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "rf=RandomForestClassifier()\n",
+    "rf.fit(x_train,y_train)\n",
+    "y_pred=rf.predict(x_test)\n",
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:58:18.072639Z",
+     "iopub.status.busy": "2024-05-28T14:58:18.072319Z",
+     "iopub.status.idle": "2024-05-28T14:58:18.081205Z",
+     "shell.execute_reply": "2024-05-28T14:58:18.080365Z",
+     "shell.execute_reply.started": "2024-05-28T14:58:18.072613Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4178,  762],\n",
+       "       [ 762, 4215]])"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Saving and Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:01:45.937561Z",
+     "iopub.status.busy": "2024-05-28T15:01:45.937238Z",
+     "iopub.status.idle": "2024-05-28T15:01:46.088033Z",
+     "shell.execute_reply": "2024-05-28T15:01:46.087204Z",
+     "shell.execute_reply.started": "2024-05-28T15:01:45.937533Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "# save the iris classification model as a pickle file\n",
+    "model_pkl_file = \"Sentimental_Analysis1.pkl\"  \n",
+    "\n",
+    "with open(model_pkl_file, 'wb') as file:  \n",
+    "    pickle.dump(rf, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:01:46.090237Z",
+     "iopub.status.busy": "2024-05-28T15:01:46.089930Z",
+     "iopub.status.idle": "2024-05-28T15:01:46.801994Z",
+     "shell.execute_reply": "2024-05-28T15:01:46.800807Z",
+     "shell.execute_reply.started": "2024-05-28T15:01:46.090212Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.844711102147827"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with open(model_pkl_file, 'rb') as file:  \n",
+    "    rf = pickle.load(file)\n",
+    "y_pred=rf.predict(x_test)\n",
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:58:19.511224Z",
+     "iopub.status.busy": "2024-05-28T14:58:19.510906Z",
+     "iopub.status.idle": "2024-05-28T14:58:19.785147Z",
+     "shell.execute_reply": "2024-05-28T14:58:19.784066Z",
+     "shell.execute_reply.started": "2024-05-28T14:58:19.511197Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=3,stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# TF_IDF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:58:19.787439Z",
+     "iopub.status.busy": "2024-05-28T14:58:19.786737Z",
+     "iopub.status.idle": "2024-05-28T14:58:19.792198Z",
+     "shell.execute_reply": "2024-05-28T14:58:19.791132Z",
+     "shell.execute_reply.started": "2024-05-28T14:58:19.787387Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:58:53.029215Z",
+     "iopub.status.busy": "2024-05-28T14:58:53.028431Z",
+     "iopub.status.idle": "2024-05-28T14:58:53.033696Z",
+     "shell.execute_reply": "2024-05-28T14:58:53.032603Z",
+     "shell.execute_reply.started": "2024-05-28T14:58:53.029178Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tfidf=TfidfVectorizer(max_features=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:58:58.538248Z",
+     "iopub.status.busy": "2024-05-28T14:58:58.537480Z",
+     "iopub.status.idle": "2024-05-28T14:59:09.408751Z",
+     "shell.execute_reply": "2024-05-28T14:59:09.407944Z",
+     "shell.execute_reply.started": "2024-05-28T14:58:58.538211Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train=tfidf.fit_transform(x_train['review']).toarray()\n",
+    "x_test=tfidf.transform(x_test['review'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T14:59:18.888515Z",
+     "iopub.status.busy": "2024-05-28T14:59:18.888049Z",
+     "iopub.status.idle": "2024-05-28T15:01:45.924455Z",
+     "shell.execute_reply": "2024-05-28T15:01:45.923366Z",
+     "shell.execute_reply.started": "2024-05-28T14:59:18.888481Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.844711102147827"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf=RandomForestClassifier()\n",
+    "rf.fit(x_train,y_train)\n",
+    "y_pred=rf.predict(x_test)\n",
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:01:45.926453Z",
+     "iopub.status.busy": "2024-05-28T15:01:45.926143Z",
+     "iopub.status.idle": "2024-05-28T15:01:45.935972Z",
+     "shell.execute_reply": "2024-05-28T15:01:45.934872Z",
+     "shell.execute_reply.started": "2024-05-28T15:01:45.926419Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4182,  758],\n",
+       "       [ 782, 4195]])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:01:46.804239Z",
+     "iopub.status.busy": "2024-05-28T15:01:46.803156Z",
+     "iopub.status.idle": "2024-05-28T15:01:47.032951Z",
+     "shell.execute_reply": "2024-05-28T15:01:47.031995Z",
+     "shell.execute_reply.started": "2024-05-28T15:01:46.804189Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=3,stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Word2Vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:04:53.247003Z",
+     "iopub.status.busy": "2024-05-28T15:04:53.246571Z",
+     "iopub.status.idle": "2024-05-28T15:05:04.199287Z",
+     "shell.execute_reply": "2024-05-28T15:05:04.198486Z",
+     "shell.execute_reply.started": "2024-05-28T15:04:53.246970Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import gensim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:05:48.076456Z",
+     "iopub.status.busy": "2024-05-28T15:05:48.076082Z",
+     "iopub.status.idle": "2024-05-28T15:05:48.080852Z",
+     "shell.execute_reply": "2024-05-28T15:05:48.079878Z",
+     "shell.execute_reply.started": "2024-05-28T15:05:48.076427Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from nltk import sent_tokenize\n",
+    "from gensim.utils import simple_preprocess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:07:37.532271Z",
+     "iopub.status.busy": "2024-05-28T15:07:37.531546Z",
+     "iopub.status.idle": "2024-05-28T15:08:02.872926Z",
+     "shell.execute_reply": "2024-05-28T15:08:02.871888Z",
+     "shell.execute_reply.started": "2024-05-28T15:07:37.532232Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "story=[]\n",
+    "for doc in df['review']:\n",
+    "    raw_sent=sent_tokenize(doc)\n",
+    "    for sent in raw_sent:\n",
+    "        story.append(simple_preprocess(sent))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:08:40.812263Z",
+     "iopub.status.busy": "2024-05-28T15:08:40.811483Z",
+     "iopub.status.idle": "2024-05-28T15:08:40.817557Z",
+     "shell.execute_reply": "2024-05-28T15:08:40.816616Z",
+     "shell.execute_reply.started": "2024-05-28T15:08:40.812227Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model=gensim.models.Word2Vec(\n",
+    "window=10,min_count=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:09:01.856845Z",
+     "iopub.status.busy": "2024-05-28T15:09:01.855976Z",
+     "iopub.status.idle": "2024-05-28T15:09:05.537674Z",
+     "shell.execute_reply": "2024-05-28T15:09:05.536873Z",
+     "shell.execute_reply.started": "2024-05-28T15:09:01.856798Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model.build_vocab(story)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:10:20.091520Z",
+     "iopub.status.busy": "2024-05-28T15:10:20.091143Z",
+     "iopub.status.idle": "2024-05-28T15:10:51.764165Z",
+     "shell.execute_reply": "2024-05-28T15:10:51.763105Z",
+     "shell.execute_reply.started": "2024-05-28T15:10:20.091491Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(28382867, 30062525)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.train(story,total_examples=model.corpus_count,epochs=model.epochs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:11:03.211080Z",
+     "iopub.status.busy": "2024-05-28T15:11:03.210673Z",
+     "iopub.status.idle": "2024-05-28T15:11:03.218564Z",
+     "shell.execute_reply": "2024-05-28T15:11:03.217552Z",
+     "shell.execute_reply.started": "2024-05-28T15:11:03.211047Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "79870"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(model.wv.index_to_key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:13:11.657877Z",
+     "iopub.status.busy": "2024-05-28T15:13:11.657479Z",
+     "iopub.status.idle": "2024-05-28T15:13:11.663513Z",
+     "shell.execute_reply": "2024-05-28T15:13:11.662556Z",
+     "shell.execute_reply.started": "2024-05-28T15:13:11.657844Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def dec_vector(doc):\n",
+    "    doc=[word for word in doc.split() if word in model.wv.index_to_key]\n",
+    "    return np.mean(model.wv[doc],axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:14:29.737526Z",
+     "iopub.status.busy": "2024-05-28T15:14:29.736457Z",
+     "iopub.status.idle": "2024-05-28T15:14:29.742036Z",
+     "shell.execute_reply": "2024-05-28T15:14:29.740881Z",
+     "shell.execute_reply.started": "2024-05-28T15:14:29.737484Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:16:04.216141Z",
+     "iopub.status.busy": "2024-05-28T15:16:04.215772Z",
+     "iopub.status.idle": "2024-05-28T15:35:52.614033Z",
+     "shell.execute_reply": "2024-05-28T15:35:52.613102Z",
+     "shell.execute_reply.started": "2024-05-28T15:16:04.216114Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 49582/49582 [19:48<00:00, 41.72it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X=[]\n",
+    "for doc in tqdm(df['review'].values):\n",
+    "    X.append(dec_vector(doc))\n",
+    "                "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:35:52.757355Z",
+     "iopub.status.busy": "2024-05-28T15:35:52.756711Z",
+     "iopub.status.idle": "2024-05-28T15:35:52.801878Z",
+     "shell.execute_reply": "2024-05-28T15:35:52.800886Z",
+     "shell.execute_reply.started": "2024-05-28T15:35:52.757317Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X=np.array(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:35:52.992514Z",
+     "iopub.status.busy": "2024-05-28T15:35:52.992157Z",
+     "iopub.status.idle": "2024-05-28T15:35:52.999577Z",
+     "shell.execute_reply": "2024-05-28T15:35:52.998462Z",
+     "shell.execute_reply.started": "2024-05-28T15:35:52.992480Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(49582, 100)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:35:53.001378Z",
+     "iopub.status.busy": "2024-05-28T15:35:53.000874Z",
+     "iopub.status.idle": "2024-05-28T15:35:53.008752Z",
+     "shell.execute_reply": "2024-05-28T15:35:53.007881Z",
+     "shell.execute_reply.started": "2024-05-28T15:35:53.001350Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, ..., 0, 0, 0])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:35:53.010300Z",
+     "iopub.status.busy": "2024-05-28T15:35:53.009962Z",
+     "iopub.status.idle": "2024-05-28T15:35:53.046198Z",
+     "shell.execute_reply": "2024-05-28T15:35:53.045411Z",
+     "shell.execute_reply.started": "2024-05-28T15:35:53.010269Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=3,stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:35:53.047433Z",
+     "iopub.status.busy": "2024-05-28T15:35:53.047187Z",
+     "iopub.status.idle": "2024-05-28T15:36:36.307334Z",
+     "shell.execute_reply": "2024-05-28T15:36:36.306297Z",
+     "shell.execute_reply.started": "2024-05-28T15:35:53.047411Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8395684178683069"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rf=RandomForestClassifier()\n",
+    "rf.fit(x_train,y_train)\n",
+    "y_pred=rf.predict(x_test)\n",
+    "accuracy_score(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:36:36.308713Z",
+     "iopub.status.busy": "2024-05-28T15:36:36.308416Z",
+     "iopub.status.idle": "2024-05-28T15:36:36.317103Z",
+     "shell.execute_reply": "2024-05-28T15:36:36.316187Z",
+     "shell.execute_reply.started": "2024-05-28T15:36:36.308682Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[4011,  929],\n",
+       "       [ 662, 4315]])"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "confusion_matrix(y_test,y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-28T15:36:36.319780Z",
+     "iopub.status.busy": "2024-05-28T15:36:36.319506Z",
+     "iopub.status.idle": "2024-05-28T15:36:37.272618Z",
+     "shell.execute_reply": "2024-05-28T15:36:37.271741Z",
+     "shell.execute_reply.started": "2024-05-28T15:36:36.319756Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "model_pkl_file = \"Sentimental_Analysis_Word2Vec.pkl\"  \n",
+    "\n",
+    "with open(model_pkl_file, 'wb') as file:  \n",
+    "    pickle.dump(rf, file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
+   "dataSources": [
+    {
+     "datasetId": 134715,
+     "sourceId": 320111,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 30699,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

packages.txt

@@ -0,0 +1 @@
+libgl1

requirements.txt

@@ -0,0 +1,10 @@
+
+
+scikit-learn==1.2.2
+numpy
+pandas
+streamlit
+nltk
+contractions
+gensim
+scipy==1.12

x.py

@@ -0,0 +1,77 @@
+
+import os
+import pandas as pd
+import numpy as np
+import streamlit as st
+import re
+import pickle
+def remove_tags(text):
+    return re.sub(re.compile('<.*?>'),'',text)
+
+def lwr(text):
+    return text.lower()
+    
+import nltk
+
+nltk.download("stopwords")
+from nltk.corpus import stopwords
+sw_list=stopwords.words('english')
+
+def stopword(text):
+    return " ".join([word for word in text.split() if word not in sw_list])
+
+import string 
+def remove_punctuation(text):
+    return text.translate(str.maketrans('', '', string.punctuation))
+
+import contractions
+def remove_contractions(text):
+    return contractions.fix(text)
+
+def dec_vector(doc):
+    with open("Sentimental_Analysis_WV.pkl", 'rb') as file:  
+        model = pickle.load(file)
+    doc=[word for word in doc.split() if word in model.wv.index_to_key]
+    return np.mean(model.wv[doc],axis=0)
+
+def xvalue(text):
+    X=[]
+    X.append(dec_vector(text))
+    return X
+
+def preprocessed(text):
+    
+    text=remove_tags(text)
+    text=lwr(text)
+    text=stopword(text)
+    text=remove_punctuation(text)
+    text=remove_contractions(text)
+    X=xvalue(text)
+    X=np.array(X)
+    return X
+
+def clear_text():
+    st.session_state["text"] = ""
+
+
+def main():
+    
+    
+    with open("Sentimental_Analysis_Word2Vec.pkl", 'rb') as file1:  
+        rf = pickle.load(file1)
+    st.title('Sentiment Analysis')
+    
+    text = st.text_input(
+        "Enter some text 👇", key="text")
+    
+    if st.button('Classify'):
+        z=preprocessed(text)
+        if rf.predict(z)[0]==1:
+            st.success("Positive")
+        else:
+            st.success("Negative")
+        st.button("Clear", on_click=clear_text)
+
+
+if __name__=='__main__':
+    main()