nlp-data-preprocessing.ipynb

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     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Loading Data"
   ]
  },
  {
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   "id": "fb8fc149",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<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",
       "\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>id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "      <th>category_1</th>\n",
       "      <th>category_2</th>\n",
       "      <th>category_3</th>\n",
       "      <th>in_data</th>\n",
       "      <th>reason_for_exclusion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6d8knd</td>\n",
       "      <td>talesfromtechsupport</td>\n",
       "      <td>Remember your command line switches...</td>\n",
       "      <td>Hi there,  &lt;lb&gt;The usual. Long time lerker, fi...</td>\n",
       "      <td>writing/stories</td>\n",
       "      <td>tech support</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58mbft</td>\n",
       "      <td>teenmom</td>\n",
       "      <td>So what was Matt \"addicted\" to?</td>\n",
       "      <td>Did he ever say what his addiction was or is h...</td>\n",
       "      <td>tv_show</td>\n",
       "      <td>teen mom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8f73s7</td>\n",
       "      <td>Harley</td>\n",
       "      <td>No Club Colors</td>\n",
       "      <td>Funny story. I went to college in Las Vegas. T...</td>\n",
       "      <td>autos</td>\n",
       "      <td>harley davidson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6ti6re</td>\n",
       "      <td>ringdoorbell</td>\n",
       "      <td>Not door bell, but floodlight mount height.</td>\n",
       "      <td>I know this is a sub for the 'Ring Doorbell' b...</td>\n",
       "      <td>hardware/tools</td>\n",
       "      <td>doorbells</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77sxto</td>\n",
       "      <td>intel</td>\n",
       "      <td>Worried about my 8700k small fft/data stress r...</td>\n",
       "      <td>Prime95 (regardless of version) and OCCT both,...</td>\n",
       "      <td>electronics</td>\n",
       "      <td>cpu</td>\n",
       "      <td>intel</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id             subreddit  \\\n",
       "0  6d8knd  talesfromtechsupport   \n",
       "1  58mbft               teenmom   \n",
       "2  8f73s7                Harley   \n",
       "3  6ti6re          ringdoorbell   \n",
       "4  77sxto                 intel   \n",
       "\n",
       "                                               title  \\\n",
       "0             Remember your command line switches...   \n",
       "1                    So what was Matt \"addicted\" to?   \n",
       "2                                     No Club Colors   \n",
       "3        Not door bell, but floodlight mount height.   \n",
       "4  Worried about my 8700k small fft/data stress r...   \n",
       "\n",
       "                                            selftext       category_1  \\\n",
       "0  Hi there,  <lb>The usual. Long time lerker, fi...  writing/stories   \n",
       "1  Did he ever say what his addiction was or is h...          tv_show   \n",
       "2  Funny story. I went to college in Las Vegas. T...            autos   \n",
       "3  I know this is a sub for the 'Ring Doorbell' b...   hardware/tools   \n",
       "4  Prime95 (regardless of version) and OCCT both,...      electronics   \n",
       "\n",
       "        category_2 category_3  in_data reason_for_exclusion  \n",
       "0     tech support        NaN     True                  NaN  \n",
       "1         teen mom        NaN     True                  NaN  \n",
       "2  harley davidson        NaN     True                  NaN  \n",
       "3        doorbells        NaN     True                  NaN  \n",
       "4              cpu      intel     True                  NaN  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "posts_df = pd.read_csv(\"../input/reddit-selfposts/rspct.tsv\", sep='\\t')\n",
    "subred_df = pd.read_csv(\"../input/reddit-selfposts/subreddit_info.csv\").set_index(['subreddit'])\n",
    "df = posts_df.join(subred_df, on='subreddit')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6882b1de",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1013000 entries, 0 to 1012999\n",
      "Data columns (total 9 columns):\n",
      " #   Column                Non-Null Count    Dtype \n",
      "---  ------                --------------    ----- \n",
      " 0   id                    1013000 non-null  object\n",
      " 1   subreddit             1013000 non-null  object\n",
      " 2   title                 1013000 non-null  object\n",
      " 3   selftext              1013000 non-null  object\n",
      " 4   category_1            1013000 non-null  object\n",
      " 5   category_2            1013000 non-null  object\n",
      " 6   category_3            136000 non-null   object\n",
      " 7   in_data               1013000 non-null  bool  \n",
      " 8   reason_for_exclusion  0 non-null        object\n",
      "dtypes: bool(1), object(8)\n",
      "memory usage: 62.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752bba08",
   "metadata": {
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     "duration": 0.024281,
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     "start_time": "2023-12-26T16:49:41.305772",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Standardizing Attribute Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9db46e8a",
   "metadata": {
    "execution": {
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   "outputs": [
    {
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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>id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>category</th>\n",
       "      <th>subcategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6d8knd</td>\n",
       "      <td>talesfromtechsupport</td>\n",
       "      <td>Remember your command line switches...</td>\n",
       "      <td>Hi there,  &lt;lb&gt;The usual. Long time lerker, fi...</td>\n",
       "      <td>writing/stories</td>\n",
       "      <td>tech support</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58mbft</td>\n",
       "      <td>teenmom</td>\n",
       "      <td>So what was Matt \"addicted\" to?</td>\n",
       "      <td>Did he ever say what his addiction was or is h...</td>\n",
       "      <td>tv_show</td>\n",
       "      <td>teen mom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8f73s7</td>\n",
       "      <td>Harley</td>\n",
       "      <td>No Club Colors</td>\n",
       "      <td>Funny story. I went to college in Las Vegas. T...</td>\n",
       "      <td>autos</td>\n",
       "      <td>harley davidson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6ti6re</td>\n",
       "      <td>ringdoorbell</td>\n",
       "      <td>Not door bell, but floodlight mount height.</td>\n",
       "      <td>I know this is a sub for the 'Ring Doorbell' b...</td>\n",
       "      <td>hardware/tools</td>\n",
       "      <td>doorbells</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>77sxto</td>\n",
       "      <td>intel</td>\n",
       "      <td>Worried about my 8700k small fft/data stress r...</td>\n",
       "      <td>Prime95 (regardless of version) and OCCT both,...</td>\n",
       "      <td>electronics</td>\n",
       "      <td>cpu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id             subreddit  \\\n",
       "0  6d8knd  talesfromtechsupport   \n",
       "1  58mbft               teenmom   \n",
       "2  8f73s7                Harley   \n",
       "3  6ti6re          ringdoorbell   \n",
       "4  77sxto                 intel   \n",
       "\n",
       "                                               title  \\\n",
       "0             Remember your command line switches...   \n",
       "1                    So what was Matt \"addicted\" to?   \n",
       "2                                     No Club Colors   \n",
       "3        Not door bell, but floodlight mount height.   \n",
       "4  Worried about my 8700k small fft/data stress r...   \n",
       "\n",
       "                                                text         category  \\\n",
       "0  Hi there,  <lb>The usual. Long time lerker, fi...  writing/stories   \n",
       "1  Did he ever say what his addiction was or is h...          tv_show   \n",
       "2  Funny story. I went to college in Las Vegas. T...            autos   \n",
       "3  I know this is a sub for the 'Ring Doorbell' b...   hardware/tools   \n",
       "4  Prime95 (regardless of version) and OCCT both,...      electronics   \n",
       "\n",
       "       subcategory  \n",
       "0     tech support  \n",
       "1         teen mom  \n",
       "2  harley davidson  \n",
       "3        doorbells  \n",
       "4              cpu  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_mapping = {\n",
    "    'selftext': 'text',\n",
    "    'category_1': 'category',\n",
    "    'category_2': 'subcategory',\n",
    "}\n",
    "\n",
    "df = df.rename(columns=column_mapping)\n",
    "df.drop(columns=['category_3', 'in_data', 'reason_for_exclusion'], inplace=True)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b01908b6",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 20000 entries, 2 to 1012979\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   id           20000 non-null  object\n",
      " 1   subreddit    20000 non-null  object\n",
      " 2   title        20000 non-null  object\n",
      " 3   text         20000 non-null  object\n",
      " 4   category     20000 non-null  object\n",
      " 5   subcategory  20000 non-null  object\n",
      "dtypes: object(6)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Limit the data to autos category\n",
    "df = df[df['category'] == 'autos']\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68447f22",
   "metadata": {
    "papermill": {
     "duration": 0.024106,
     "end_time": "2023-12-26T16:49:42.542331",
     "exception": false,
     "start_time": "2023-12-26T16:49:42.518225",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Cleaning Text Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ce63f5",
   "metadata": {
    "papermill": {
     "duration": 0.02441,
     "end_time": "2023-12-26T16:49:42.591889",
     "exception": false,
     "start_time": "2023-12-26T16:49:42.567479",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Identify Noise with Regular Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8423fc6c",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "\n",
    "RE_SUSPICIOUS = re.compile(r'[&#<>{}\\[\\]\\\\]')\n",
    "\n",
    "def impurity(text: str, min_len=10):\n",
    "    \"\"\" Returns the share of suspicious characters in a text \"\"\"\n",
    "    \n",
    "    return 0 if text is None or len(text) < min_len else len(RE_SUSPICIOUS.findall(text)) / len(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c9be019c",
   "metadata": {
    "execution": {
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     "start_time": "2023-12-26T16:49:42.748064",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
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       "model_id": "75b0a6859f73440090da8c9378fa73fc",
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      "text/plain": [
       "  0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>impurity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>997126</th>\n",
       "      <td>Looking at buying a 335i with 39k miles and 11...</td>\n",
       "      <td>0.214716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>626478</th>\n",
       "      <td>I'm looking to lease an a4 premium plus automa...</td>\n",
       "      <td>0.165099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138199</th>\n",
       "      <td>Breakdown below:&lt;lb&gt;&lt;lb&gt;Elantra GT&lt;lb&gt;&lt;lb&gt;2.0L...</td>\n",
       "      <td>0.139130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647490</th>\n",
       "      <td>Bulbs Needed:&lt;lb&gt;&lt;lb&gt;&lt;lb&gt;**194 LED BULB x8**&lt;l...</td>\n",
       "      <td>0.132411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542876</th>\n",
       "      <td>I currently have a deposit on a 2013 335is (CP...</td>\n",
       "      <td>0.129317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     text  impurity\n",
       "997126  Looking at buying a 335i with 39k miles and 11...  0.214716\n",
       "626478  I'm looking to lease an a4 premium plus automa...  0.165099\n",
       "138199  Breakdown below:<lb><lb>Elantra GT<lb><lb>2.0L...  0.139130\n",
       "647490  Bulbs Needed:<lb><lb><lb>**194 LED BULB x8**<l...  0.132411\n",
       "542876  I currently have a deposit on a 2013 335is (CP...  0.129317"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "\n",
    "tqdm.pandas()\n",
    "\n",
    "df['impurity'] = df['text'].progress_apply(impurity , min_len=10)\n",
    "\n",
    "df[['text', 'impurity']].sort_values(by='impurity', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd5486b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:49:43.386942Z",
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    },
    "papermill": {
     "duration": 0.041081,
     "end_time": "2023-12-26T16:49:43.398548",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from typing import Callable\n",
    "from collections import Counter\n",
    "\n",
    "\n",
    "def count_words(ds: pd.Series, preprocess: Callable = None, min_freq: int = 2):\n",
    "\n",
    "    # process tokens and update counter\n",
    "    def update(doc):\n",
    "        tokens = doc if preprocess is None else preprocess(doc)\n",
    "        counter.update(tokens)\n",
    "\n",
    "    # create counter and run through all data\n",
    "    counter = Counter()\n",
    "    ds.progress_map(update)\n",
    "\n",
    "    # transform counter into data frame\n",
    "    freq_df = pd.DataFrame.from_dict(counter, orient='index', columns=['freq'])\n",
    "    freq_df = freq_df.query('freq >= @min_freq')\n",
    "    freq_df.index.name = 'token'\n",
    "    \n",
    "    return freq_df.sort_values('freq', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dc20533",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:49:43.453242Z",
     "iopub.status.busy": "2023-12-26T16:49:43.452378Z",
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     "shell.execute_reply": "2023-12-26T16:49:43.690165Z"
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     "duration": 0.270765,
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     "exception": false,
     "start_time": "2023-12-26T16:49:43.424253",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "458034fa89cc48dea14d757e6f633aa9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>token</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;lb&gt;</th>\n",
       "      <td>100729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&lt;tab&gt;</th>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         freq\n",
       "token        \n",
       "<lb>   100729\n",
       "<tab>     642"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_words(df['text'], preprocess=lambda t: re.findall(r'<[\\w/]*>', t))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee20b2c0",
   "metadata": {
    "papermill": {
     "duration": 0.028669,
     "end_time": "2023-12-26T16:49:43.751544",
     "exception": false,
     "start_time": "2023-12-26T16:49:43.722875",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "It appears to be have two types of tokens which we can remove to improve our impurity "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08ad6ca8",
   "metadata": {
    "papermill": {
     "duration": 0.025992,
     "end_time": "2023-12-26T16:49:43.805299",
     "exception": false,
     "start_time": "2023-12-26T16:49:43.779307",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Removal with Regular Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05af4ee1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:49:43.860714Z",
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def clean(text):\n",
    "    import html\n",
    "    \n",
    "    # convert html escapes like &amp; to characters.\n",
    "    text = html.unescape(text)\n",
    "    \n",
    "    # tags like <tab>\n",
    "    text = re.sub(r'<[^<>]*>', ' ', text)\n",
    "    \n",
    "    # markdown URLs like [Some text](https://....)\n",
    "    text = re.sub(r'\\[([^\\[\\]]*)\\]\\([^\\(\\)]*\\)', r'\\1', text)\n",
    "    \n",
    "    # text or code in brackets like [0]\n",
    "    text = re.sub(r'\\[[^\\[\\]]*\\]', ' ', text)\n",
    "    \n",
    "    # standalone sequences of specials, matches &# but not #cool\n",
    "    text = re.sub(r'(?:^|\\s)[&#<>{}\\[\\]+|\\\\:-]{1,}(?:\\s|$)', ' ', text)\n",
    "    \n",
    "    # standalone sequences of hyphens like --- or ==\n",
    "    text = re.sub(r'(?:^|\\s)[\\-=\\+]{2,}(?:\\s|$)', ' ', text)\n",
    "    \n",
    "    # sequences of white spaces\n",
    "    text = re.sub(r'\\s+', ' ', text)\n",
    "    \n",
    "    return text.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "58589484",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:49:43.926597Z",
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     "shell.execute_reply": "2023-12-26T16:49:48.525164Z"
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     "exception": false,
     "start_time": "2023-12-26T16:49:43.897937",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87ba95ed35ae4b9387c1b9a9789b223f",
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       "  0%|          | 0/20000 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>text</th>\n",
       "      <th>impurity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>711988</th>\n",
       "      <td>Mustang 2018, 2019, or 2020? Must Haves!!&lt;lb&gt;&lt;...</td>\n",
       "      <td>0.030864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957625</th>\n",
       "      <td>At the dealership, they offered an option for ...</td>\n",
       "      <td>0.026455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836076</th>\n",
       "      <td>I am looking at four Caymans, all are in a sim...</td>\n",
       "      <td>0.024631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222061</th>\n",
       "      <td>Hello,&lt;lb&gt;&lt;lb&gt;I came across this great looking...</td>\n",
       "      <td>0.022762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947793</th>\n",
       "      <td>The Mazda 3 hatchback I'm looking at is a 2007...</td>\n",
       "      <td>0.022099</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     text  impurity\n",
       "711988  Mustang 2018, 2019, or 2020? Must Haves!!<lb><...  0.030864\n",
       "957625  At the dealership, they offered an option for ...  0.026455\n",
       "836076  I am looking at four Caymans, all are in a sim...  0.024631\n",
       "222061  Hello,<lb><lb>I came across this great looking...  0.022762\n",
       "947793  The Mazda 3 hatchback I'm looking at is a 2007...  0.022099"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clean_text'] = df['text'].progress_map(clean)\n",
    "df['impurity'] = df['clean_text'].progress_apply(impurity, min_len=20)\n",
    "df[['text', 'impurity']].sort_values(by='impurity',ascending=False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "619aa5ab",
   "metadata": {
    "papermill": {
     "duration": 0.026981,
     "end_time": "2023-12-26T16:49:48.583342",
     "exception": false,
     "start_time": "2023-12-26T16:49:48.556361",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Character Normalization with textacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0bf0253",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:49:48.640890Z",
     "iopub.status.busy": "2023-12-26T16:49:48.640491Z",
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     "shell.execute_reply": "2023-12-26T16:50:06.918232Z"
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     "duration": 18.311912,
     "end_time": "2023-12-26T16:50:06.923654",
     "exception": false,
     "start_time": "2023-12-26T16:49:48.611742",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting textacy\r\n",
      "  Downloading textacy-0.13.0-py3-none-any.whl (210 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m210.7/210.7 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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      "Collecting floret~=0.10.0 (from textacy)\r\n",
      "  Obtaining dependency information for floret~=0.10.0 from https://files.pythonhosted.org/packages/16/ee/388a5c76c9292f4bef85d7ef895005bb39a0899f8004e9daceb57b2bb0c9/floret-0.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\r\n",
      "  Downloading floret-0.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.1 kB)\r\n",
      "Collecting jellyfish>=0.8.0 (from textacy)\r\n",
      "  Obtaining dependency information for jellyfish>=0.8.0 from https://files.pythonhosted.org/packages/26/87/8d31224804af9dfa7b34657e083b67b24b322c41dd9464b52218c1a33890/jellyfish-1.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata\r\n",
      "  Downloading jellyfish-1.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB)\r\n",
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      "  Downloading pyphen-0.14.0-py3-none-any.whl (2.0 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m40.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
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      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /opt/conda/lib/python3.10/site-packages (from thinc<8.3.0,>=8.1.8->spacy~=3.0->textacy) (0.1.4)\r\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /opt/conda/lib/python3.10/site-packages (from typer<0.10.0,>=0.3.0->spacy~=3.0->textacy) (8.1.7)\r\n",
      "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from weasel<0.4.0,>=0.1.0->spacy~=3.0->textacy) (0.16.0)\r\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->spacy~=3.0->textacy) (2.1.3)\r\n",
      "Downloading floret-0.10.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (320 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.4/320.4 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading jellyfish-1.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m47.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: pyphen, jellyfish, floret, textacy\r\n",
      "Successfully installed floret-0.10.5 jellyfish-1.0.3 pyphen-0.14.0 textacy-0.13.0\r\n"
     ]
    }
   ],
   "source": [
    "! pip install textacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "94479c44",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:06.988019Z",
     "iopub.status.busy": "2023-12-26T16:50:06.987493Z",
     "iopub.status.idle": "2023-12-26T16:50:16.342337Z",
     "shell.execute_reply": "2023-12-26T16:50:16.340811Z"
    },
    "papermill": {
     "duration": 9.391308,
     "end_time": "2023-12-26T16:50:16.345344",
     "exception": false,
     "start_time": "2023-12-26T16:50:06.954036",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import textacy\n",
    "import textacy.preprocessing as tprep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37634546",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:16.406175Z",
     "iopub.status.busy": "2023-12-26T16:50:16.405198Z",
     "iopub.status.idle": "2023-12-26T16:50:16.413517Z",
     "shell.execute_reply": "2023-12-26T16:50:16.412017Z"
    },
    "papermill": {
     "duration": 0.042038,
     "end_time": "2023-12-26T16:50:16.416163",
     "exception": false,
     "start_time": "2023-12-26T16:50:16.374125",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def normalize(text):\n",
    "    text = tprep.normalize.hyphenated_words(text)\n",
    "    text = tprep.normalize.quotation_marks(text)\n",
    "    text = tprep.normalize.unicode(text)\n",
    "    text = tprep.remove.accents(text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02da3c7b",
   "metadata": {
    "papermill": {
     "duration": 0.028184,
     "end_time": "2023-12-26T16:50:16.472851",
     "exception": false,
     "start_time": "2023-12-26T16:50:16.444667",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Pattern-based Data Masking with textacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8ed58fbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:16.531875Z",
     "iopub.status.busy": "2023-12-26T16:50:16.531353Z",
     "iopub.status.idle": "2023-12-26T16:50:19.215466Z",
     "shell.execute_reply": "2023-12-26T16:50:19.213990Z"
    },
    "papermill": {
     "duration": 2.718415,
     "end_time": "2023-12-26T16:50:19.219688",
     "exception": false,
     "start_time": "2023-12-26T16:50:16.501273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "924a39e21825406094595c674008adee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>freq</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>token</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>www.getlowered.com</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>http://www.ecolamautomotive.com/#!2/kv7fq</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>https://www.reddit.com/r/Jeep/comments/4ux232/just_ordered_an_android_head_unit_joying_jeep/</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>http://imgur.com/gallery/XkRsw</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>https://www.youtube.com/watch?v=DuTI_jlA8v8</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    freq\n",
       "token                                                   \n",
       "www.getlowered.com                                     3\n",
       "http://www.ecolamautomotive.com/#!2/kv7fq              2\n",
       "https://www.reddit.com/r/Jeep/comments/4ux232/j...     2\n",
       "http://imgur.com/gallery/XkRsw                         2\n",
       "https://www.youtube.com/watch?v=DuTI_jlA8v8            2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from textacy.preprocessing.resources import RE_URL\n",
    "\n",
    "count_words(df['clean_text'], preprocess=RE_URL.findall).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a22356",
   "metadata": {
    "papermill": {
     "duration": 0.029524,
     "end_time": "2023-12-26T16:50:19.279360",
     "exception": false,
     "start_time": "2023-12-26T16:50:19.249836",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can filter the/clean the urls and apply our normalize function to our text corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6a0a2171",
   "metadata": {
    "execution": {
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     "start_time": "2023-12-26T16:50:19.309836",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "  0%|          | 0/20000 [00:00<?, ?it/s]"
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/20000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>id</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>category</th>\n",
       "      <th>subcategory</th>\n",
       "      <th>impurity</th>\n",
       "      <th>clean_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8f73s7</td>\n",
       "      <td>Harley</td>\n",
       "      <td>No Club Colors</td>\n",
       "      <td>Funny story. I went to college in Las Vegas. T...</td>\n",
       "      <td>autos</td>\n",
       "      <td>harley davidson</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Funny story. I went to college in Las Vegas. T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>5s0q8r</td>\n",
       "      <td>Mustang</td>\n",
       "      <td>Roush vs Shleby GT500</td>\n",
       "      <td>I am trying to determine which is faster, and ...</td>\n",
       "      <td>autos</td>\n",
       "      <td>ford</td>\n",
       "      <td>0.0</td>\n",
       "      <td>I am trying to determine which is faster, and ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5z3405</td>\n",
       "      <td>Volkswagen</td>\n",
       "      <td>2001 Golf Wagon looking for some insight</td>\n",
       "      <td>Hello! &lt;lb&gt;&lt;lb&gt;Trying to find some information...</td>\n",
       "      <td>autos</td>\n",
       "      <td>VW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hello! Trying to find some information on repl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>7df18v</td>\n",
       "      <td>Lexus</td>\n",
       "      <td>IS 250 Coolant Flush/Change</td>\n",
       "      <td>https://www.cars.com/articles/how-often-should...</td>\n",
       "      <td>autos</td>\n",
       "      <td>lexus</td>\n",
       "      <td>0.0</td>\n",
       "      <td>_URL_ I have a IS 250 AWD from 2006. About 73K...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>5tpve8</td>\n",
       "      <td>volt</td>\n",
       "      <td>Gen1 mpg w/ dead battery?</td>\n",
       "      <td>Hi, new to this subreddit.  I'm considering bu...</td>\n",
       "      <td>autos</td>\n",
       "      <td>chevrolet</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Hi, new to this subreddit. I'm considering buy...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id   subreddit                                     title  \\\n",
       "2    8f73s7      Harley                            No Club Colors   \n",
       "56   5s0q8r     Mustang                     Roush vs Shleby GT500   \n",
       "78   5z3405  Volkswagen  2001 Golf Wagon looking for some insight   \n",
       "270  7df18v       Lexus               IS 250 Coolant Flush/Change   \n",
       "286  5tpve8        volt                 Gen1 mpg w/ dead battery?   \n",
       "\n",
       "                                                  text category  \\\n",
       "2    Funny story. I went to college in Las Vegas. T...    autos   \n",
       "56   I am trying to determine which is faster, and ...    autos   \n",
       "78   Hello! <lb><lb>Trying to find some information...    autos   \n",
       "270  https://www.cars.com/articles/how-often-should...    autos   \n",
       "286  Hi, new to this subreddit.  I'm considering bu...    autos   \n",
       "\n",
       "         subcategory  impurity  \\\n",
       "2    harley davidson       0.0   \n",
       "56              ford       0.0   \n",
       "78                VW       0.0   \n",
       "270            lexus       0.0   \n",
       "286        chevrolet       0.0   \n",
       "\n",
       "                                            clean_text  \n",
       "2    Funny story. I went to college in Las Vegas. T...  \n",
       "56   I am trying to determine which is faster, and ...  \n",
       "78   Hello! Trying to find some information on repl...  \n",
       "270  _URL_ I have a IS 250 AWD from 2006. About 73K...  \n",
       "286  Hi, new to this subreddit. I'm considering buy...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['clean_text'] = df['clean_text'].progress_map(tprep.replace.urls)\n",
    "df['clean_text'] = df['clean_text'].progress_map(normalize)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4cdfa4a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:39.825786Z",
     "iopub.status.busy": "2023-12-26T16:50:39.824952Z",
     "iopub.status.idle": "2023-12-26T16:50:39.904267Z",
     "shell.execute_reply": "2023-12-26T16:50:39.903094Z"
    },
    "papermill": {
     "duration": 0.116322,
     "end_time": "2023-12-26T16:50:39.907500",
     "exception": false,
     "start_time": "2023-12-26T16:50:39.791178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.rename(columns={'text': 'raw_text', 'clean_text': 'text'}, inplace=True)\n",
    "df.drop(columns=['impurity'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7174cd67",
   "metadata": {
    "papermill": {
     "duration": 0.03188,
     "end_time": "2023-12-26T16:50:39.970482",
     "exception": false,
     "start_time": "2023-12-26T16:50:39.938602",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Tokenization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56368e6e",
   "metadata": {
    "papermill": {
     "duration": 0.030486,
     "end_time": "2023-12-26T16:50:40.031348",
     "exception": false,
     "start_time": "2023-12-26T16:50:40.000862",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Tokenization with Regular Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e17b3c8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:40.095044Z",
     "iopub.status.busy": "2023-12-26T16:50:40.094592Z",
     "iopub.status.idle": "2023-12-26T16:50:40.101421Z",
     "shell.execute_reply": "2023-12-26T16:50:40.100234Z"
    },
    "papermill": {
     "duration": 0.042196,
     "end_time": "2023-12-26T16:50:40.104141",
     "exception": false,
     "start_time": "2023-12-26T16:50:40.061945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "RE_TOKEN = re.compile(r\"\"\"\n",
    "                ( [#]?[@\\w'’\\.\\-\\:]*\\w     # words, hashtags and email addresses\n",
    "                | [:;<]\\-?[\\)\\(3]          # coarse pattern for basic text emojis\n",
    "                | [\\U0001F100-\\U0001FFFF]  # coarse code range for unicode emojis\n",
    "                )\n",
    "                \"\"\", \n",
    "                re.VERBOSE\n",
    "            )\n",
    "\n",
    "def tokenize(text):\n",
    "    return RE_TOKEN.findall(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a702bdfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:40.166151Z",
     "iopub.status.busy": "2023-12-26T16:50:40.165710Z",
     "iopub.status.idle": "2023-12-26T16:50:40.170887Z",
     "shell.execute_reply": "2023-12-26T16:50:40.169741Z"
    },
    "papermill": {
     "duration": 0.039266,
     "end_time": "2023-12-26T16:50:40.173223",
     "exception": false,
     "start_time": "2023-12-26T16:50:40.133957",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "text = \"\"\"\n",
    "    2019-08-10 23:32: @pete/@louis - I don't have a well-designed \n",
    "    solution for today's problem. The code of module AC68 should be -1. \n",
    "    Have to think a bit... #goodnight ;-) 😩😬\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c871e89c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:40.236362Z",
     "iopub.status.busy": "2023-12-26T16:50:40.234293Z",
     "iopub.status.idle": "2023-12-26T16:50:40.243302Z",
     "shell.execute_reply": "2023-12-26T16:50:40.241964Z"
    },
    "papermill": {
     "duration": 0.042583,
     "end_time": "2023-12-26T16:50:40.245693",
     "exception": false,
     "start_time": "2023-12-26T16:50:40.203110",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-08-10|23:32|@pete|@louis|I|don't|have|a|well-designed|solution|for|today's|problem|The|code|of|module|AC68|should|be|-1|Have|to|think|a|bit|#goodnight|;-)|😩|😬\n"
     ]
    }
   ],
   "source": [
    "print(*tokenize(text), sep='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0934f7",
   "metadata": {
    "papermill": {
     "duration": 0.0311,
     "end_time": "2023-12-26T16:50:40.307764",
     "exception": false,
     "start_time": "2023-12-26T16:50:40.276664",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Tokenization with NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "062343de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:40.372819Z",
     "iopub.status.busy": "2023-12-26T16:50:40.372285Z",
     "iopub.status.idle": "2023-12-26T16:50:41.298933Z",
     "shell.execute_reply": "2023-12-26T16:50:41.297103Z"
    },
    "papermill": {
     "duration": 0.960701,
     "end_time": "2023-12-26T16:50:41.301706",
     "exception": false,
     "start_time": "2023-12-26T16:50:40.341005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "2019-08-10|23:32|:|@|pete/|@|louis|-|I|do|n't|have|a|well-designed|solution|for|today|'s|problem|.|The|code|of|module|AC68|should|be|-1|.|Have|to|think|a|bit|...|#|goodnight|;|-|)|😩😬\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "\n",
    "nltk.download('punkt')\n",
    "print(*nltk.tokenize.word_tokenize(text), sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1384fb0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:41.364545Z",
     "iopub.status.busy": "2023-12-26T16:50:41.364036Z",
     "iopub.status.idle": "2023-12-26T16:50:41.370988Z",
     "shell.execute_reply": "2023-12-26T16:50:41.369917Z"
    },
    "papermill": {
     "duration": 0.042052,
     "end_time": "2023-12-26T16:50:41.373712",
     "exception": false,
     "start_time": "2023-12-26T16:50:41.331660",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-08-10|23:32|@pete|@louis|I|don't|have|a|well-designed|solution|for|today's|problem|The|code|of|module|AC68|should|be|-1|Have|to|think|a|bit|#goodnight|;-)|😩|😬\n"
     ]
    }
   ],
   "source": [
    "tokenizer = nltk.tokenize.RegexpTokenizer(RE_TOKEN.pattern, flags=re.VERBOSE)\n",
    "print(*tokenizer.tokenize(text), sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "15ab934f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:41.437593Z",
     "iopub.status.busy": "2023-12-26T16:50:41.437165Z",
     "iopub.status.idle": "2023-12-26T16:50:41.444822Z",
     "shell.execute_reply": "2023-12-26T16:50:41.443348Z"
    },
    "papermill": {
     "duration": 0.042833,
     "end_time": "2023-12-26T16:50:41.448380",
     "exception": false,
     "start_time": "2023-12-26T16:50:41.405547",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-08-|10|23:32|:|@pete|/|@louis|-|I|don't|have|a|well-designed|solution|for|today's|problem|.|The|code|of|module|AC68|should|be|-|1|.|Have|to|think|a|bit|...|#goodnight|;-)|😩|😬\n"
     ]
    }
   ],
   "source": [
    "tokenizer = nltk.tokenize.TweetTokenizer()\n",
    "print(*tokenizer.tokenize(text), sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d93b75aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:41.512390Z",
     "iopub.status.busy": "2023-12-26T16:50:41.511952Z",
     "iopub.status.idle": "2023-12-26T16:50:41.519582Z",
     "shell.execute_reply": "2023-12-26T16:50:41.518268Z"
    },
    "papermill": {
     "duration": 0.042668,
     "end_time": "2023-12-26T16:50:41.522183",
     "exception": false,
     "start_time": "2023-12-26T16:50:41.479515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-08-10|23|:|32|:|@pete/@louis|-|I|don|'|t|have|a|well-designed|solution|for|today|'|s|problem.|The|code|of|module|AC68|should|be|-1.|Have|to|think|a|bit|...|#goodnight|;|-|)|😩😬\n"
     ]
    }
   ],
   "source": [
    "tokenizer = nltk.tokenize.ToktokTokenizer()\n",
    "print(*tokenizer.tokenize(text), sep='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d05e6d90",
   "metadata": {
    "papermill": {
     "duration": 0.030277,
     "end_time": "2023-12-26T16:50:41.582726",
     "exception": false,
     "start_time": "2023-12-26T16:50:41.552449",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Linguistic Processing with spaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a00db67d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:41.645147Z",
     "iopub.status.busy": "2023-12-26T16:50:41.644604Z",
     "iopub.status.idle": "2023-12-26T16:50:43.142961Z",
     "shell.execute_reply": "2023-12-26T16:50:43.141400Z"
    },
    "papermill": {
     "duration": 1.533406,
     "end_time": "2023-12-26T16:50:43.145931",
     "exception": false,
     "start_time": "2023-12-26T16:50:41.612525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "\n",
    "nlp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b343ef0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.209112Z",
     "iopub.status.busy": "2023-12-26T16:50:43.208089Z",
     "iopub.status.idle": "2023-12-26T16:50:43.217733Z",
     "shell.execute_reply": "2023-12-26T16:50:43.216227Z"
    },
    "papermill": {
     "duration": 0.044525,
     "end_time": "2023-12-26T16:50:43.220417",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.175892",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('tok2vec', <spacy.pipeline.tok2vec.Tok2Vec at 0x7d9b194b2020>),\n",
       " ('tagger', <spacy.pipeline.tagger.Tagger at 0x7d9b194b1660>),\n",
       " ('parser', <spacy.pipeline.dep_parser.DependencyParser at 0x7d9b1980de70>),\n",
       " ('attribute_ruler',\n",
       "  <spacy.pipeline.attributeruler.AttributeRuler at 0x7d9b193d8140>),\n",
       " ('lemmatizer',\n",
       "  <spacy.lang.en.lemmatizer.EnglishLemmatizer at 0x7d9b193d61c0>),\n",
       " ('ner', <spacy.pipeline.ner.EntityRecognizer at 0x7d9b1980e030>)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ae44abb",
   "metadata": {
    "papermill": {
     "duration": 0.031961,
     "end_time": "2023-12-26T16:50:43.284113",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.252152",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As spacy loads multiple components together, one can disable them during the loading pahse by passing disable argument or later on via nlp.disable_pipes(...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bb7c3247",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.347028Z",
     "iopub.status.busy": "2023-12-26T16:50:43.346605Z",
     "iopub.status.idle": "2023-12-26T16:50:43.352021Z",
     "shell.execute_reply": "2023-12-26T16:50:43.350615Z"
    },
    "papermill": {
     "duration": 0.040048,
     "end_time": "2023-12-26T16:50:43.354442",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.314394",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# nlp.disable_pipes('parser', 'ner')\n",
    "# nlp = spacy.load(\"en_core_web_sm\", disable=[\"parser\", \"ner\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe5b1314",
   "metadata": {
    "papermill": {
     "duration": 0.031281,
     "end_time": "2023-12-26T16:50:43.417821",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.386540",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Processing Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dd90f8e6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.479581Z",
     "iopub.status.busy": "2023-12-26T16:50:43.479181Z",
     "iopub.status.idle": "2023-12-26T16:50:43.511391Z",
     "shell.execute_reply": "2023-12-26T16:50:43.510374Z"
    },
    "papermill": {
     "duration": 0.066512,
     "end_time": "2023-12-26T16:50:43.514259",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.447747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "text = \"Let's go to N.Y city\"\n",
    "doc = nlp(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f86d86ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.577695Z",
     "iopub.status.busy": "2023-12-26T16:50:43.577217Z",
     "iopub.status.idle": "2023-12-26T16:50:43.584542Z",
     "shell.execute_reply": "2023-12-26T16:50:43.582891Z"
    },
    "papermill": {
     "duration": 0.041874,
     "end_time": "2023-12-26T16:50:43.587000",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.545126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let|'s|go|to|N.Y|city|"
     ]
    }
   ],
   "source": [
    "for token in doc:\n",
    "    print(token , end='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e61a3377",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.649854Z",
     "iopub.status.busy": "2023-12-26T16:50:43.649380Z",
     "iopub.status.idle": "2023-12-26T16:50:43.672722Z",
     "shell.execute_reply": "2023-12-26T16:50:43.671289Z"
    },
    "papermill": {
     "duration": 0.058181,
     "end_time": "2023-12-26T16:50:43.675515",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.617334",
     "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>text</th>\n",
       "      <th>lemma_</th>\n",
       "      <th>is_stop</th>\n",
       "      <th>is_alpha</th>\n",
       "      <th>pos_</th>\n",
       "      <th>dep_</th>\n",
       "      <th>ent_type_</th>\n",
       "      <th>ent_iob_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Let</th>\n",
       "      <td>Let</td>\n",
       "      <td>let</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>VERB</td>\n",
       "      <td>ROOT</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>'s</th>\n",
       "      <td>'s</td>\n",
       "      <td>us</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>PRON</td>\n",
       "      <td>nsubj</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go</th>\n",
       "      <td>go</td>\n",
       "      <td>go</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>VERB</td>\n",
       "      <td>ccomp</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>to</th>\n",
       "      <td>to</td>\n",
       "      <td>to</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>ADP</td>\n",
       "      <td>prep</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N.Y</th>\n",
       "      <td>N.Y</td>\n",
       "      <td>N.Y</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>compound</td>\n",
       "      <td>GPE</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <td>city</td>\n",
       "      <td>city</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>pobj</td>\n",
       "      <td></td>\n",
       "      <td>O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      text lemma_  is_stop  is_alpha   pos_      dep_ ent_type_ ent_iob_\n",
       "Let    Let    let    False      True   VERB      ROOT                  O\n",
       "'s      's     us     True     False   PRON     nsubj                  O\n",
       "go      go     go     True      True   VERB     ccomp                  O\n",
       "to      to     to     True      True    ADP      prep                  O\n",
       "N.Y    N.Y    N.Y    False     False  PROPN  compound       GPE        B\n",
       "city  city   city    False      True   NOUN      pobj                  O"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def display_doc(doc: spacy.tokens.Doc):\n",
    "    tokens = []\n",
    "\n",
    "    for i, token in enumerate(doc):\n",
    "        tokens.append(dict(\n",
    "            token=token,\n",
    "            text=token.text,\n",
    "            lemma_=token.lemma_,\n",
    "            is_stop=token.is_stop,\n",
    "            is_alpha=token.is_alpha,\n",
    "            pos_=token.pos_,\n",
    "            dep_=token.dep_,\n",
    "            ent_type_=token.ent_type_,\n",
    "            ent_iob_=token.ent_iob_,\n",
    "        ))\n",
    "\n",
    "    df = pd.DataFrame(tokens).set_index('token')\n",
    "    df.index.name = None\n",
    "    return df\n",
    "\n",
    "display_doc(doc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e491040",
   "metadata": {
    "papermill": {
     "duration": 0.030767,
     "end_time": "2023-12-26T16:50:43.737415",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.706648",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Customizing Tokenization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "89296b70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.802673Z",
     "iopub.status.busy": "2023-12-26T16:50:43.801727Z",
     "iopub.status.idle": "2023-12-26T16:50:43.823342Z",
     "shell.execute_reply": "2023-12-26T16:50:43.821658Z"
    },
    "papermill": {
     "duration": 0.056996,
     "end_time": "2023-12-26T16:50:43.825865",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.768869",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@Pete|:|choose|low|-|carb|#|food|#|eat|-|smart|.|_|url|_|;-)|😋|👍|"
     ]
    }
   ],
   "source": [
    "text = \"@Pete: choose low-carb #food #eat-smart. _url_ ;-) 😋👍\"\n",
    "doc = nlp(text)\n",
    "\n",
    "for token in doc:\n",
    "    print(token, end=\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6c8c7af5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.889202Z",
     "iopub.status.busy": "2023-12-26T16:50:43.888709Z",
     "iopub.status.idle": "2023-12-26T16:50:43.898263Z",
     "shell.execute_reply": "2023-12-26T16:50:43.896988Z"
    },
    "papermill": {
     "duration": 0.044137,
     "end_time": "2023-12-26T16:50:43.900548",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.856411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from spacy.tokenizer import Tokenizer\n",
    "from spacy.util import compile_prefix_regex, compile_infix_regex, compile_suffix_regex\n",
    "\n",
    "\n",
    "def custom_tokenizer(nlp: spacy.Language) -> Tokenizer:\n",
    "    \n",
    "    # use default patterns except the ones matched by re.search\n",
    "    prefixes = [pattern for pattern in nlp.Defaults.prefixes if pattern not in ['-', '_', '#']]\n",
    "    suffixes = [pattern for pattern in nlp.Defaults.suffixes if pattern not in ['_']]\n",
    "    infixes  = [pattern for pattern in nlp.Defaults.infixes if not re.search(pattern, 'xx-xx')]\n",
    "\n",
    "    return Tokenizer(vocab=nlp.vocab, \n",
    "                     rules=nlp.Defaults.tokenizer_exceptions,\n",
    "                     prefix_search=compile_prefix_regex(prefixes).search,\n",
    "                     suffix_search=compile_suffix_regex(suffixes).search,\n",
    "                     infix_finditer=compile_infix_regex(infixes).finditer,\n",
    "                     token_match=nlp.Defaults.token_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe9bc0a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:43.965500Z",
     "iopub.status.busy": "2023-12-26T16:50:43.964596Z",
     "iopub.status.idle": "2023-12-26T16:50:44.327257Z",
     "shell.execute_reply": "2023-12-26T16:50:44.326017Z"
    },
    "papermill": {
     "duration": 0.398696,
     "end_time": "2023-12-26T16:50:44.329981",
     "exception": false,
     "start_time": "2023-12-26T16:50:43.931285",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@Pete|:|choose|low-carb|#food|#eat-smart|.|_url_|;-)|😋|👍|"
     ]
    }
   ],
   "source": [
    "nlp.tokenizer = custom_tokenizer(nlp)\n",
    "\n",
    "doc = nlp(text)\n",
    "for token in doc:\n",
    "    print(token, end=\"|\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7da6e25",
   "metadata": {
    "papermill": {
     "duration": 0.032845,
     "end_time": "2023-12-26T16:50:44.394597",
     "exception": false,
     "start_time": "2023-12-26T16:50:44.361752",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Working with Stop Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ff7d6c0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:44.466405Z",
     "iopub.status.busy": "2023-12-26T16:50:44.465249Z",
     "iopub.status.idle": "2023-12-26T16:50:45.782198Z",
     "shell.execute_reply": "2023-12-26T16:50:45.780157Z"
    },
    "papermill": {
     "duration": 1.356032,
     "end_time": "2023-12-26T16:50:45.785051",
     "exception": false,
     "start_time": "2023-12-26T16:50:44.429019",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Dear, Ryan, need, sit, talk, Regards, Pete]\n"
     ]
    }
   ],
   "source": [
    "nlp = spacy.load('en_core_web_sm')\n",
    "text = \"Dear Ryan, we need to sit down and talk. Regards, Pete\"\n",
    "doc = nlp(text)\n",
    "\n",
    "non_stop = [t for t in doc if not t.is_stop and not t.is_punct]\n",
    "print(non_stop)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4185286a",
   "metadata": {
    "papermill": {
     "duration": 0.03061,
     "end_time": "2023-12-26T16:50:45.848143",
     "exception": false,
     "start_time": "2023-12-26T16:50:45.817533",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "If we want to customize the stop words, we can do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fb07f156",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:45.913345Z",
     "iopub.status.busy": "2023-12-26T16:50:45.911747Z",
     "iopub.status.idle": "2023-12-26T16:50:46.169363Z",
     "shell.execute_reply": "2023-12-26T16:50:46.167976Z"
    },
    "papermill": {
     "duration": 0.292684,
     "end_time": "2023-12-26T16:50:46.172227",
     "exception": false,
     "start_time": "2023-12-26T16:50:45.879543",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ryan|need|sit|down|talk|Pete|"
     ]
    }
   ],
   "source": [
    "from spacy.lang.en import English\n",
    "\n",
    "\n",
    "excluded_stop_words = {'down'}\n",
    "included_stop_words = {'dear', 'regards'}\n",
    "\n",
    "\n",
    "class CustomEnglishDefaults(English.Defaults):\n",
    "    stop_words = English.Defaults.stop_words.copy()\n",
    "    stop_words -= excluded_stop_words\n",
    "    stop_words |= included_stop_words\n",
    "    \n",
    "\n",
    "class CustomEnglish(English):\n",
    "    Defaults = CustomEnglishDefaults\n",
    "\n",
    "nlp = CustomEnglish()\n",
    "\n",
    "text = \"Dear Ryan, we need to sit down and talk. Regards, Pete\"\n",
    "doc = nlp.make_doc(text) # only tokenize\n",
    "    \n",
    "tokens_wo_stop = [token for token in doc ]\n",
    "for token in doc:\n",
    "    if not token.is_stop and not token.is_punct:\n",
    "        print(token, end='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "052f4404",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:46.237643Z",
     "iopub.status.busy": "2023-12-26T16:50:46.237204Z",
     "iopub.status.idle": "2023-12-26T16:50:47.490450Z",
     "shell.execute_reply": "2023-12-26T16:50:47.489022Z"
    },
    "papermill": {
     "duration": 1.289813,
     "end_time": "2023-12-26T16:50:47.493643",
     "exception": false,
     "start_time": "2023-12-26T16:50:46.203830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# reset to original\n",
    "nlp = spacy.load('en_core_web_sm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57285516",
   "metadata": {
    "papermill": {
     "duration": 0.031626,
     "end_time": "2023-12-26T16:50:47.557226",
     "exception": false,
     "start_time": "2023-12-26T16:50:47.525600",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Extracting Lemmas based on Part-of-Speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4b7d5c85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:47.624953Z",
     "iopub.status.busy": "2023-12-26T16:50:47.624481Z",
     "iopub.status.idle": "2023-12-26T16:50:47.643319Z",
     "shell.execute_reply": "2023-12-26T16:50:47.641953Z"
    },
    "papermill": {
     "duration": 0.056419,
     "end_time": "2023-12-26T16:50:47.645993",
     "exception": false,
     "start_time": "2023-12-26T16:50:47.589574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my|good|friend|Ryan|Peters|like|fancy|adventure|game|.\n"
     ]
    }
   ],
   "source": [
    "text = \"My best friend Ryan Peters likes fancy adventure games.\"\n",
    "doc = nlp(text)\n",
    "print(*[t.lemma_ for t in doc], sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1560e612",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:47.712414Z",
     "iopub.status.busy": "2023-12-26T16:50:47.712034Z",
     "iopub.status.idle": "2023-12-26T16:50:47.717727Z",
     "shell.execute_reply": "2023-12-26T16:50:47.716817Z"
    },
    "papermill": {
     "duration": 0.042124,
     "end_time": "2023-12-26T16:50:47.720171",
     "exception": false,
     "start_time": "2023-12-26T16:50:47.678047",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[friend, Ryan, Peters, adventure, games]\n"
     ]
    }
   ],
   "source": [
    "print([t for t in doc if t.pos_ in ['NOUN', 'PROPN']])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e101d5fb",
   "metadata": {
    "papermill": {
     "duration": 0.034135,
     "end_time": "2023-12-26T16:50:47.787950",
     "exception": false,
     "start_time": "2023-12-26T16:50:47.753815",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can now use functions from textacy to do the similar thing we did above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d04264b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:47.855252Z",
     "iopub.status.busy": "2023-12-26T16:50:47.854838Z",
     "iopub.status.idle": "2023-12-26T16:50:47.862250Z",
     "shell.execute_reply": "2023-12-26T16:50:47.860722Z"
    },
    "papermill": {
     "duration": 0.044394,
     "end_time": "2023-12-26T16:50:47.865238",
     "exception": false,
     "start_time": "2023-12-26T16:50:47.820844",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best|friend|fancy|adventure|games\n"
     ]
    }
   ],
   "source": [
    "tokens = textacy.extract.words(\n",
    "    doc,\n",
    "    filter_stops=True,           # default True, no stopwords\n",
    "    filter_punct=True,           # default True, no punctuation\n",
    "    filter_nums=True,            # default False, no numbers\n",
    "    include_pos=['ADJ', 'NOUN'], # default None = include all\n",
    "    exclude_pos=None,            # default None = exclude none\n",
    "    min_freq=1                   # minimum frequency of words\n",
    ")\n",
    "\n",
    "print(*[t for t in tokens], sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3120b051",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:47.933141Z",
     "iopub.status.busy": "2023-12-26T16:50:47.932608Z",
     "iopub.status.idle": "2023-12-26T16:50:47.941667Z",
     "shell.execute_reply": "2023-12-26T16:50:47.939884Z"
    },
    "papermill": {
     "duration": 0.046304,
     "end_time": "2023-12-26T16:50:47.944667",
     "exception": false,
     "start_time": "2023-12-26T16:50:47.898363",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good|friend|fancy|adventure|game\n"
     ]
    }
   ],
   "source": [
    "def extract_lemmas(doc, **kwargs):\n",
    "    return [t.lemma_ for t in textacy.extract.words(doc, **kwargs)]\n",
    "\n",
    "lemmas = extract_lemmas(doc, include_pos=['ADJ', 'NOUN'])\n",
    "print(*lemmas, sep='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8d2e973",
   "metadata": {
    "papermill": {
     "duration": 0.031851,
     "end_time": "2023-12-26T16:50:48.010672",
     "exception": false,
     "start_time": "2023-12-26T16:50:47.978821",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Extracting Noun Phrases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0363a562",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:48.075876Z",
     "iopub.status.busy": "2023-12-26T16:50:48.075392Z",
     "iopub.status.idle": "2023-12-26T16:50:48.081385Z",
     "shell.execute_reply": "2023-12-26T16:50:48.080137Z"
    },
    "papermill": {
     "duration": 0.042417,
     "end_time": "2023-12-26T16:50:48.084180",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.041763",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My best friend Ryan Peters likes fancy adventure games.\n"
     ]
    }
   ],
   "source": [
    "print(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ff74e849",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:48.149665Z",
     "iopub.status.busy": "2023-12-26T16:50:48.149177Z",
     "iopub.status.idle": "2023-12-26T16:50:48.157943Z",
     "shell.execute_reply": "2023-12-26T16:50:48.156683Z"
    },
    "papermill": {
     "duration": 0.045158,
     "end_time": "2023-12-26T16:50:48.160685",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.115527",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good friend|fancy adventure|fancy adventure game\n",
      "My best friend|Ryan Peters|fancy adventure games\n"
     ]
    }
   ],
   "source": [
    "patterns = [\"POS:ADJ POS:NOUN:+\"]\n",
    "\n",
    "spans = textacy.extract.matches.token_matches(doc, patterns=patterns)\n",
    "    \n",
    "print(*[s.lemma_ for s in spans], sep='|')\n",
    "print(*doc.noun_chunks, sep='|')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "24ceb157",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:48.228861Z",
     "iopub.status.busy": "2023-12-26T16:50:48.228357Z",
     "iopub.status.idle": "2023-12-26T16:50:48.238261Z",
     "shell.execute_reply": "2023-12-26T16:50:48.236718Z"
    },
    "papermill": {
     "duration": 0.046743,
     "end_time": "2023-12-26T16:50:48.240833",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.194090",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good_friend|fancy_adventure|fancy_adventure_game|adventure_game\n"
     ]
    }
   ],
   "source": [
    "def extract_noun_phrases(doc, preceding_pos=['NOUN'], sep='_'):\n",
    "    patterns = []\n",
    "    \n",
    "    for pos in preceding_pos:\n",
    "        patterns.append(f\"POS:{pos} POS:NOUN:+\")\n",
    "\n",
    "    spans = textacy.extract.matches.token_matches(doc, patterns=patterns)\n",
    "\n",
    "    return [sep.join([t.lemma_ for t in s]) for s in spans]\n",
    "\n",
    "print(*extract_noun_phrases(doc, ['ADJ', 'NOUN']), sep='|')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b9b9872",
   "metadata": {
    "papermill": {
     "duration": 0.033865,
     "end_time": "2023-12-26T16:50:48.306424",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.272559",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Extracting Named Entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8f4ab074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:48.373796Z",
     "iopub.status.busy": "2023-12-26T16:50:48.373338Z",
     "iopub.status.idle": "2023-12-26T16:50:48.395717Z",
     "shell.execute_reply": "2023-12-26T16:50:48.394227Z"
    },
    "papermill": {
     "duration": 0.059152,
     "end_time": "2023-12-26T16:50:48.398114",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.338962",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(James O'Neill, PERSON) (World Cargo Inc, ORG) (San Francisco, GPE) "
     ]
    }
   ],
   "source": [
    "text = \"James O'Neill, chairman of World Cargo Inc, lives in San Francisco.\"\n",
    "doc = nlp(text)\n",
    "\n",
    "for ent in doc.ents:\n",
    "    print(f\"({ent.text}, {ent.label_})\", end=\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c4d9ef0c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:48.466013Z",
     "iopub.status.busy": "2023-12-26T16:50:48.465538Z",
     "iopub.status.idle": "2023-12-26T16:50:48.476795Z",
     "shell.execute_reply": "2023-12-26T16:50:48.475538Z"
    },
    "papermill": {
     "duration": 0.04743,
     "end_time": "2023-12-26T16:50:48.479210",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.431780",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    James O'Neill\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       ", chairman of \n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    World Cargo Inc\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       ", lives in \n",
       "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    San Francisco\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
       "</mark>\n",
       ".</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from spacy import displacy\n",
    "\n",
    "displacy.render(doc, style='ent', jupyter=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ad78bd2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:50:48.546840Z",
     "iopub.status.busy": "2023-12-26T16:50:48.546315Z",
     "iopub.status.idle": "2023-12-26T16:50:48.555184Z",
     "shell.execute_reply": "2023-12-26T16:50:48.553747Z"
    },
    "papermill": {
     "duration": 0.045754,
     "end_time": "2023-12-26T16:50:48.557639",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.511885",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"James_O'Neill/PERSON\", 'San_Francisco/GPE']\n"
     ]
    }
   ],
   "source": [
    "def extract_entities(doc, include_types=None, sep='_'):\n",
    "\n",
    "    ents = textacy.extract.entities(\n",
    "        doc, \n",
    "        include_types=include_types, \n",
    "        exclude_types=None, \n",
    "        drop_determiners=True, \n",
    "        min_freq=1\n",
    "    )\n",
    "    \n",
    "    return [sep.join([t.lemma_ for t in e]) + '/' + e.label_ for e in ents]\n",
    "\n",
    "\n",
    "print(extract_entities(doc, ['PERSON', 'GPE']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff76364",
   "metadata": {
    "papermill": {
     "duration": 0.031711,
     "end_time": "2023-12-26T16:50:48.621979",
     "exception": false,
     "start_time": "2023-12-26T16:50:48.590268",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Feature Extraction on a Large Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a3088e1",
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   },
   "source": [
    "## One Function to Get It All"
   ]
  },
  {
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   "id": "bd51d1a6",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lemmas: ['good', 'friend', 'Ryan', 'Peters', 'like', 'fancy', 'adventure', 'game']\n",
      "adjs_verbs: ['good', 'like', 'fancy']\n",
      "nouns: ['friend', 'Ryan', 'Peters', 'adventure', 'game']\n",
      "nouns_phrases: ['adventure_game']\n",
      "adj_noun_phrases: ['good_friend', 'fancy_adventure', 'fancy_adventure_game']\n",
      "entities: ['Ryan_Peters/PERSON']\n"
     ]
    }
   ],
   "source": [
    "def extract_nlp(doc):\n",
    "    return dict(\n",
    "        lemmas=extract_lemmas(doc, exclude_pos = ['PART', 'PUNCT', 'DET', 'PRON', 'SYM', 'SPACE'], filter_stops = False),\n",
    "        adjs_verbs=extract_lemmas(doc, include_pos = ['ADJ', 'VERB']),\n",
    "        nouns=extract_lemmas(doc, include_pos = ['NOUN', 'PROPN']),\n",
    "        nouns_phrases=extract_noun_phrases(doc, ['NOUN']),\n",
    "        adj_noun_phrases=extract_noun_phrases(doc, ['ADJ']),\n",
    "        entities=extract_entities(doc, ['PERSON', 'ORG', 'GPE', 'LOC'])\n",
    "    )\n",
    "\n",
    "text = \"My best friend Ryan Peters likes fancy adventure games.\"\n",
    "doc = nlp(text)\n",
    "\n",
    "for col, values in extract_nlp(doc).items():\n",
    "    print(f\"{col}: {values}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "39918427",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "nlp_columns = list(extract_nlp(nlp.make_doc('')).keys())\n",
    "\n",
    "for col in nlp_columns:\n",
    "    df[col] = None\n",
    "    \n",
    "nlp.tokenizer = custom_tokenizer(nlp)\n",
    "\n",
    "df['text'] = df['title'] + ': ' + df['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e2d389a3",
   "metadata": {
    "execution": {
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     "start_time": "2023-12-26T16:50:49.124231",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7a03da559bfa426aac83d5835f675693",
       "version_major": 2,
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       "  0%|          | 0/400 [00:00<?, ?it/s]"
      ]
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     "metadata": {},
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   ],
   "source": [
    "import math\n",
    "\n",
    "\n",
    "batch_size = 50\n",
    "batches = math.ceil(len(df) / batch_size)\n",
    "\n",
    "for i in tqdm(range(0, len(df), batch_size), total=batches):\n",
    "    docs = nlp.pipe(df['text'][i:i+batch_size])\n",
    "    \n",
    "    for j, doc in enumerate(docs):\n",
    "        for col, values in extract_nlp(doc).items():\n",
    "            df[col].iloc[i+j] = values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "11989199",
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    "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>text</th>\n",
       "      <th>lemmas</th>\n",
       "      <th>nouns</th>\n",
       "      <th>nouns_phrases</th>\n",
       "      <th>entities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60820</th>\n",
       "      <td>CPO Fee Question: As someone who may not be ab...</td>\n",
       "      <td>[CPO, Fee, Question, as, may, be, able, stand,...</td>\n",
       "      <td>[CPO, Fee, Question, wait, Model, Model, S, CP...</td>\n",
       "      <td>[shipping_fee, shipping_fee, documentation_cos...</td>\n",
       "      <td>[Model_S_CPO/PERSON, CPO/ORG, CPO/ORG, Doc/PER...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732345</th>\n",
       "      <td>This little Bluetooth remote made playing musi...</td>\n",
       "      <td>[little, Bluetooth, remote, make, play, music,...</td>\n",
       "      <td>[Bluetooth, music, phone, radio, aux, jack, Bl...</td>\n",
       "      <td>[phone_everytime, steering_wheel, steering_whe...</td>\n",
       "      <td>[jack/PERSON, jack/PERSON]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32758</th>\n",
       "      <td>Anyone from the Bay Area? I need advice on an ...</td>\n",
       "      <td>[from, Bay, Area, need, advice, on, auto, shop...</td>\n",
       "      <td>[Bay, Area, advice, auto, shop, work, truck, 4...</td>\n",
       "      <td>[auto_shop, lift_kit, seat_belt, alarm_system]</td>\n",
       "      <td>[Bay_Area/LOC, fine_Bay_Area/LOC]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891889</th>\n",
       "      <td>Would you switch allegiances to get a Raptor?:...</td>\n",
       "      <td>[would, switch, allegiance, get, raptor, be, c...</td>\n",
       "      <td>[allegiance, raptor, chevy, guy, Ford, Raptor,...</td>\n",
       "      <td>[chevy_guy]</td>\n",
       "      <td>[Ford_Raptor/ORG]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542752</th>\n",
       "      <td>What do you guys think about truck bed caps?: ...</td>\n",
       "      <td>[do, guy, think, about, truck, bed, cap, mean,...</td>\n",
       "      <td>[guy, truck, bed, cap, kind, rest, truck, form...</td>\n",
       "      <td>[truck_bed, truck_bed_cap, bed_cap, bed_cap, p...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                                     text  \\\n",
       "60820   CPO Fee Question: As someone who may not be ab...   \n",
       "732345  This little Bluetooth remote made playing musi...   \n",
       "32758   Anyone from the Bay Area? I need advice on an ...   \n",
       "891889  Would you switch allegiances to get a Raptor?:...   \n",
       "542752  What do you guys think about truck bed caps?: ...   \n",
       "\n",
       "                                                   lemmas  \\\n",
       "60820   [CPO, Fee, Question, as, may, be, able, stand,...   \n",
       "732345  [little, Bluetooth, remote, make, play, music,...   \n",
       "32758   [from, Bay, Area, need, advice, on, auto, shop...   \n",
       "891889  [would, switch, allegiance, get, raptor, be, c...   \n",
       "542752  [do, guy, think, about, truck, bed, cap, mean,...   \n",
       "\n",
       "                                                    nouns  \\\n",
       "60820   [CPO, Fee, Question, wait, Model, Model, S, CP...   \n",
       "732345  [Bluetooth, music, phone, radio, aux, jack, Bl...   \n",
       "32758   [Bay, Area, advice, auto, shop, work, truck, 4...   \n",
       "891889  [allegiance, raptor, chevy, guy, Ford, Raptor,...   \n",
       "542752  [guy, truck, bed, cap, kind, rest, truck, form...   \n",
       "\n",
       "                                            nouns_phrases  \\\n",
       "60820   [shipping_fee, shipping_fee, documentation_cos...   \n",
       "732345  [phone_everytime, steering_wheel, steering_whe...   \n",
       "32758      [auto_shop, lift_kit, seat_belt, alarm_system]   \n",
       "891889                                        [chevy_guy]   \n",
       "542752  [truck_bed, truck_bed_cap, bed_cap, bed_cap, p...   \n",
       "\n",
       "                                                 entities  \n",
       "60820   [Model_S_CPO/PERSON, CPO/ORG, CPO/ORG, Doc/PER...  \n",
       "732345                         [jack/PERSON, jack/PERSON]  \n",
       "32758                   [Bay_Area/LOC, fine_Bay_Area/LOC]  \n",
       "891889                                  [Ford_Raptor/ORG]  \n",
       "542752                                                 []  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['text', 'lemmas', 'nouns', 'nouns_phrases', 'entities']].sample(5)"
   ]
  },
  {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_words(df['nouns_phrases']).head(10).plot(kind='barh', figsize=(10, 6)).invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c20bc2b",
   "metadata": {
    "papermill": {
     "duration": 0.035306,
     "end_time": "2023-12-26T16:59:21.579710",
     "exception": false,
     "start_time": "2023-12-26T16:59:21.544404",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Language Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca8099f5",
   "metadata": {
    "papermill": {
     "duration": 0.03535,
     "end_time": "2023-12-26T16:59:21.651638",
     "exception": false,
     "start_time": "2023-12-26T16:59:21.616288",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Language Detection with fastText\n",
    "\n",
    "There are different trained models available on the fastText website. We will be using the smaller model lid.176.ftz which has a size of less than 1 MB and is almost as accurate as the large model with 126MB. See https://fasttext.cc/docs/en/language-identification.html for instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "543f5829",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:59:21.726054Z",
     "iopub.status.busy": "2023-12-26T16:59:21.725264Z",
     "iopub.status.idle": "2023-12-26T16:59:23.136775Z",
     "shell.execute_reply": "2023-12-26T16:59:23.134821Z"
    },
    "papermill": {
     "duration": 1.453009,
     "end_time": "2023-12-26T16:59:23.140235",
     "exception": false,
     "start_time": "2023-12-26T16:59:21.687226",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-12-26 16:59:22--  https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz\r\n",
      "Resolving dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)... 3.163.189.51, 3.163.189.108, 3.163.189.14, ...\r\n",
      "Connecting to dl.fbaipublicfiles.com (dl.fbaipublicfiles.com)|3.163.189.51|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 938013 (916K) [binary/octet-stream]\r\n",
      "Saving to: 'lid.176.ftz'\r\n",
      "\r\n",
      "lid.176.ftz         100%[===================>] 916.03K  --.-KB/s    in 0.05s   \r\n",
      "\r\n",
      "2023-12-26 16:59:23 (18.4 MB/s) - 'lid.176.ftz' saved [938013/938013]\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!wget https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e5c6b8a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:59:23.215539Z",
     "iopub.status.busy": "2023-12-26T16:59:23.215047Z",
     "iopub.status.idle": "2023-12-26T16:59:23.284556Z",
     "shell.execute_reply": "2023-12-26T16:59:23.283276Z"
    },
    "papermill": {
     "duration": 0.111227,
     "end_time": "2023-12-26T16:59:23.287092",
     "exception": false,
     "start_time": "2023-12-26T16:59:23.175865",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(('__label__en', '__label__de', '__label__te'), array([0.97039694, 0.00256404, 0.00231963]))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n"
     ]
    }
   ],
   "source": [
    "import fasttext\n",
    "\n",
    "\n",
    "lang_model = fasttext.load_model(\"lid.176.ftz\")\n",
    "\n",
    "print(lang_model.predict('\"Good morning\" in German is \"Guten Morgen\"', 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1df1c32f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-26T16:59:23.360427Z",
     "iopub.status.busy": "2023-12-26T16:59:23.359885Z",
     "iopub.status.idle": "2023-12-26T16:59:23.370558Z",
     "shell.execute_reply": "2023-12-26T16:59:23.369123Z"
    },
    "papermill": {
     "duration": 0.051436,
     "end_time": "2023-12-26T16:59:23.373555",
     "exception": false,
     "start_time": "2023-12-26T16:59:23.322119",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('__label__en', '__label__de', '__label__te'),\n",
       " array([0.97039694, 0.00256404, 0.00231963]))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lang_model.predict('\"Good morning\" in German is \"Guten Morgen\"', 3)"
   ]
  },
  {
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    "The predict function takes a Unicode string as its first argument. The second, optional parameter k specifies that we want the k language labels with the highest probabilities.\n",
    "\n",
    "The model returns labels in the form `__label__<code>`, where code is the ISO 639 language code<footnote>See https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes for a complete list.</footnote> and probabilites for each label.\n",
    "\n",
    "Let's wrap the language identification into a preprocessing function. The function returns the detected language only if the calculated probability is higher than the specified threshold, otherwise, it returns the default language. This is useful for corpora like the hacker news, which is basically an English corpus with some utterances from other languages."
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    "def predict_language(text, threshold=0.8, default='en'):\n",
    "    \n",
    "    # skip language detection for very short texts\n",
    "    if len(text) < 20:\n",
    "        return default\n",
    "\n",
    "    # fasttext requires single line input\n",
    "    text = text.replace('\\n', ' ')\n",
    "    \n",
    "    labels, probas = lang_model.predict(text)\n",
    "    lang = labels[0].replace(\"__label__\", \"\")\n",
    "    proba = probas[0]\n",
    "    \n",
    "    if proba < threshold:\n",
    "        return default\n",
    "    else:\n",
    "        return lang"
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       "      <th>1</th>\n",
       "      <td>Ich mag Version 2.0 von Chat4you nicht 😡👎</td>\n",
       "      <td>de</td>\n",
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       "                                           text lang\n",
       "0       I don't like version 2.0 of Chat4you 😡👎   en\n",
       "1     Ich mag Version 2.0 von Chat4you nicht 😡👎   de\n",
       "2        Мне не нравится версия 2.0 Chat4you 😡👎   ru\n",
       "3        Não gosto da versão 2.0 do Chat4you 😡👎   pt\n",
       "4  मुझे Chat4you का संस्करण 2.0 पसंद नहीं है 😡👎   hi"
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    "data = [\"I don't like version 2.0 of Chat4you 😡👎\",   # English\n",
    "        \"Ich mag Version 2.0 von Chat4you nicht 😡👎\", # German\n",
    "        \"Мне не нравится версия 2.0 Chat4you 😡👎\",    # Russian\n",
    "        \"Não gosto da versão 2.0 do Chat4you 😡👎\",    # Portugese\n",
    "        \"मुझे Chat4you का संस्करण 2.0 पसंद नहीं है 😡👎\"]   # Hindi\n",
    "demo_df = pd.Series(data, name='text').to_frame()\n",
    "\n",
    "demo_df['lang'] = demo_df['text'].apply(predict_language)\n",
    "demo_df"
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   "source": [
    "In order to get the real names for the language codes, we can provide a mapping dictionary."
   ]
  },
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   "source": [
    "url = 'https://raw.githubusercontent.com/haliaeetus/iso-639/master/data/iso_639-1.csv'\n",
    "lang_df = pd.read_csv(url)\n",
    "lang_df = lang_df[['name', '639-1', '639-2']].melt(id_vars=['name'], var_name='iso', value_name='code')\n",
    "\n",
    "# create dictionary with entries {'code': 'name'}\n",
    "iso639_languages = lang_df.set_index('code')['name'].to_dict()"
   ]
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       "                                           text lang   lang_name\n",
       "0       I don't like version 2.0 of Chat4you 😡👎   en     English\n",
       "1     Ich mag Version 2.0 von Chat4you nicht 😡👎   de      German\n",
       "2        Мне не нравится версия 2.0 Chat4you 😡👎   ru     Russian\n",
       "3        Não gosto da versão 2.0 do Chat4you 😡👎   pt  Portuguese\n",
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