03_Basic_preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic preprocessing of the corpus\n",
    "\n",
    "In this Notebook, we will some basic preprocessing of the corpus. This is necessary to analyze and visualize the corpus in the upcoming Notebooks in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run notebook 02\n",
    "\n",
    "We need this so that we can use the variables from that notebook. You can ignore the outputs from these cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PU38l1leSjMmTJ5vLpOR1GBUVZdSrV89wdXU1smfPbrz11lvG3bt3/+HWPHmP6q/o6GijatWqRtasWQ0XFxejUKFCRr9+/YwrV67Ybedp6S/DMIwuXboY+fLlM5ydnQ1fX1+jVq1aZtAzDGsdXzbDMIx/7jwiAAAAAOCfwD17AAAAAGBBhD0AAAAAsCDCHgAAAABYEGEPAAAAACyIsAcAAAAAFkTYAwAAAAALIuwBAAAAgAUR9gAAAADAggh7AAAAAGBBhD0AAAAAsCDCHgAAAABY0P8DEgqTvyG2H50AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "variable=oaiIdentifier<br>value=%{x}<br>krantnaam=%{y}<extra></extra>",
         "legendgroup": "oaiIdentifier",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "oaiIdentifier",
         "offsetgroup": "oaiIdentifier",
         "orientation": "h",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          302,
          307,
          309
         ],
         "xaxis": "x",
         "y": [
          "De Tĳd : godsdienstig-staatkundig dagblad",
          "De standaard",
          "Het vaderland : staat- en letterkundig nieuwsblad"
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "legend": {
         "title": {
          "text": "variable"
         },
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
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            "error_y": {
             "color": "#2a3f5f"
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            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
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            },
            "type": "bar"
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          "barpolar": [
           {
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           "bgcolor": "#E5ECF6",
           "caxis": {
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            "linecolor": "white",
            "ticks": ""
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
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           "ticks": "",
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Total number of newspaper issues per day of the week"
        },
        "xaxis": {
         "anchor": "y",
         "categoryarray": [
          "Monday",
          "Tuesday",
          "Wednesday",
          "Thursday",
          "Friday",
          "Saturday"
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         "categoryorder": "array",
         "domain": [
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         "title": {
          "text": "Day"
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        },
        "yaxis": {
         "anchor": "x",
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     },
     "metadata": {},
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   ],
   "source": [
    "%run 02_Loading_data_and_visualizing_corpus.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if DataFrame is stored correctly\n",
    "\n",
    "Let's quickly check if the DataFrame is stored correctly. We do this by displaying the first 5 rows of the DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <th></th>\n",
       "      <th>identifier</th>\n",
       "      <th>oaiIdentifier</th>\n",
       "      <th>type</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>content</th>\n",
       "      <th>krantnaam</th>\n",
       "      <th>verspreidingsgebied</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13792</th>\n",
       "      <td>http://resolver.kb.nl/resolve?urn=ddd:01026234...</td>\n",
       "      <td>DDD:ddd:010262344:mpeg21</td>\n",
       "      <td>artikel</td>\n",
       "      <td>RIVIKR-VA.A.B-'</td>\n",
       "      <td>1873-01-01</td>\n",
       "      <td>&amp;M.STERDAM 31 December, ü ding n_ar: Tot Mannh...</td>\n",
       "      <td>De Tĳd : godsdienstig-staatkundig dagblad</td>\n",
       "      <td>Landelijk</td>\n",
       "      <td>January</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13747</th>\n",
       "      <td>http://resolver.kb.nl/resolve?urn=ddd:01026234...</td>\n",
       "      <td>DDD:ddd:010262344:mpeg21</td>\n",
       "      <td>artikel</td>\n",
       "      <td>?sfde van den feestdag:</td>\n",
       "      <td>1873-01-01</td>\n",
       "      <td>?sfde van den feestdag: 'ESIIIJI.EIIIS O. 11.»...</td>\n",
       "      <td>De Tĳd : godsdienstig-staatkundig dagblad</td>\n",
       "      <td>Landelijk</td>\n",
       "      <td>January</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13748</th>\n",
       "      <td>http://resolver.kb.nl/resolve?urn=ddd:01026234...</td>\n",
       "      <td>DDD:ddd:010262344:mpeg21</td>\n",
       "      <td>artikel</td>\n",
       "      <td>'^ERDAIVI, 31 December.</td>\n",
       "      <td>1873-01-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>De Tĳd : godsdienstig-staatkundig dagblad</td>\n",
       "      <td>Landelijk</td>\n",
       "      <td>January</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>http://resolver.kb.nl/resolve?urn=ddd:01106545...</td>\n",
       "      <td>DDD:ddd:011065450:mpeg21</td>\n",
       "      <td>artikel</td>\n",
       "      <td>WATERHOOGTE.</td>\n",
       "      <td>1873-01-01</td>\n",
       "      <td>M&lt;i.bh'i;n,27l&gt;cc. 10 »:. 0 dm. Gtv. 4 dm. CoU...</td>\n",
       "      <td>De standaard</td>\n",
       "      <td>Landelijk</td>\n",
       "      <td>January</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>http://resolver.kb.nl/resolve?urn=ddd:01106545...</td>\n",
       "      <td>DDD:ddd:011065450:mpeg21</td>\n",
       "      <td>artikel</td>\n",
       "      <td>KOERS VAN ANTWERPEN 30 Dec. 1872.</td>\n",
       "      <td>1873-01-01</td>\n",
       "      <td>Amiterdam ztgt £r. 210.05 Geld. fr. 210 30 pap...</td>\n",
       "      <td>De standaard</td>\n",
       "      <td>Landelijk</td>\n",
       "      <td>January</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              identifier  \\\n",
       "13792  http://resolver.kb.nl/resolve?urn=ddd:01026234...   \n",
       "13747  http://resolver.kb.nl/resolve?urn=ddd:01026234...   \n",
       "13748  http://resolver.kb.nl/resolve?urn=ddd:01026234...   \n",
       "6969   http://resolver.kb.nl/resolve?urn=ddd:01106545...   \n",
       "6968   http://resolver.kb.nl/resolve?urn=ddd:01106545...   \n",
       "\n",
       "                  oaiIdentifier     type                              title  \\\n",
       "13792  DDD:ddd:010262344:mpeg21  artikel                    RIVIKR-VA.A.B-'   \n",
       "13747  DDD:ddd:010262344:mpeg21  artikel            ?sfde van den feestdag:   \n",
       "13748  DDD:ddd:010262344:mpeg21  artikel            '^ERDAIVI, 31 December.   \n",
       "6969   DDD:ddd:011065450:mpeg21  artikel                       WATERHOOGTE.   \n",
       "6968   DDD:ddd:011065450:mpeg21  artikel  KOERS VAN ANTWERPEN 30 Dec. 1872.   \n",
       "\n",
       "            date                                            content  \\\n",
       "13792 1873-01-01  &M.STERDAM 31 December, ü ding n_ar: Tot Mannh...   \n",
       "13747 1873-01-01  ?sfde van den feestdag: 'ESIIIJI.EIIIS O. 11.»...   \n",
       "13748 1873-01-01                                                NaN   \n",
       "6969  1873-01-01  M<i.bh'i;n,27l>cc. 10 »:. 0 dm. Gtv. 4 dm. CoU...   \n",
       "6968  1873-01-01  Amiterdam ztgt £r. 210.05 Geld. fr. 210 30 pap...   \n",
       "\n",
       "                                       krantnaam verspreidingsgebied    month  \\\n",
       "13792  De Tĳd : godsdienstig-staatkundig dagblad           Landelijk  January   \n",
       "13747  De Tĳd : godsdienstig-staatkundig dagblad           Landelijk  January   \n",
       "13748  De Tĳd : godsdienstig-staatkundig dagblad           Landelijk  January   \n",
       "6969                                De standaard           Landelijk  January   \n",
       "6968                                De standaard           Landelijk  January   \n",
       "\n",
       "             day  \n",
       "13792  Wednesday  \n",
       "13747  Wednesday  \n",
       "13748  Wednesday  \n",
       "6969   Wednesday  \n",
       "6968   Wednesday  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good! Now we can move on to the actual preprocessing steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load NLP model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[SpaCy](https://spacy.io/) offers language models which you can import and use to perform natural language processing. We load the library and the appropriate Dutch model for our corpus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "from spacy.lang.nl.examples import sentences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the Dutch natural language processing (NLP) package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"nl_core_news_sm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Doc items\n",
    "\n",
    "Create a helper function. We will use this in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_text(text):\n",
    "    return nlp(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column in the pandas DataFrame, called \"doc\". This column stores the content of each article in a way that is easy to use in later steps of this tutorial. In order to make the Doc items, we first need to get rid of the rows in the dataframe that do not contain any content of the article. This is represented by a 'NaN' in the 'content' column. We can use the 'dropna' function to get rid of the NaN values. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna(subset=['content'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note that running the next cell takes approximately 15 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"doc\"] = data[\"content\"].apply(process_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <th>13792</th>\n",
       "      <td>http://resolver.kb.nl/resolve?urn=ddd:01026234...</td>\n",
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       "      <td>RIVIKR-VA.A.B-'</td>\n",
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       "      <td>Landelijk</td>\n",
       "      <td>January</td>\n",
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       "      <td>WATERHOOGTE.</td>\n",
       "      <td>1873-01-01</td>\n",
       "      <td>M&lt;i.bh'i;n,27l&gt;cc. 10 »:. 0 dm. Gtv. 4 dm. CoU...</td>\n",
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       "      <td>January</td>\n",
       "      <td>Wednesday</td>\n",
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       "      <td>KOERS VAN ANTWERPEN 30 Dec. 1872.</td>\n",
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       "      <td>De standaard</td>\n",
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       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>http://resolver.kb.nl/resolve?urn=ddd:01106545...</td>\n",
       "      <td>DDD:ddd:011065450:mpeg21</td>\n",
       "      <td>artikel</td>\n",
       "      <td>Koers van het geld bij de Ned. Bank sedert 12 ...</td>\n",
       "      <td>1873-01-01</td>\n",
       "      <td>Wissel-DUcuulo S jiCt.; Promeneu-DijconU 51') ...</td>\n",
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       "      <td>January</td>\n",
       "      <td>Wednesday</td>\n",
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       "                                              identifier  \\\n",
       "13792  http://resolver.kb.nl/resolve?urn=ddd:01026234...   \n",
       "13747  http://resolver.kb.nl/resolve?urn=ddd:01026234...   \n",
       "6969   http://resolver.kb.nl/resolve?urn=ddd:01106545...   \n",
       "6968   http://resolver.kb.nl/resolve?urn=ddd:01106545...   \n",
       "6967   http://resolver.kb.nl/resolve?urn=ddd:01106545...   \n",
       "\n",
       "                  oaiIdentifier     type  \\\n",
       "13792  DDD:ddd:010262344:mpeg21  artikel   \n",
       "13747  DDD:ddd:010262344:mpeg21  artikel   \n",
       "6969   DDD:ddd:011065450:mpeg21  artikel   \n",
       "6968   DDD:ddd:011065450:mpeg21  artikel   \n",
       "6967   DDD:ddd:011065450:mpeg21  artikel   \n",
       "\n",
       "                                                   title       date  \\\n",
       "13792                                    RIVIKR-VA.A.B-' 1873-01-01   \n",
       "13747                            ?sfde van den feestdag: 1873-01-01   \n",
       "6969                                        WATERHOOGTE. 1873-01-01   \n",
       "6968                   KOERS VAN ANTWERPEN 30 Dec. 1872. 1873-01-01   \n",
       "6967   Koers van het geld bij de Ned. Bank sedert 12 ... 1873-01-01   \n",
       "\n",
       "                                                 content  \\\n",
       "13792  &M.STERDAM 31 December, ü ding n_ar: Tot Mannh...   \n",
       "13747  ?sfde van den feestdag: 'ESIIIJI.EIIIS O. 11.»...   \n",
       "6969   M<i.bh'i;n,27l>cc. 10 »:. 0 dm. Gtv. 4 dm. CoU...   \n",
       "6968   Amiterdam ztgt £r. 210.05 Geld. fr. 210 30 pap...   \n",
       "6967   Wissel-DUcuulo S jiCt.; Promeneu-DijconU 51') ...   \n",
       "\n",
       "                                       krantnaam verspreidingsgebied    month  \\\n",
       "13792  De Tĳd : godsdienstig-staatkundig dagblad           Landelijk  January   \n",
       "13747  De Tĳd : godsdienstig-staatkundig dagblad           Landelijk  January   \n",
       "6969                                De standaard           Landelijk  January   \n",
       "6968                                De standaard           Landelijk  January   \n",
       "6967                                De standaard           Landelijk  January   \n",
       "\n",
       "             day                                                doc  \n",
       "13792  Wednesday  (&, M.STERDAM, 31, December, ,, ü, ding, n_ar,...  \n",
       "13747  Wednesday  (?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...  \n",
       "6969   Wednesday  (M, <, i.bh'i;n,27l, >, cc, ., 10, », :, ., 0,...  \n",
       "6968   Wednesday  (Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...  \n",
       "6967   Wednesday  (Wissel-DUcuulo, S, jiCt, ., ;, Promeneu-Dijco...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tokenization\n",
    "\n",
    "Tokenization refers to the process of breaking down a piece of text into small units, called 'tokens'. In our case, the tokens are the words in an article, but tokens can also consist of parts of words or characters. Tokenization is a crucial part of NLP.\n",
    "\n",
    "Create a helper function. We will use this in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_token(doc):\n",
    "    return [(token.text) for token in doc]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a \"Token\" column in the DataFrame. This column stores the words in each article as a list, which is useful in later steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['tokens'] = data['doc'].apply(get_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <td>KOERS VAN ANTWERPEN 30 Dec. 1872.</td>\n",
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       "                                              identifier  \\\n",
       "13792  http://resolver.kb.nl/resolve?urn=ddd:01026234...   \n",
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       "6968   http://resolver.kb.nl/resolve?urn=ddd:01106545...   \n",
       "6967   http://resolver.kb.nl/resolve?urn=ddd:01106545...   \n",
       "\n",
       "                  oaiIdentifier     type  \\\n",
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       "6969   DDD:ddd:011065450:mpeg21  artikel   \n",
       "6968   DDD:ddd:011065450:mpeg21  artikel   \n",
       "6967   DDD:ddd:011065450:mpeg21  artikel   \n",
       "\n",
       "                                                   title       date  \\\n",
       "13792                                    RIVIKR-VA.A.B-' 1873-01-01   \n",
       "13747                            ?sfde van den feestdag: 1873-01-01   \n",
       "6969                                        WATERHOOGTE. 1873-01-01   \n",
       "6968                   KOERS VAN ANTWERPEN 30 Dec. 1872. 1873-01-01   \n",
       "6967   Koers van het geld bij de Ned. Bank sedert 12 ... 1873-01-01   \n",
       "\n",
       "                                                 content  \\\n",
       "13792  &M.STERDAM 31 December, ü ding n_ar: Tot Mannh...   \n",
       "13747  ?sfde van den feestdag: 'ESIIIJI.EIIIS O. 11.»...   \n",
       "6969   M<i.bh'i;n,27l>cc. 10 »:. 0 dm. Gtv. 4 dm. CoU...   \n",
       "6968   Amiterdam ztgt £r. 210.05 Geld. fr. 210 30 pap...   \n",
       "6967   Wissel-DUcuulo S jiCt.; Promeneu-DijconU 51') ...   \n",
       "\n",
       "                                       krantnaam verspreidingsgebied    month  \\\n",
       "13792  De Tĳd : godsdienstig-staatkundig dagblad           Landelijk  January   \n",
       "13747  De Tĳd : godsdienstig-staatkundig dagblad           Landelijk  January   \n",
       "6969                                De standaard           Landelijk  January   \n",
       "6968                                De standaard           Landelijk  January   \n",
       "6967                                De standaard           Landelijk  January   \n",
       "\n",
       "             day                                                doc  \\\n",
       "13792  Wednesday  (&, M.STERDAM, 31, December, ,, ü, ding, n_ar,...   \n",
       "13747  Wednesday  (?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...   \n",
       "6969   Wednesday  (M, <, i.bh'i;n,27l, >, cc, ., 10, », :, ., 0,...   \n",
       "6968   Wednesday  (Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...   \n",
       "6967   Wednesday  (Wissel-DUcuulo, S, jiCt, ., ;, Promeneu-Dijco...   \n",
       "\n",
       "                                                  tokens  \n",
       "13792  [&, M.STERDAM, 31, December, ,, ü, ding, n_ar,...  \n",
       "13747  [?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...  \n",
       "6969   [M, <, i.bh'i;n,27l, >, cc, ., 10, », :, ., 0,...  \n",
       "6968   [Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...  \n",
       "6967   [Wissel-DUcuulo, S, jiCt, ., ;, Promeneu-Dijco...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatization\n",
    "\n",
    "Lemmatization is the process of reducing words to their most basic form, known as the lemma. For example, the lemma of 'running' is 'run' and the lemma of 'better' is 'good'. Lemmatization is important for NLP, because it reduces the complexity of a text, improves accuracy of many NLP tasks, and leads to better semantic understanding. \n",
    "\n",
    "Create a helper function. We will use this function in the next step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lemma(doc):\n",
    "    return [(token.lemma_) for token in doc]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create 'lemma' column in the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['lemmas'] = data['doc'].apply(get_lemma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display lemmas and tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>tokens</th>\n",
       "      <th>lemmas</th>\n",
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       "      <th>13792</th>\n",
       "      <td>[&amp;, M.STERDAM, 31, December, ,, ü, ding, n_ar,...</td>\n",
       "      <td>[&amp;, M.STERDAM, 31, December, ,, ü, ding, n_ar,...</td>\n",
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       "      <th>13747</th>\n",
       "      <td>[?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...</td>\n",
       "      <td>[?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...</td>\n",
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       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>[M, &lt;, i.bh'i;n,27l, &gt;, cc, ., 10, », :, ., 0,...</td>\n",
       "      <td>[m, &lt;, i.bh'i;n,27l, &gt;, cc, ., 10, », :, ., 0,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>[Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...</td>\n",
       "      <td>[Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...</td>\n",
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       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>[Wissel-DUcuulo, S, jiCt, ., ;, Promeneu-Dijco...</td>\n",
       "      <td>[Wissel-DUcuulo, S, JiCt, ., ;, Promeneu-Dijco...</td>\n",
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      "text/plain": [
       "                                                  tokens  \\\n",
       "13792  [&, M.STERDAM, 31, December, ,, ü, ding, n_ar,...   \n",
       "13747  [?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...   \n",
       "6969   [M, <, i.bh'i;n,27l, >, cc, ., 10, », :, ., 0,...   \n",
       "6968   [Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...   \n",
       "6967   [Wissel-DUcuulo, S, jiCt, ., ;, Promeneu-Dijco...   \n",
       "\n",
       "                                                  lemmas  \n",
       "13792  [&, M.STERDAM, 31, December, ,, ü, ding, n_ar,...  \n",
       "13747  [?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...  \n",
       "6969   [m, <, i.bh'i;n,27l, >, cc, ., 10, », :, ., 0,...  \n",
       "6968   [Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...  \n",
       "6967   [Wissel-DUcuulo, S, JiCt, ., ;, Promeneu-Dijco...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['tokens', 'lemmas']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing the tokens with the lemmas, we can see how some words have been reduced to their root form. For example, 'viel' has been changed to 'vallen'. However, we can also see that 'tweeden' has been changed to 'tweed'. This change is more questionable. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count number of words in each article\n",
    "\n",
    "In the next code block, we will count the number of words in each article and store it in a separate column, called 'article_length'. We will use this information to make a comparison between the different newspapers in the next Notebook. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>tokens</th>\n",
       "      <th>article_length</th>\n",
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       "      <th>13792</th>\n",
       "      <td>RIVIKR-VA.A.B-'</td>\n",
       "      <td>[&amp;, M.STERDAM, 31, December, ,, ü, ding, n_ar,...</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>13747</th>\n",
       "      <td>?sfde van den feestdag:</td>\n",
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       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>6969</th>\n",
       "      <td>WATERHOOGTE.</td>\n",
       "      <td>[M, &lt;, i.bh'i;n,27l, &gt;, cc, ., 10, », :, ., 0,...</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6968</th>\n",
       "      <td>KOERS VAN ANTWERPEN 30 Dec. 1872.</td>\n",
       "      <td>[Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>6967</th>\n",
       "      <td>Koers van het geld bij de Ned. Bank sedert 12 ...</td>\n",
       "      <td>[Wissel-DUcuulo, S, jiCt, ., ;, Promeneu-Dijco...</td>\n",
       "      <td>34</td>\n",
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      "text/plain": [
       "                                                   title  \\\n",
       "13792                                    RIVIKR-VA.A.B-'   \n",
       "13747                            ?sfde van den feestdag:   \n",
       "6969                                        WATERHOOGTE.   \n",
       "6968                   KOERS VAN ANTWERPEN 30 Dec. 1872.   \n",
       "6967   Koers van het geld bij de Ned. Bank sedert 12 ...   \n",
       "\n",
       "                                                  tokens  article_length  \n",
       "13792  [&, M.STERDAM, 31, December, ,, ü, ding, n_ar,...              41  \n",
       "13747  [?, sfde, van, den, feestdag, :, ', ESIIIJI.EI...              18  \n",
       "6969   [M, <, i.bh'i;n,27l, >, cc, ., 10, », :, ., 0,...              74  \n",
       "6968   [Amiterdam, ztgt, £, r., 210.05, Geld, ., fr.,...              21  \n",
       "6967   [Wissel-DUcuulo, S, jiCt, ., ;, Promeneu-Dijco...              34  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Retrieve the length of each article in the corpus and store it in the DataFrame\n",
    "\n",
    "## Create empty list to store the lengths\n",
    "article_lengths = []\n",
    "\n",
    "## Retrieve length of each article and store in list\n",
    "for index, row in data.iterrows():\n",
    "    article_lengths.append(len(row['tokens']))\n",
    "\n",
    "## Append list to DataFrame\n",
    "data['article_length'] = article_lengths\n",
    "\n",
    "## Show the first rows of title, tokens and article length in DataFrame                                    \n",
    "data[['title', 'tokens', 'article_length']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write new dataset into separate file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('data_preprocessed.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a created a new file, called 'data_preprocessed.csv'. We will use this in the next Notebooks."
   ]
  }
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