CMM_R2-The good and bad of Economy analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Communication Methods & Measures - R2\n",
    "\n",
    "### Main changes:\n",
    "* Creating scatterplots for each pair of classifiers\n",
    "* Reporting Krippendorff's alpha (and confidence intervals) instead of correlations\n",
    "* \"readability_score\" module used: https://github.com/wimmuskee/readability-score)\n",
    "* For Krippendorff's Alpha calculations: used same predictions as in R0 (used for precision/recall) yet with KAlpha = ordinal\n",
    "\n",
    "\n",
    "\n",
    "# Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure IPython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# activate plots\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from scripts_r2 import analyze # some analysis helper functions for this paper\n",
    "import pandas \n",
    "import datetime\n",
    "import os\n",
    "import numpy\n",
    "import statsmodels.formula.api as sm\n",
    "import seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## prepare some visualization stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "seaborn.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "corplot = lambda cortable, title: seaborn.heatmap(cortable, linewidth=.1, annot=True, cmap=\"RdBu_r\", vmin=-1, vmax=1).set_title(title)\n",
    "tool_order = [ \"recessie\" , \"boukes\",\"LIWC\",\"sentistrength\",\"pattern\",\"polyglot\",\"DANEW\"]\n",
    "text_order = [\"text_\"+tool for tool in tool_order]\n",
    "title_order = [\"title_\"+tool for tool in tool_order]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "path_to_files = \"./data\"\n",
    "coded_files   = \"./results/data_with_sentiment.csv\"\n",
    "os.makedirs('results',exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do or Load Sentiment analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using previously stored computations\n",
      "filtered out 215 observations\n"
     ]
    }
   ],
   "source": [
    "# If sentiments have not yet been computed\n",
    "if not os.path.exists(coded_files):\n",
    "    print(\"Starting from scratch\")\n",
    "    raw_data = analyze.load_files(path_to_files)\n",
    "    print(\"Recoding\")\n",
    "    rec_data = analyze.recode_annotations(raw_data) \n",
    "    print(\"Adding sentiment\")\n",
    "    sen_data = analyze.add_sentiments(analyze.add_sentiments(rec_data,'text'),'title')\n",
    "    print(\"Adding linguistic features for error analysis\")\n",
    "    tex_data = analyze.add_text_properties(analyze.add_text_properties(sen_data,'text'),'title')\n",
    "    print(\"Writing results to disk\")\n",
    "    tex_data.to_csv(coded_files)\n",
    "    data = tex_data\n",
    "# If sentiments have been computed\n",
    "else:\n",
    "    print(\"Using previously stored computations\")\n",
    "    data = pandas.read_csv(coded_files)\n",
    "\n",
    "data.index = data.ID\n",
    "\n",
    "# Add LIWC results if available\n",
    "if \"LIWC2015 Results (ID_Text.csv).csv\" in os.listdir(path_to_files):\n",
    "    text_liwc = pandas.read_csv(os.path.join(path_to_files,\"LIWC2015 Results (ID_Text.csv).csv\"))\n",
    "    text_liwc = pandas.DataFrame({'ID':text_liwc.A, 'text_LIWC':text_liwc.Posemo - text_liwc.Negemo}).loc[2:,:]\n",
    "    text_liwc.index = text_liwc.ID.astype(\"float64\")\n",
    "    data = data.join(text_liwc.drop('ID',1))\n",
    "    \n",
    "if \"LIWC2015 Results (ID_Title.csv).csv\" in os.listdir(path_to_files):\n",
    "    title_liwc = pandas.read_csv(os.path.join(path_to_files,\"LIWC2015 Results (ID_Title.csv).csv\"))\n",
    "    title_liwc = pandas.DataFrame({'ID':title_liwc.A, 'title_LIWC':title_liwc.Posemo - title_liwc.Negemo}).loc[2:,:]\n",
    "    title_liwc.index = title_liwc.ID.astype(\"float64\")\n",
    "    data = data.join(title_liwc.drop('ID',1))\n",
    "    \n",
    "# Set time-based index\n",
    "todate = lambda d: datetime.datetime.strptime(d.replace('T',' ').split('.')[0],'%Y-%m-%d %H:%M:%S')\n",
    "data.index = data.date_y.map(todate) # saving as CSV breaks the timeindex, so we reconstruct it here\n",
    "    \n",
    "\n",
    "# Filter out timepoints after which data becomes too sparse\n",
    "filtered = (data.index >= datetime.datetime(year=2015,month=7,day=7)).sum()\n",
    "data = data[data.index < datetime.datetime(year=2015,month=7,day=7)] \n",
    "print(\"filtered out\",filtered, \"observations\")\n",
    "\n",
    "data = data[~data.index.dayofweek.isin([0,6])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_y                         0\n",
       "Codeur                         0\n",
       "Day_in_2015                    0\n",
       "Economisch_Ja                  0\n",
       "Hoofd_OW                     120\n",
       "ID                             0\n",
       "Posit_Nega                   121\n",
       "Source                         0\n",
       "Toon_Kop                     114\n",
       "date_x                         0\n",
       "headline                       0\n",
       "outlet                         0\n",
       "date_y.1                       0\n",
       "type                           0\n",
       "source                         0\n",
       "title                          0\n",
       "text                           0\n",
       "online                         0\n",
       "quality                        0\n",
       "title_gold                  1166\n",
       "text_gold                   4380\n",
       "text_polyglot                  0\n",
       "text_pattern                   0\n",
       "text_DANEW                     0\n",
       "text_boukes                    0\n",
       "text_sentistrength             0\n",
       "text_recessie                  0\n",
       "title_polyglot                 0\n",
       "title_pattern                  0\n",
       "title_DANEW                    0\n",
       "title_boukes                   0\n",
       "title_sentistrength            0\n",
       "title_recessie                 0\n",
       "Flesch_text                    0\n",
       "letter_count_text              0\n",
       "polysyllword_count_text        0\n",
       "sent_count_text                0\n",
       "sentlen_average_text           0\n",
       "simpleword_count_text          0\n",
       "syll_count_text                0\n",
       "word_count_text                0\n",
       "wordlen_average_text           0\n",
       "wordletter_average_text        0\n",
       "wordsent_average_text          0\n",
       "Flesch_title                   0\n",
       "letter_count_title             0\n",
       "polysyllword_count_title       0\n",
       "sent_count_title               0\n",
       "sentlen_average_title          0\n",
       "simpleword_count_title         0\n",
       "syll_count_title               0\n",
       "word_count_title               0\n",
       "wordlen_average_title          0\n",
       "wordletter_average_title       0\n",
       "wordsent_average_title         0\n",
       "text_LIWC                      0\n",
       "title_LIWC                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## order columns appropriately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data.index = range(len(data))\n",
    "red = data.copy()\n",
    "data = data.drop(text_order,axis=1)\n",
    "data = data.drop(title_order,axis=1)\n",
    "data = data.join(red[text_order])\n",
    "data = data.join(red[title_order])\n",
    "del(red)\n",
    "data.index = data.index = data.date_y.map(todate) # saving as CSV breaks the timeindex, so we reconstruct it here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option to toggle non-relevant human annotations to '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#data.text_gold = data.text_gold.replace({numpy.nan:0})\n",
    "#data.title_gold = data.title_gold.replace({numpy.nan:0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resulting crosstabs for gold annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fulltext crosstab\n"
     ]
    },
    {
     "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>online</th>\n",
       "      <th>online_%</th>\n",
       "      <th>offline</th>\n",
       "      <th>offline_%</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-2.0</th>\n",
       "      <td>61</td>\n",
       "      <td>0.24</td>\n",
       "      <td>265</td>\n",
       "      <td>0.23</td>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>43</td>\n",
       "      <td>0.17</td>\n",
       "      <td>225</td>\n",
       "      <td>0.19</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.04</td>\n",
       "      <td>120</td>\n",
       "      <td>0.10</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>64</td>\n",
       "      <td>0.25</td>\n",
       "      <td>274</td>\n",
       "      <td>0.23</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>77</td>\n",
       "      <td>0.30</td>\n",
       "      <td>287</td>\n",
       "      <td>0.25</td>\n",
       "      <td>364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      online  online_%  offline  offline_%  total\n",
       "-2.0      61      0.24      265       0.23    326\n",
       "-1.0      43      0.17      225       0.19    268\n",
       " 0.0      10      0.04      120       0.10    130\n",
       " 1.0      64      0.25      274       0.23    338\n",
       " 2.0      77      0.30      287       0.25    364"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Fulltext crosstab\")\n",
    "pandas.DataFrame(\n",
    "    {\n",
    "        'online':data.text_gold[data.online].value_counts(),\n",
    "        'online_%': round(data.text_gold[data.online].value_counts()/data.text_gold[data.online].value_counts().sum(),2),\n",
    "        'offline':data.text_gold[~data.online].value_counts(),\n",
    "        'offline_%': round(data.text_gold[~data.online].value_counts()/data.text_gold[~data.online].value_counts().sum(),2),\n",
    "        'total': data.text_gold.value_counts()\n",
    "    }\n",
    ").sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Headline crosstab\n"
     ]
    },
    {
     "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>online</th>\n",
       "      <th>online_%</th>\n",
       "      <th>offline</th>\n",
       "      <th>offline_%</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.0</th>\n",
       "      <td>244</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>368</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1640</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>173</td>\n",
       "      <td>0.22</td>\n",
       "      <td>935</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      online  online_%  offline  offline_%  total\n",
       "-1.0     244      0.31     1280       0.33   1524\n",
       " 0.0     368      0.47     1640       0.43   2008\n",
       " 1.0     173      0.22      935       0.24   1108"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Headline crosstab\")\n",
    "pandas.DataFrame(\n",
    "    {\n",
    "        'online':data.title_gold[data.online].value_counts(),\n",
    "        'online_%': round(data.title_gold[data.online].value_counts()/data.title_gold[data.online].value_counts().sum(),2),\n",
    "        'offline':data.title_gold[~data.online].value_counts(),\n",
    "        'offline_%': round(data.title_gold[~data.online].value_counts()/data.title_gold[~data.online].value_counts().sum(),2),\n",
    "        'total': data.title_gold.value_counts()\n",
    "    }\n",
    ").sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing fleiss-scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Headline Fleiss score, N:  (-0.054945054945054944, 8)\n",
      "Fulltext Fleiss score, N:  (0.23809523809523808, 8)\n"
     ]
    }
   ],
   "source": [
    "annotations = pandas.read_csv(os.path.join(path_to_files, \"Inhoudsanalyse_AllesMerged_noICR_toneOnly.csv\"),delimiter=\";\")\n",
    "annotations = annotations.replace({\" \":numpy.nan})\n",
    "#annotations[[\"Toon_Kop\",\"Posit_Nega\"]] = annotations[[\"Toon_Kop\",\"Posit_Nega\"]].astype(float)\n",
    "\n",
    "\n",
    "print(\"Headline Fleiss score, N: \", analyze.calculate_intercoder_reliability(annotations, \"ID\",\"Codeur\",\"Toon_Kop\"))\n",
    "print(\"Fulltext Fleiss score, N: \", analyze.calculate_intercoder_reliability(annotations, \"ID\",\"Codeur\",\"Posit_Nega\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add 'the best of the best' models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "data = analyze.z_best(data, 'title', list(reversed([\"LIWC\",\"polyglot\",\"sentistrength\",\"DANEW\"])))\n",
    "data = analyze.z_best(data, 'text', list(reversed([\"LIWC\",\"polyglot\",\"sentistrength\",\"DANEW\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agregations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "article_data = data.copy()\n",
    "daily_data   = data.copy().resample(\"1D\").mean()\n",
    "weekly_data  = data.copy().resample(\"1W\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0e12ab4630>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "article_data.resample(\"1D\").count().date_y.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# article level errors\n",
    "article_text_errors  = analyze.calculate_errors(article_data,'text')\n",
    "article_title_errors = analyze.calculate_errors(article_data,'title')\n",
    "# daily level errors\n",
    "daily_text_errors  = analyze.calculate_errors(daily_data, 'text')\n",
    "daily_title_errors = analyze.calculate_errors(daily_data, 'title')\n",
    "# weekly level errors\n",
    "weekly_text_errors  = analyze.calculate_errors(weekly_data, 'text')\n",
    "weekly_title_errors = analyze.calculate_errors(weekly_data, 'title')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatterplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     count      mean       std       min       25%       50%  \\\n",
      "text_recessie       1426.0 -0.039271  0.194306 -1.000000  0.000000  0.000000   \n",
      "text_boukes         1426.0  0.575736  1.708661 -9.000000  0.000000  0.000000   \n",
      "text_LIWC           1426.0  0.265035  1.197659 -4.000000 -0.430000  0.000000   \n",
      "text_sentistrength  1426.0 -0.686536  0.939160 -3.000000 -1.000000 -1.000000   \n",
      "text_pattern        1426.0  0.039773  0.099160 -0.575000 -0.014911  0.040563   \n",
      "text_polyglot       1426.0  0.165750  0.223018 -1.000000  0.032258  0.166667   \n",
      "text_DANEW          1426.0  0.045519  0.025625 -0.086960  0.029726  0.044263   \n",
      "text_gold           1426.0  0.102384  1.533498 -2.000000 -1.000000  0.000000   \n",
      "text_top3           1426.0  0.089412  0.722280 -2.020815 -0.375797  0.078692   \n",
      "\n",
      "                         75%        max  \n",
      "text_recessie       0.000000   0.000000  \n",
      "text_boukes         1.000000  14.000000  \n",
      "text_LIWC           0.910000   8.700000  \n",
      "text_sentistrength  0.000000   2.000000  \n",
      "text_pattern        0.098846   0.600000  \n",
      "text_polyglot       0.299561   1.000000  \n",
      "text_DANEW          0.060010   0.150069  \n",
      "text_gold           2.000000   2.000000  \n",
      "text_top3           0.534656   3.291406  \n"
     ]
    }
   ],
   "source": [
    "analyze.export_scatterplots(article_data, \"text\", tool_order, \"scatterplots_r2\", standardization=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     count        mean         std  min         25%  \\\n",
      "text_recessie       1426.0   -0.039271    0.194306 -1.0    0.000000   \n",
      "text_boukes         1426.0   -0.167327    0.148579 -1.0   -0.217391   \n",
      "text_LIWC           1426.0   -0.328341    0.188608 -1.0   -0.437795   \n",
      "text_sentistrength  1426.0   -0.074614    0.375664 -1.0   -0.200000   \n",
      "text_pattern        1426.0    0.046421    0.168783 -1.0   -0.046656   \n",
      "text_polyglot       1426.0    0.165750    0.223018 -1.0    0.032258   \n",
      "text_DANEW          1426.0    0.117831    0.216215 -1.0   -0.015425   \n",
      "text_gold           1426.0    0.051192    0.766749 -1.0   -0.500000   \n",
      "text_top3           1426.0   -0.205520    0.271931 -1.0   -0.380666   \n",
      "ID                  1426.0  712.500000  411.795054  0.0  356.250000   \n",
      "\n",
      "                           50%          75%     max  \n",
      "text_recessie         0.000000     0.000000     0.0  \n",
      "text_boukes          -0.217391    -0.130435     1.0  \n",
      "text_LIWC            -0.370079    -0.226772     1.0  \n",
      "text_sentistrength   -0.200000     0.200000     1.0  \n",
      "text_pattern          0.047766     0.146972     1.0  \n",
      "text_polyglot         0.166667     0.299561     1.0  \n",
      "text_DANEW            0.107234     0.240106     1.0  \n",
      "text_gold             0.000000     1.000000     1.0  \n",
      "text_top3            -0.209556    -0.037890     1.0  \n",
      "ID                  712.500000  1068.750000  1425.0  \n"
     ]
    }
   ],
   "source": [
    "analyze.export_scatterplots(article_data, \"text\", tool_order, \"scatterplots_r2\", standardization='min-max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     count          mean         std       min         25%  \\\n",
      "text_recessie       1426.0 -3.927069e-02    0.194306 -1.000000    0.000000   \n",
      "text_boukes         1426.0 -6.975875e-17    1.000351 -5.606200   -0.337070   \n",
      "text_LIWC           1426.0 -3.114230e-17    1.000351 -3.562392   -0.580532   \n",
      "text_sentistrength  1426.0  2.491384e-17    1.000351 -2.464197   -0.333888   \n",
      "text_pattern        1426.0  1.993107e-17    1.000351 -6.201974   -0.551657   \n",
      "text_polyglot       1426.0  3.737076e-17    1.000351 -5.228984   -0.598781   \n",
      "text_DANEW          1426.0  1.245692e-17    1.000351 -5.171816   -0.616530   \n",
      "text_gold           1426.0 -3.737076e-17    1.000351 -1.371454   -0.719121   \n",
      "text_top3           1426.0  1.494830e-17    1.000351 -2.922646   -0.644310   \n",
      "ID                  1426.0  7.125000e+02  411.795054  0.000000  356.250000   \n",
      "\n",
      "                           50%          75%          max  \n",
      "text_recessie         0.000000     0.000000     0.000000  \n",
      "text_boukes          -0.337070     0.248389     7.859354  \n",
      "text_LIWC            -0.221372     0.538710     7.045347  \n",
      "text_sentistrength   -0.333888     0.731267     2.861576  \n",
      "text_pattern          0.007971     0.595951     5.651710  \n",
      "text_polyglot         0.004110     0.600210     3.742034  \n",
      "text_DANEW           -0.049030     0.565722     4.081487  \n",
      "text_gold            -0.066789     1.237877     1.237877  \n",
      "text_top3            -0.014848     0.616659     4.434733  \n",
      "ID                  712.500000  1068.750000  1425.000000  \n"
     ]
    }
   ],
   "source": [
    "analyze.export_scatterplots(article_data, \"text\", tool_order, \"scatterplots_r2\", standardization='z-score')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      count      mean       std         min     25%       50%  \\\n",
      "title_recessie       4640.0 -0.001293  0.035940   -1.000000  0.0000  0.000000   \n",
      "title_boukes         4640.0  0.016595  0.249043   -2.000000  0.0000  0.000000   \n",
      "title_LIWC           4640.0 -0.249030  8.622618 -100.000000  0.0000  0.000000   \n",
      "title_sentistrength  4640.0 -0.241164  0.808334   -3.000000  0.0000  0.000000   \n",
      "title_pattern        4640.0 -0.002780  0.225720   -1.000000  0.0000  0.000000   \n",
      "title_polyglot       4640.0  0.045520  0.697669   -1.000000 -0.2125  0.000000   \n",
      "title_DANEW          4640.0  0.056064  0.194030   -1.326222  0.0000  0.020307   \n",
      "title_gold           4640.0 -0.089655  0.747880   -1.000000 -1.0000  0.000000   \n",
      "title_top3           4640.0 -0.003418  0.722481   -4.101097 -0.4002  0.085527   \n",
      "\n",
      "                          75%         max  \n",
      "title_recessie       0.000000    0.000000  \n",
      "title_boukes         0.000000    2.000000  \n",
      "title_LIWC           0.000000  100.000000  \n",
      "title_sentistrength  0.000000    3.000000  \n",
      "title_pattern        0.000000    0.960000  \n",
      "title_polyglot       1.000000    1.000000  \n",
      "title_DANEW          0.138981    2.293778  \n",
      "title_gold           0.000000    1.000000  \n",
      "title_top3           0.571253    4.272150  \n"
     ]
    }
   ],
   "source": [
    "analyze.export_scatterplots(article_data, \"title\", tool_order, \"scatterplots_r2\", standardization=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      count         mean          std  min          25%  \\\n",
      "title_recessie       4640.0    -0.001293     0.035940 -1.0     0.000000   \n",
      "title_boukes         4640.0     0.008297     0.124522 -1.0     0.000000   \n",
      "title_LIWC           4640.0    -0.002490     0.086226 -1.0     0.000000   \n",
      "title_sentistrength  4640.0    -0.080388     0.269445 -1.0     0.000000   \n",
      "title_pattern        4640.0     0.017571     0.230326 -1.0     0.020408   \n",
      "title_polyglot       4640.0     0.045520     0.697669 -1.0    -0.212500   \n",
      "title_DANEW          4640.0    -0.236306     0.107199 -1.0    -0.267280   \n",
      "title_gold           4640.0    -0.089655     0.747880 -1.0    -1.000000   \n",
      "title_top3           4640.0    -0.021245     0.172569 -1.0    -0.116019   \n",
      "ID                   4640.0  2319.500000  1339.596954  0.0  1159.750000   \n",
      "\n",
      "                              50%          75%     max  \n",
      "title_recessie       0.000000e+00     0.000000     0.0  \n",
      "title_boukes         0.000000e+00     0.000000     1.0  \n",
      "title_LIWC           0.000000e+00     0.000000     1.0  \n",
      "title_sentistrength  0.000000e+00     0.000000     1.0  \n",
      "title_pattern        2.040816e-02     0.020408     1.0  \n",
      "title_polyglot       0.000000e+00     1.000000     1.0  \n",
      "title_DANEW         -2.560613e-01    -0.190495     1.0  \n",
      "title_gold           0.000000e+00     0.000000     1.0  \n",
      "title_top3          -2.220446e-16     0.116019     1.0  \n",
      "ID                   2.319500e+03  3479.250000  4639.0  \n"
     ]
    }
   ],
   "source": [
    "analyze.export_scatterplots(article_data, \"title\", tool_order, \"scatterplots_r2\", standardization='min-max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      count          mean          std        min  \\\n",
      "title_recessie       4640.0 -1.293103e-03     0.035940  -1.000000   \n",
      "title_boukes         4640.0 -7.656711e-19     1.000108  -8.098246   \n",
      "title_LIWC           4640.0  1.531342e-17     1.000108 -11.569771   \n",
      "title_sentistrength  4640.0 -3.062684e-17     1.000108  -3.413360   \n",
      "title_pattern        4640.0 -3.062684e-18     1.000108  -4.418431   \n",
      "title_polyglot       4640.0 -1.837611e-17     1.000108  -1.498752   \n",
      "title_DANEW          4640.0  3.062684e-18     1.000108  -7.124836   \n",
      "title_gold           4640.0 -1.225074e-17     1.000108  -1.217366   \n",
      "title_top3           4640.0  9.188053e-18     1.000108  -5.672284   \n",
      "ID                   4640.0  2.319500e+03  1339.596954   0.000000   \n",
      "\n",
      "                             25%          50%          75%          max  \n",
      "title_recessie          0.000000     0.000000     0.000000     0.000000  \n",
      "title_boukes           -0.066642    -0.066642    -0.066642     7.964963  \n",
      "title_LIWC              0.028884     0.028884     0.028884    11.627540  \n",
      "title_sentistrength     0.298379     0.298379     0.298379     4.010118  \n",
      "title_pattern           0.012317     0.012317     0.012317     4.265835  \n",
      "title_polyglot         -0.369871    -0.065252     1.368247     1.368247  \n",
      "title_DANEW            -0.288976    -0.184308     0.427388    11.534036  \n",
      "title_gold             -1.217366     0.119892     0.119892     1.457150  \n",
      "title_top3             -0.549252     0.123124     0.795499     5.918532  \n",
      "ID                   1159.750000  2319.500000  3479.250000  4639.000000  \n"
     ]
    }
   ],
   "source": [
    "analyze.export_scatterplots(article_data, \"title\", tool_order, \"scatterplots_r2\", standardization='z-score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intercoder reliability across tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text_recessie\n",
      "text_boukes\n",
      "text_LIWC\n",
      "text_sentistrength\n",
      "text_pattern\n",
      "text_polyglot\n",
      "text_DANEW\n",
      "text_top4\n",
      "text_top3\n",
      "text_top2\n",
      "text_top1\n"
     ]
    }
   ],
   "source": [
    "analyze.export_for_check_intercoder(article_data,'text', 'kalpha_results_r2', trinairize=True,trinary_cutoff=0.5, beta=1, errors=False, normalization='zscore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "title_recessie\n",
      "title_boukes\n",
      "title_LIWC\n",
      "title_sentistrength\n",
      "title_pattern\n",
      "title_polyglot\n",
      "title_DANEW\n",
      "title_top4\n",
      "title_top3\n",
      "title_top2\n",
      "title_top1\n"
     ]
    }
   ],
   "source": [
    "analyze.export_for_check_intercoder(article_data,'title', 'kalpha_results_r2', trinairize=True,trinary_cutoff=0.5, beta=1, errors=False, normalization='zscore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5806.000000\n",
       "mean       -0.204487\n",
       "std         9.007601\n",
       "min      -100.000000\n",
       "25%         0.000000\n",
       "50%         0.000000\n",
       "75%         0.000000\n",
       "max       100.000000\n",
       "Name: title_LIWC, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['title_LIWC'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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