src/Study2_QuantitativeAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description\n",
    "This open-source notebook is a companion to `QualitativeAnalysis.ipynb` and was written for the quantitative analysis presented in Mack et al., *\"What Do We Mean by 'Accessibility Research'? A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019\"*, CHI'21. \n",
    "\n",
    "It reports on the larger programmatic analysis of the last 26-years of accessibility papers (since the founding of ASSETS); *N=*836\n",
    "\n",
    "It relates to Section 4.4 of the Mack et al. paper. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import random\n",
    "import os\n",
    "import html\n",
    "import csv\n",
    "import re\n",
    "import itertools\n",
    "from collections import Counter, defaultdict\n",
    "from nltk.tokenize import word_tokenize\n",
    "from typing import List, Dict, Iterable, Tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_DIR = \"../datasets\"\n",
    "KEYWORD_MAPPING_FILE = os.path.join(DATASET_DIR, 'Dataset2_KeywordMappings.csv')\n",
    "A11Y_PAPER_METADATA_FILE = os.path.join(DATASET_DIR, 'Dataset2_PaperMetadata_ASSETSandCHI_1994-2019.csv')\n",
    "ALL_PAPER_METADATA_FILE = os.path.join(DATASET_DIR, 'Dataset2_PaperMetadata_AllCHI.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counts by venue (ASSETS and CHI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>Year</th>\n",
       "      <th>ASSETS</th>\n",
       "      <th>CHI</th>\n",
       "      <th>CHI total</th>\n",
       "      <th>CHI proportion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>66</td>\n",
       "      <td>0.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1996</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0.018182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1997</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>0.018182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>81</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1999</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>78</td>\n",
       "      <td>0.025641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>69</td>\n",
       "      <td>0.014493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2002</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>75</td>\n",
       "      <td>0.053333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2004</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>93</td>\n",
       "      <td>0.021505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>93</td>\n",
       "      <td>0.053763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>151</td>\n",
       "      <td>0.046358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>27</td>\n",
       "      <td>7</td>\n",
       "      <td>181</td>\n",
       "      <td>0.038674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2008</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>218</td>\n",
       "      <td>0.022936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2009</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>277</td>\n",
       "      <td>0.028881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2010</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>302</td>\n",
       "      <td>0.023179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2011</td>\n",
       "      <td>27</td>\n",
       "      <td>11</td>\n",
       "      <td>409</td>\n",
       "      <td>0.026895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2012</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>369</td>\n",
       "      <td>0.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013</td>\n",
       "      <td>26</td>\n",
       "      <td>16</td>\n",
       "      <td>392</td>\n",
       "      <td>0.040816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>465</td>\n",
       "      <td>0.017204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>484</td>\n",
       "      <td>0.043388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>545</td>\n",
       "      <td>0.040367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>32</td>\n",
       "      <td>26</td>\n",
       "      <td>600</td>\n",
       "      <td>0.043333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>27</td>\n",
       "      <td>40</td>\n",
       "      <td>665</td>\n",
       "      <td>0.060150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>40</td>\n",
       "      <td>55</td>\n",
       "      <td>702</td>\n",
       "      <td>0.078348</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  ASSETS  CHI  CHI total  CHI proportion\n",
       "0   1994      22    0         69        0.000000\n",
       "1   1995       0    2         66        0.030303\n",
       "2   1996      21    1         55        0.018182\n",
       "3   1997       0    1         55        0.018182\n",
       "4   1998      31    0         81        0.000000\n",
       "5   1999       0    2         78        0.025641\n",
       "6   2000      27    0         72        0.000000\n",
       "7   2001       0    1         69        0.014493\n",
       "8   2002      31    0         61        0.000000\n",
       "9   2003       0    4         75        0.053333\n",
       "10  2004      25    2         93        0.021505\n",
       "11  2005      23    5         93        0.053763\n",
       "12  2006      28    7        151        0.046358\n",
       "13  2007      27    7        181        0.038674\n",
       "14  2008      29    5        218        0.022936\n",
       "15  2009      26    8        277        0.028881\n",
       "16  2010      28    7        302        0.023179\n",
       "17  2011      27   11        409        0.026895\n",
       "18  2012      25    9        369        0.024390\n",
       "19  2013      26   16        392        0.040816\n",
       "20  2014      28    8        465        0.017204\n",
       "21  2015      30   21        484        0.043388\n",
       "22  2016      23   22        545        0.040367\n",
       "23  2017      32   26        600        0.043333\n",
       "24  2018      27   40        665        0.060150\n",
       "25  2019      40   55        702        0.078348"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split quant data (N = 836) by CHI and ASSETS and compute CHI proportion\n",
    "df_all = pd.read_csv(ALL_PAPER_METADATA_FILE)\n",
    "df_all_chi = df_all[df_all['venue'] == 'CHI']\n",
    "all_chi_counts = Counter(df_all_chi.year)\n",
    "\n",
    "df_a11y = pd.read_csv(A11Y_PAPER_METADATA_FILE)\n",
    "df_a11y_assets = df_a11y[df_a11y['venue'] == 'ASSETS']\n",
    "df_a11y_chi = df_a11y[df_a11y['venue'] == 'CHI']\n",
    "a11y_assets_counts = Counter(df_a11y_assets.year)\n",
    "a11y_chi_counts = Counter(df_a11y_chi.year)\n",
    "\n",
    "grid = []\n",
    "for yr in range(1994, 2020):\n",
    "    chi_prop = a11y_chi_counts[yr] / all_chi_counts[yr]\n",
    "    grid.append([yr, a11y_assets_counts[yr], a11y_chi_counts[yr], all_chi_counts[yr], chi_prop])\n",
    "    \n",
    "headers = ['Year', 'ASSETS', 'CHI', 'CHI total', 'CHI proportion']\n",
    "df_assets_chi_proportion = pd.DataFrame(grid, columns=headers)\n",
    "\n",
    "display(df_assets_chi_proportion)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 396x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot counts over time\n",
    "row_inds_lt_2004 = df_assets_chi_proportion['Year'] < 2004\n",
    "row_inds_gteq_2004 = df_assets_chi_proportion['Year'] >= 2004\n",
    "\n",
    "# biennial range of ASSETS\n",
    "df_assets_biennial = df_assets_chi_proportion[['Year','ASSETS']][row_inds_lt_2004]\n",
    "df_assets_biennial = df_assets_biennial[df_assets_biennial['ASSETS'] != 0]\n",
    "\n",
    "# annual range of ASSETS and CHI\n",
    "df_assets_annual = df_assets_chi_proportion[['Year','ASSETS']][row_inds_gteq_2004]\n",
    "df_chi_annual = df_assets_chi_proportion[['Year','CHI']]\n",
    "\n",
    "annual_counts = []\n",
    "for idx, entry in df_assets_annual.iterrows():\n",
    "    annual_counts.append([entry['Year'], entry['ASSETS'], 'ASSETS'])\n",
    "for idx, entry in df_chi_annual.iterrows():\n",
    "    annual_counts.append([entry['Year'], entry['CHI'], 'CHI'])\n",
    "    \n",
    "df_annual = pd.DataFrame(annual_counts, columns=['Year', 'Count', 'Venue'])\n",
    "\n",
    "plt.close()\n",
    "fig, ax = plt.subplots(figsize=(5.5, 4))\n",
    "# ax = sns.set_style(style=None, rc=None )\n",
    "\n",
    "colors = sns.color_palette()\n",
    "\n",
    "sns.lineplot(\n",
    "    ax=ax,\n",
    "    data=df_assets_biennial,\n",
    "    x='Year',\n",
    "    y='ASSETS',\n",
    "    marker='o', \n",
    "    linestyle='',\n",
    "    color=colors[0]\n",
    ")\n",
    "sns.lineplot(\n",
    "    ax=ax,\n",
    "    data=df_annual,\n",
    "    x='Year',\n",
    "    y='Count',\n",
    "    hue='Venue',\n",
    "    markers=True,\n",
    "    color=colors[0]\n",
    ")\n",
    "\n",
    "ax.set_title('Number of accessibility papers per year', fontsize=14)\n",
    "ax.set_ylabel('Papers')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 396x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot proportion of CHI papers on accessbility\n",
    "plt.close()\n",
    "fig, ax = plt.subplots(figsize=(5.5, 4))\n",
    "\n",
    "df_assets_chi_proportion['CHI percent'] = 100 * df_assets_chi_proportion['CHI proportion']\n",
    "\n",
    "sns.lineplot(\n",
    "    ax=ax,\n",
    "    data=df_assets_chi_proportion,\n",
    "    x='Year',\n",
    "    y='CHI percent',\n",
    "    color=colors[1],\n",
    "    markers=True\n",
    ")\n",
    "\n",
    "fmt = '%.0f%%' \n",
    "yticks = mtick.FormatStrFormatter(fmt)\n",
    "ax.yaxis.set_major_formatter(yticks)\n",
    "ax.set_title('Proportion of CHI papers on accessibility', fontsize=14)\n",
    "ax.set_ylabel('Percentage')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Keyword analysis\n",
    "\n",
    "We define three keyword groups, for keywords related to community of focus, methods, and technology.\n",
    "\n",
    "Keyword mappings are available in `dataset/Dataset2_KeywordMappings.csv`. Any author keywords that occur at least twice in our set of papers was annotated by two authors for keyword group, and a cluster code within that group.\n",
    "\n",
    "We enrich the author keywords by detecting author keywords in the title and abstract of each paper in our dataset. We aggregate all keywords by categories as defined in the paper.\n",
    "\n",
    "We then plot keyword trends over time based on these categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>keyword</th>\n",
       "      <th>theme_code</th>\n",
       "      <th>category_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blind</td>\n",
       "      <td>human</td>\n",
       "      <td>blind and low vision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>visual impairment</td>\n",
       "      <td>human</td>\n",
       "      <td>blind and low vision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>blindness</td>\n",
       "      <td>human</td>\n",
       "      <td>blind and low vision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>visually impaired</td>\n",
       "      <td>human</td>\n",
       "      <td>blind and low vision</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>older adults</td>\n",
       "      <td>human</td>\n",
       "      <td>older adults</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>wheelchair</td>\n",
       "      <td>technology</td>\n",
       "      <td>wheelchair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>white cane</td>\n",
       "      <td>technology</td>\n",
       "      <td>white cane</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>camera</td>\n",
       "      <td>technology</td>\n",
       "      <td>camera</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>word processing</td>\n",
       "      <td>technology</td>\n",
       "      <td>word processing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>xml</td>\n",
       "      <td>technology</td>\n",
       "      <td>xml</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>376 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               keyword  theme_code         category_code\n",
       "2                blind       human  blind and low vision\n",
       "3    visual impairment       human  blind and low vision\n",
       "4            blindness       human  blind and low vision\n",
       "5    visually impaired       human  blind and low vision\n",
       "6         older adults       human          older adults\n",
       "..                 ...         ...                   ...\n",
       "487         wheelchair  technology            wheelchair\n",
       "488         white cane  technology            white cane\n",
       "489             camera  technology                camera\n",
       "490    word processing  technology       word processing\n",
       "491                xml  technology                   xml\n",
       "\n",
       "[376 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load mappings and filter for keywords in three groups\n",
    "kw_cats = {'human', 'method', 'technology'}\n",
    "\n",
    "CAT_NAME_DICT = {\n",
    "    \"human\": \"Community of Focus\",\n",
    "    \"technology\": \"Technology\",\n",
    "    \"method\": \"Method\"\n",
    "}\n",
    "\n",
    "df = pd.read_csv(KEYWORD_MAPPING_FILE)\n",
    "df = df[['keyword', 'theme_code', 'category_code']]\n",
    "df = df.loc[df['theme_code'].isin(kw_cats)]\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "836 papers processed.\n"
     ]
    }
   ],
   "source": [
    "# Process a11y paper data to enrich keywords\n",
    "\n",
    "# remove html tags from string\n",
    "def clean_str(s: str) -> str:\n",
    "    clean = re.compile('<.*?>')\n",
    "    return html.unescape(re.sub(clean, '', s))\n",
    "\n",
    "# flatten list of lists\n",
    "def flatten(l: List) -> List:\n",
    "    return [item for sublist in l for item in sublist]\n",
    "\n",
    "# reverse mapping dict\n",
    "def reverse_dict(d: Dict[str, Iterable]) -> Dict:\n",
    "    rev_dict = dict()\n",
    "    for k, vs in d.items():\n",
    "        for v in vs:\n",
    "            rev_dict[v] = k\n",
    "    return rev_dict\n",
    "\n",
    "# check if token sequence contains subsequence\n",
    "def has_tok_seq(main_seq, subseq):\n",
    "    for i, _ in enumerate(main_seq):\n",
    "        if main_seq[i:i+len(subseq)] == subseq:\n",
    "            return True\n",
    "    return False\n",
    "\n",
    "# get kws\n",
    "cat_mapping, cat_kws, cat_kw_toks, map_dict, reverse_map = dict(), dict(), dict(), dict(), dict()\n",
    "\n",
    "for cat_name in kw_cats:\n",
    "    cat_mapping[cat_name] = df[df.theme_code == cat_name]\n",
    "    cat_kws[cat_name] = cat_mapping[cat_name].keyword.dropna().unique()\n",
    "    cat_kw_toks[cat_name] = {kw: word_tokenize(kw) for kw in cat_kws[cat_name]}\n",
    "\n",
    "    # create kw mapping\n",
    "    map_dict[cat_name] = defaultdict(set)\n",
    "    for kw, agg in zip(cat_mapping[cat_name].keyword, cat_mapping[cat_name]['category_code']):\n",
    "        map_dict[cat_name][agg].add(kw)\n",
    "    reverse_map[cat_name] = reverse_dict(map_dict[cat_name])\n",
    "    \n",
    "# read a11y papers and group by human kw cluster\n",
    "a11y_papers = []\n",
    "with open(A11Y_PAPER_METADATA_FILE, 'r') as f:\n",
    "    reader = csv.DictReader(f)\n",
    "    for entry in reader:\n",
    "        entry['DOI'] = entry['\\ufeffDOI']\n",
    "        entry['year'] = int(entry['year'])\n",
    "        entry['title'] = clean_str(entry['title'])\n",
    "        entry['subtitle'] = clean_str(entry['subtitle'])\n",
    "        entry['fulltitle'] = f\"{entry['title']} : {entry['subtitle']}\"\n",
    "        entry['title_toks'] = word_tokenize(entry['fulltitle'].lower())\n",
    "        entry['abstract'] = clean_str(entry['abstract'])\n",
    "        entry['abstract_toks'] = word_tokenize(entry['abstract'].lower())\n",
    "        entry['kw_list'] = [kw.strip().lower() for kw in entry['keywords'].split(',')]\n",
    "        entry['keywords'] = ','.join(entry['kw_list'])\n",
    "        del entry['\\ufeffDOI']\n",
    "        for cat_name in kw_cats:\n",
    "            entry[f'{cat_name}_kws_in_title'] = [kw for kw, kw_seq in cat_kw_toks[cat_name].items() if has_tok_seq(entry['title_toks'], kw_seq)]\n",
    "            entry[f'{cat_name}_kws_in_abstract'] = [kw for kw, kw_seq in cat_kw_toks[cat_name].items() if has_tok_seq(entry['abstract_toks'], kw_seq)]\n",
    "            entry[f'{cat_name}_kws_in_title_abs'] = list(set(entry[f'{cat_name}_kws_in_title']) | set(entry[f'{cat_name}_kws_in_abstract']))\n",
    "            entry[f'{cat_name}_kw_cats'] = list(set([reverse_map[cat_name][kw] for kw in entry['kw_list'] if kw in reverse_map[cat_name]]))\n",
    "            entry[f'all_{cat_name}_cats'] = list(set(entry[f'{cat_name}_kw_cats']) | set(\n",
    "                [reverse_map[cat_name][kw] for kw in set(entry[f'{cat_name}_kws_in_title'] + entry[f'{cat_name}_kws_in_abstract']\n",
    "            ) if kw in reverse_map[cat_name]]))\n",
    "            if not(entry[f'{cat_name}_kw_cats']):\n",
    "                entry[f'{cat_name}_kw_cats'] = ['none']\n",
    "            if not(entry[f'all_{cat_name}_cats']):\n",
    "                entry[f'all_{cat_name}_cats'] = ['none']\n",
    "            entry[f'normalized_all_{cat_name}_cats'] = {\n",
    "                kw: 1. / (len(entry[f'all_{cat_name}_cats']))\n",
    "                for kw in entry[f'all_{cat_name}_cats']\n",
    "            }\n",
    "        a11y_papers.append(entry)\n",
    "        \n",
    "print(f'{len(a11y_papers)} papers processed.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "204 papers with additional Technology information.\n",
      "108 papers without Technology information.\n",
      "\n",
      "398 papers with additional Method information.\n",
      "285 papers without Method information.\n",
      "\n",
      "202 papers with additional Community of Focus information.\n",
      "125 papers without Community of Focus information.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# number of papers with enriched keywords and without keyword info per category\n",
    "kw_in_title_abstract = dict()\n",
    "kw_none_counts = dict()\n",
    "for cat_name in kw_cats:\n",
    "    kw_in_title_abstract[cat_name] = [\n",
    "        p for p in a11y_papers\n",
    "        if set(p[f'{cat_name}_kw_cats']) == {'none'}\n",
    "        and p[f'all_{cat_name}_cats']\n",
    "        and set(p[f'all_{cat_name}_cats']) != {'none'}\n",
    "    ]\n",
    "    print(f'{len(kw_in_title_abstract[cat_name])} papers with additional {CAT_NAME_DICT[cat_name]} information.')\n",
    "    kw_none_counts[cat_name] = [p for p in a11y_papers if set(p[f'all_{cat_name}_cats']) == {'none'}]\n",
    "    print(f'{len(kw_none_counts[cat_name])} papers without {CAT_NAME_DICT[cat_name]} information.')\n",
    "    print()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed keyword counts per aggregate category.\n"
     ]
    }
   ],
   "source": [
    "# get keyword counts by year by category\n",
    "year_range = range(1994, 2020)\n",
    "kw_counts_by_year = defaultdict(lambda: defaultdict(list))\n",
    "cat_counts_by_year = defaultdict(lambda: defaultdict(list))\n",
    "norm_cat_counts_by_year = defaultdict(lambda: defaultdict(list))\n",
    "for cat_name in kw_cats:\n",
    "    for kw, mapped_cat in reverse_map[cat_name].items():\n",
    "        year_matches = Counter([\n",
    "            entry['year'] for entry in a11y_papers\n",
    "            if kw in entry[f'{cat_name}_kws_in_title_abs']\n",
    "               or kw in entry['kw_list']\n",
    "        ])\n",
    "        counts_by_year = [year_matches.get(yr, 0) for yr in year_range]\n",
    "        kw_counts_by_year[cat_name][cat_name].append([kw, mapped_cat] + counts_by_year)\n",
    "    for kw_cat in map_dict[cat_name].keys():\n",
    "        year_matches = Counter([\n",
    "            entry['year'] for entry in a11y_papers\n",
    "            if kw_cat in entry[f'all_{cat_name}_cats']\n",
    "        ])\n",
    "        cat_counts_by_year[cat_name][kw_cat] = [year_matches.get(yr, 0) for yr in year_range]\n",
    "    for kw_cat in map_dict[cat_name].keys():\n",
    "        norm_entry = []\n",
    "        year_matches = [\n",
    "            (entry['year'], entry[f'normalized_all_{cat_name}_cats'][kw_cat])\n",
    "            for entry in a11y_papers\n",
    "            if kw_cat in entry[f'normalized_all_{cat_name}_cats']\n",
    "        ]\n",
    "        for yr in year_range:\n",
    "            that_yr_matches = [m[1] for m in year_matches if m[0] == yr]\n",
    "            norm_entry.append(sum(that_yr_matches) if that_yr_matches else 0.)\n",
    "        norm_cat_counts_by_year[cat_name][kw_cat] = norm_entry\n",
    "        \n",
    "print('Computed keyword counts per aggregate category.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>top_cat</th>\n",
       "      <th>mapped_cat</th>\n",
       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
       "      <th>1999</th>\n",
       "      <th>2000</th>\n",
       "      <th>2001</th>\n",
       "      <th>...</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>technology</td>\n",
       "      <td>web</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.159195</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.167949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070578</td>\n",
       "      <td>0.112745</td>\n",
       "      <td>0.101111</td>\n",
       "      <td>0.092437</td>\n",
       "      <td>0.039063</td>\n",
       "      <td>0.029167</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.016846</td>\n",
       "      <td>0.062147</td>\n",
       "      <td>0.044889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>technology</td>\n",
       "      <td>3d printing/diy/fab</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009804</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031250</td>\n",
       "      <td>0.047917</td>\n",
       "      <td>0.066111</td>\n",
       "      <td>0.031447</td>\n",
       "      <td>0.004944</td>\n",
       "      <td>0.042222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>technology</td>\n",
       "      <td>AR/MR/VR</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.021264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.025833</td>\n",
       "      <td>0.018868</td>\n",
       "      <td>0.055085</td>\n",
       "      <td>0.036000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>technology</td>\n",
       "      <td>mobile</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078571</td>\n",
       "      <td>0.090686</td>\n",
       "      <td>0.078095</td>\n",
       "      <td>0.123810</td>\n",
       "      <td>0.118527</td>\n",
       "      <td>0.056488</td>\n",
       "      <td>0.110754</td>\n",
       "      <td>0.078931</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.076212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>technology</td>\n",
       "      <td>screen readers</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.011494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043793</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>0.003268</td>\n",
       "      <td>0.039063</td>\n",
       "      <td>0.035417</td>\n",
       "      <td>0.024405</td>\n",
       "      <td>0.038544</td>\n",
       "      <td>0.030932</td>\n",
       "      <td>0.038593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>human</td>\n",
       "      <td>intellectual/developmental disability</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007143</td>\n",
       "      <td>0.016304</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.006410</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>0.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>human</td>\n",
       "      <td>general</td>\n",
       "      <td>0.115385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104902</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.021739</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.025641</td>\n",
       "      <td>0.069892</td>\n",
       "      <td>0.030488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>human</td>\n",
       "      <td>mental health</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.032609</td>\n",
       "      <td>0.008547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.024390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>human</td>\n",
       "      <td>other or unclear</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.011765</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056452</td>\n",
       "      <td>0.014286</td>\n",
       "      <td>0.016129</td>\n",
       "      <td>0.013158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023551</td>\n",
       "      <td>0.047009</td>\n",
       "      <td>0.016026</td>\n",
       "      <td>0.048387</td>\n",
       "      <td>0.012195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>human</td>\n",
       "      <td>other</td>\n",
       "      <td>0.038462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026316</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010753</td>\n",
       "      <td>0.019048</td>\n",
       "      <td>0.010753</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.042857</td>\n",
       "      <td>0.014493</td>\n",
       "      <td>0.021368</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>0.024390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        top_cat                             mapped_cat      1994  1995  \\\n",
       "0    technology                                    web  0.147059   0.0   \n",
       "1    technology                    3d printing/diy/fab  0.000000   0.0   \n",
       "2    technology                               AR/MR/VR  0.000000   0.0   \n",
       "3    technology                                 mobile  0.000000   0.0   \n",
       "4    technology                         screen readers  0.039216   0.0   \n",
       "..          ...                                    ...       ...   ...   \n",
       "150       human  intellectual/developmental disability  0.000000   0.0   \n",
       "151       human                                general  0.115385   0.0   \n",
       "152       human                          mental health  0.000000   0.0   \n",
       "153       human                       other or unclear  0.102564   0.0   \n",
       "154       human                                  other  0.038462   0.0   \n",
       "\n",
       "         1996  1997      1998  1999      2000  2001  ...      2010      2011  \\\n",
       "0    0.066667  0.25  0.159195   0.0  0.167949   NaN  ...  0.070578  0.112745   \n",
       "1    0.000000  0.00  0.000000   0.0  0.000000   NaN  ...  0.000000  0.009804   \n",
       "2    0.012500  0.00  0.021264   0.0  0.022436   NaN  ...  0.000000  0.000000   \n",
       "3    0.000000  0.00  0.000000   0.0  0.019231   NaN  ...  0.078571  0.090686   \n",
       "4    0.000000  0.00  0.011494   0.0  0.000000   NaN  ...  0.043793  0.000000   \n",
       "..        ...   ...       ...   ...       ...   ...  ...       ...       ...   \n",
       "150  0.000000   NaN  0.014493   0.0  0.000000   0.0  ...  0.016129  0.014286   \n",
       "151  0.104902   NaN  0.086957   0.0  0.157895   0.0  ...  0.032258  0.071429   \n",
       "152  0.000000   NaN  0.000000   0.0  0.000000   0.0  ...  0.000000  0.000000   \n",
       "153  0.011765   NaN  0.014493   0.0  0.026316   0.0  ...  0.056452  0.014286   \n",
       "154  0.058824   NaN  0.065217   0.0  0.026316   0.0  ...  0.010753  0.019048   \n",
       "\n",
       "         2012      2013      2014      2015      2016      2017      2018  \\\n",
       "0    0.101111  0.092437  0.039063  0.029167  0.031250  0.016846  0.062147   \n",
       "1    0.000000  0.000000  0.031250  0.047917  0.066111  0.031447  0.004944   \n",
       "2    0.016667  0.003268  0.003906  0.028571  0.025833  0.018868  0.055085   \n",
       "3    0.078095  0.123810  0.118527  0.056488  0.110754  0.078931  0.101695   \n",
       "4    0.044444  0.003268  0.039063  0.035417  0.024405  0.038544  0.030932   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "150  0.000000  0.000000  0.007143  0.016304  0.025641  0.006410  0.005376   \n",
       "151  0.016129  0.013158  0.028571  0.021739  0.038462  0.025641  0.069892   \n",
       "152  0.000000  0.052632  0.014286  0.032609  0.008547  0.000000  0.016129   \n",
       "153  0.016129  0.013158  0.000000  0.023551  0.047009  0.016026  0.048387   \n",
       "154  0.010753  0.052632  0.042857  0.014493  0.021368  0.019231  0.005376   \n",
       "\n",
       "         2019  \n",
       "0    0.044889  \n",
       "1    0.042222  \n",
       "2    0.036000  \n",
       "3    0.076212  \n",
       "4    0.038593  \n",
       "..        ...  \n",
       "150  0.024390  \n",
       "151  0.030488  \n",
       "152  0.024390  \n",
       "153  0.012195  \n",
       "154  0.024390  \n",
       "\n",
       "[155 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# proportion of normalized counts (used to generate Table 5)\n",
    "sum_by_cat = dict()\n",
    "for cat_name in kw_cats:\n",
    "    sum_by_cat[cat_name] = np.sum([\n",
    "        values for kw, values in norm_cat_counts_by_year[cat_name].items()\n",
    "    ], axis=0)\n",
    "    \n",
    "proportion_by_year = defaultdict(dict)\n",
    "for cat_name in norm_cat_counts_by_year:\n",
    "    for kw, counts in norm_cat_counts_by_year[cat_name].items():\n",
    "        prop = [ct / sm if sm != 0 else float('nan') for ct, sm in zip(counts, sum_by_cat[cat_name])]\n",
    "        proportion_by_year[cat_name][kw] = prop\n",
    "        \n",
    "headers = ['top_cat', 'mapped_cat'] + list(year_range)\n",
    "proportion_as_table = []\n",
    "for cat_name in proportion_by_year:\n",
    "    for kw in proportion_by_year[cat_name]:\n",
    "        proportion_as_table.append([cat_name, kw] + proportion_by_year[cat_name][kw])\n",
    "        \n",
    "df_prop = pd.DataFrame(proportion_as_table, columns=headers)\n",
    "\n",
    "display(df_prop)\n"
   ]
  }
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