examples/examples_labelled.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 1: Trump v Clinton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open debate_transcript.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open('trump_hillary_debate.txt') as file:\n",
    "    text = file.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parse and filter the transcript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transcript = text.split('\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "filtered_transcript = []\n",
    "for para in transcript:\n",
    "    if para.startswith('Trump:'):\n",
    "        filtered_transcript.append((0,para[7:]))\n",
    "    elif para.startswith('Clinton:'):\n",
    "        filtered_transcript.append((1,para[9:]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys, os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "package_path = os.path.dirname(os.getcwd())\n",
    "sys.path.append(package_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading spaCy's language model...\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "from wordmesh import Wordmesh, LabelledWordmesh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a LabelledWordmesh object, and extract the **40 most frequently used *adjectives*** for each label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wm = LabelledWordmesh(filtered_transcript, dimensions=(500,900), num_keywords=40, extract_ngrams=False, \n",
    "                      keyword_extractor='tf', pos_filter=['ADJ'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cluster the extracted keywords on the canvas based on their **meaning**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wm.set_clustering_criteria(by='meaning')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Color the keywords based on their label and scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The following function maps the label and score of the keywords to their color\n",
    "def colormap(label, score):\n",
    "    if label==0:\n",
    "        R,G,B = (150+100*score/22, 50+90*score/22, 50+90*score/22)\n",
    "    elif label==1:\n",
    "        R,G,B = (50+90*score/22, 50+90*score/22, 150+100*score/22)\n",
    "    return (R,G,B)\n",
    "\n",
    "#applying the mapping the wordmesh\n",
    "wm.set_fontcolor(custom_colors = list(map(colormap, wm.labels, wm.scores)))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 10,
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