NLP with SpaCy- Adding Extensions Attributes in SpaCy(How to use sentiment analysis in SpaCy).ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  NLP With SpaCy -Extending SpaCy\n",
    "+ Doc Document \n",
    "+ Tokens\n",
    "+ Span\n",
    "\n",
    "#### Usefulness\n",
    "+ Allows you to add extra functionality to SpaCy\n",
    "    + eg sentiment analysis\n",
    "+ extend the API to become more accessible \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doc\n",
    "![](spacy_pipeline.svg)\n",
    "\n",
    "#### Creating an Extension\n",
    "+ set_extension\n",
    "+ 3 Types \n",
    " - Attribute Extension\n",
    " - Property Extension (getter,setter)\n",
    " - Method Extension(method)\n",
    " \n",
    "#### Calling the extension\n",
    "  ._\n",
    "  (Doc._.myextension)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp1 = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('tagger', <spacy.pipeline.Tagger at 0x1c8b5f62ac8>),\n",
       " ('parser', <spacy.pipeline.DependencyParser at 0x1c8b6031728>),\n",
       " ('ner', <spacy.pipeline.EntityRecognizer at 0x1c8b6031780>)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp1.pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Adding Extension\n",
    "from spacy.tokens import Doc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install nltk\n",
    "nltk.download()\n",
    "pip install twython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sentiment_scores(docx):\n",
    "    return sent_analyzer.polarity_scores(docx.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Doc.set_extension(\"sentimenter\",getter=sentiment_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex1 = nlp(\"This movie was very nice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'neg': 0.0, 'neu': 0.564, 'pos': 0.436, 'compound': 0.4754}\n"
     ]
    }
   ],
   "source": [
    "# Calling\n",
    "print(ex1._.sentimenter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex2 = nlp(\"I don't like onions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'neg': 0.513, 'neu': 0.487, 'pos': 0.0, 'compound': -0.2755}\n"
     ]
    }
   ],
   "source": [
    "print(ex2._.sentimenter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# By Jesse JCharis\n",
    "# J-Secur1ty\n",
    "# Jesus Saves @JCharisTech\n"
   ]
  }
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