03_find_ads.ipynb

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     "name": "stdout",
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     "text": [
      "Python Version: 3.7.4 (default, Aug 13 2019, 20:35:49) \n",
      "[GCC 7.3.0]\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "print(\"Python Version:\", sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#######################\n",
    "# standard code block #\n",
    "#######################\n",
    "\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "# auto reload imports that change\n",
    "%load_ext autoreload\n",
    "# only set to auto reload for marked imports\n",
    "%autoreload 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pull Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymongo import MongoClient\n",
    "%aimport credentials.cred\n",
    "from credentials import cred\n",
    "\n",
    "\n",
    "config = {\n",
    "  'host': cred.mongo_host,\n",
    "  'username': cred.mongo_user,\n",
    "  'password': cred.mongo_pass,\n",
    "  'authSource': cred.mongo_auth_db\n",
    "}\n",
    "\n",
    "# get a mongo client\n",
    "client = MongoClient(**config)\n",
    "\n",
    "# use the clean database\n",
    "jre_clean = client.jre_clean\n",
    "podcasts_clean = jre_clean.podcasts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Podcasts from Podscribe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query\n",
    "ps_ep_mongo = list(\n",
    "    podcasts_clean\n",
    "    .find({'source':'podscribe'},{'_id':0, 'number':1, 'name':1, 'desc':1, 'text':1})\n",
    "    .limit(200)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pull out text and names\n",
    "ps_ep_text_list = [ep['text'] for ep in ps_ep_mongo]\n",
    "ps_ep_list = [' '.join([x['text'] for x in ep]) for ep in ps_ep_text_list]\n",
    "ps_ep_names = [f\"#{x['number']} \" + x['name'] for x in ps_ep_mongo]\n",
    "ps_ep_descs = [x['desc'] for x in ps_ep_mongo]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chunked Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ps_ep_textblob = []\n",
    "\n",
    "for ep in ps_ep_list:\n",
    "    ps_ep_textblob.append(TextBlob(ep))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ps_ep_sentiment = []\n",
    "\n",
    "for ep in ps_ep_textblob:\n",
    "    ep_blob = []\n",
    "    ep_blob.append(TextBlob(' '.join(ep.words[:1000])))\n",
    "    ep_blob.append(TextBlob(' '.join(ep.words[1000:-1000])))\n",
    "    ep_blob.append(TextBlob(' '.join(ep.words[-1000:])))\n",
    "    ps_ep_sentiment.append(deepcopy(ep_blob))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sentiment_list = []\n",
    "\n",
    "for ad1, body, ad2 in ps_ep_sentiment:\n",
    "    sentiment = []\n",
    "    sentiment.append(ad1.sentiment)\n",
    "    sentiment.append(body.sentiment)\n",
    "    sentiment.append(ad2.sentiment)\n",
    "    sentiment_list.append(deepcopy(sentiment))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep_sentiment_list = []\n",
    "\n",
    "# episode sentiment [[episode 0 [timestamp0, polarity0, subjectivity0]]]\n",
    "for ep in ps_ep_text_list:\n",
    "    ep_sent = []\n",
    "    \n",
    "    for txt in ep:\n",
    "        tblob = TextBlob(txt['text'])\n",
    "        ep_sent.append([txt['time'], tblob.sentiment[0], tblob.sentiment[1]])\n",
    "    ep_sentiment_list.append(deepcopy(ep_sent))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = [x for x in range(0,len(sentiment_list))]\n",
    "\n",
    "y1_pol = [x[0].polarity for x in sentiment_list]\n",
    "y1_sub = [x[0].subjectivity for x in sentiment_list]\n",
    "\n",
    "y2_pol = [x[1].polarity for x in sentiment_list]\n",
    "y2_sub = [x[1].subjectivity for x in sentiment_list]\n",
    "\n",
    "y3_pol = [x[2].polarity for x in sentiment_list]\n",
    "y3_sub = [x[2].subjectivity for x in sentiment_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "av_pol = (np.array(y1_pol) + np.array(y2_pol) + np.array(y3_pol))/3"
   ]
  },
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   "cell_type": "code",
   "execution_count": 43,
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      "/home/eric/anaconda3/envs/metis/lib/python3.7/site-packages/ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in true_divide\n",
      "  after removing the cwd from sys.path.\n",
      "/home/eric/anaconda3/envs/metis/lib/python3.7/site-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \"\"\"\n",
      "/home/eric/anaconda3/envs/metis/lib/python3.7/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in true_divide\n",
      "  \n"
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      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
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   ],
   "source": [
    "plt.suptitle('')\n",
    "plt.figure(figsize=[10,10])\n",
    "\n",
    "plt.plot(x,y1_pol/av_pol, label='ad1')\n",
    "plt.plot(x,y2_pol/av_pol, label='body')\n",
    "plt.plot(x,y3_pol/av_pol, label='ad2')\n",
    "plt.legend()\n",
    "plt.savefig('./reports/figures/ad_vs_body_sentiment.svg',bbox_inches='tight')"
   ]
  }
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