notebooks/lda_chapters.ipynb

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      "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates InRelease [88.7 kB]33m\n",
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      "Get:17 http://archive.ubuntu.com/ubuntu bionic-backports/universe amd64 Packages [4,244 B]33m\n",
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      "Fetched 17.4 MB in 19s (906 kB/s)                                              \u001b[0m\u001b[33m\u001b[33m\n",
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
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      "Collecting nltk\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f6/1d/d925cfb4f324ede997f6d47bea4d9babba51b49e87a767c170b77005889d/nltk-3.4.5.zip (1.5MB)\n",
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      "\u001b[?25hRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from nltk) (1.12.0)\n",
      "Building wheels for collected packages: nltk\n",
      "  Building wheel for nltk (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for nltk: filename=nltk-3.4.5-cp37-none-any.whl size=1449906 sha256=8e0e261ba3de912f88cce75cbd8052d7b5bac8c5d9ed4cbb045d137ef4b8ec3a\n",
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      "Successfully installed nltk-3.4.5\n",
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      "\u001b[?25hRequirement already satisfied: scipy>=0.18.1 in /opt/conda/lib/python3.7/site-packages (from gensim) (1.3.1)\n",
      "Collecting smart-open>=1.8.1 (from gensim)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0c/09/735f2786dfac9bbf39d244ce75c0313d27d4962e71e0774750dc809f2395/smart_open-1.9.0.tar.gz (70kB)\n",
      "\u001b[K     |████████████████████████████████| 71kB 1.2MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.7/site-packages (from gensim) (1.17.2)\n",
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      "Collecting boto>=2.32 (from smart-open>=1.8.1->gensim)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/23/10/c0b78c27298029e4454a472a1919bde20cb182dab1662cec7f2ca1dcc523/boto-2.49.0-py2.py3-none-any.whl (1.4MB)\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f9/01/1c749dc1bca8dda969f5fe0ba16fa6d24c6bd96572d118f790773c54a636/boto3-1.10.45-py2.py3-none-any.whl (128kB)\n",
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      "\u001b[?25hRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->smart-open>=1.8.1->gensim) (2.8)\n",
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      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->smart-open>=1.8.1->gensim) (1.25.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->smart-open>=1.8.1->gensim) (2019.6.16)\n",
      "Collecting botocore<1.14.0,>=1.13.45 (from boto3->smart-open>=1.8.1->gensim)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/96/22/9f8201d900956e57a9811e1b1c91c9f76c87487c76f636c2df1ce8379c38/botocore-1.13.45-py2.py3-none-any.whl (5.9MB)\n",
      "\u001b[K     |████████████████████████████████| 5.9MB 1.2MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting s3transfer<0.3.0,>=0.2.0 (from boto3->smart-open>=1.8.1->gensim)\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/16/8a/1fc3dba0c4923c2a76e1ff0d52b305c44606da63f718d14d3231e21c51b0/s3transfer-0.2.1-py2.py3-none-any.whl (70kB)\n",
      "\u001b[K     |████████████████████████████████| 71kB 1.3MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1 (from boto3->smart-open>=1.8.1->gensim)\n",
      "  Downloading https://files.pythonhosted.org/packages/83/94/7179c3832a6d45b266ddb2aac329e101367fbdb11f425f13771d27f225bb/jmespath-0.9.4-py2.py3-none-any.whl\n",
      "Requirement already satisfied: python-dateutil<3.0.0,>=2.1; python_version >= \"2.7\" in /opt/conda/lib/python3.7/site-packages (from botocore<1.14.0,>=1.13.45->boto3->smart-open>=1.8.1->gensim) (2.8.0)\n",
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      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/22/cd/a6aa959dca619918ccb55023b4cb151949c64d4d5d55b3f4ffd7eee0c6e8/docutils-0.15.2-py3-none-any.whl (547kB)\n",
      "\u001b[K     |████████████████████████████████| 552kB 637kB/s eta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: smart-open\n",
      "  Building wheel for smart-open (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for smart-open: filename=smart_open-1.9.0-cp37-none-any.whl size=73088 sha256=3cd52af2cbd23b8196ac97ccbb0569d5f723b33937b8ca16467194e60dd8e29c\n",
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      "Installing collected packages: boto, docutils, jmespath, botocore, s3transfer, boto3, smart-open, gensim\n",
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      "Reading package lists... Done\n",
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      "  libxxf86dga1 openjdk-11-jre openjdk-11-jre-headless x11-utils\n",
      "Suggested packages:\n",
      "  pcscd libnss-mdns fonts-ipafont-gothic fonts-ipafont-mincho\n",
      "  fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic mesa-utils\n",
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      "  libxmuu1 libxtst6 libxxf86dga1 openjdk-11-jre openjdk-11-jre-headless\n",
      "  x11-utils\n",
      "0 upgraded, 14 newly installed, 0 to remove and 65 not upgraded.\n",
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      "Get:9 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 openjdk-11-jre amd64 11.0.5+10-0ubuntu1.1~18.04 [34.4 kB]\n",
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      "Get:11 http://archive.ubuntu.com/ubuntu bionic/main amd64 libfontenc1 amd64 1:1.1.3-1 [13.9 kB]\n",
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      "update-alternatives: using /usr/lib/jvm/java-11-openjdk-amd64/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode\n",
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      "Setting up ca-certificates-java (20180516ubuntu1~18.04.1) ...\n",
      "head: cannot open '/etc/ssl/certs/java/cacerts' for reading: No such file or directory\n",
      "Adding debian:QuoVadis_Root_CA_3_G3.pem\n",
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      "Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem\n",
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      "Adding debian:VeriSign_Class_3_Public_Primary_Certification_Authority_-_G5.pem\n",
      "Adding debian:GeoTrust_Primary_Certification_Authority_-_G2.pem\n",
      "Adding debian:CFCA_EV_ROOT.pem\n",
      "Adding debian:EE_Certification_Centre_Root_CA.pem\n",
      "Adding debian:VeriSign_Class_3_Public_Primary_Certification_Authority_-_G4.pem\n",
      "Adding debian:TÜRKTRUST_Elektronik_Sertifika_Hizmet_Sağlayıcısı_H5.pem\n",
      "Adding debian:OISTE_WISeKey_Global_Root_GA_CA.pem\n",
      "Adding debian:Staat_der_Nederlanden_EV_Root_CA.pem\n",
      "Adding debian:Staat_der_Nederlanden_Root_CA_-_G2.pem\n",
      "Adding debian:Go_Daddy_Class_2_CA.pem\n",
      "Adding debian:Certplus_Class_2_Primary_CA.pem\n",
      "Adding debian:Cybertrust_Global_Root.pem\n",
      "Adding debian:Actalis_Authentication_Root_CA.pem\n",
      "Adding debian:Taiwan_GRCA.pem\n",
      "Adding debian:QuoVadis_Root_CA_3.pem\n",
      "Adding debian:Buypass_Class_3_Root_CA.pem\n",
      "Adding debian:SSL.com_Root_Certification_Authority_RSA.pem\n",
      "Adding debian:DigiCert_Trusted_Root_G4.pem\n",
      "Adding debian:OpenTrust_Root_CA_G1.pem\n",
      "Adding debian:Buypass_Class_2_Root_CA.pem\n",
      "Adding debian:DigiCert_Global_Root_G3.pem\n",
      "Adding debian:Starfield_Class_2_CA.pem\n",
      "Adding debian:Chambers_of_Commerce_Root_-_2008.pem\n",
      "Adding debian:COMODO_Certification_Authority.pem\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding debian:OpenTrust_Root_CA_G2.pem\n",
      "Adding debian:GeoTrust_Universal_CA_2.pem\n",
      "Adding debian:Visa_eCommerce_Root.pem\n",
      "Adding debian:ePKI_Root_Certification_Authority.pem\n",
      "Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem\n",
      "Adding debian:TeliaSonera_Root_CA_v1.pem\n",
      "Adding debian:USERTrust_RSA_Certification_Authority.pem\n",
      "Adding debian:TrustCor_RootCert_CA-2.pem\n",
      "Adding debian:GeoTrust_Universal_CA.pem\n",
      "Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem\n",
      "Adding debian:DigiCert_Assured_ID_Root_CA.pem\n",
      "Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem\n",
      "Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem\n",
      "Adding debian:TrustCor_ECA-1.pem\n",
      "Adding debian:TrustCor_RootCert_CA-1.pem\n",
      "Adding debian:Entrust_Root_Certification_Authority_-_G2.pem\n",
      "Adding debian:Security_Communication_RootCA2.pem\n",
      "Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem\n",
      "Adding debian:TWCA_Root_Certification_Authority.pem\n",
      "Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem\n",
      "Adding debian:Verisign_Class_3_Public_Primary_Certification_Authority_-_G3.pem\n",
      "Adding debian:thawte_Primary_Root_CA_-_G3.pem\n",
      "Adding debian:Amazon_Root_CA_3.pem\n",
      "Adding debian:Entrust_Root_Certification_Authority.pem\n",
      "Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem\n",
      "Adding debian:Certum_Trusted_Network_CA_2.pem\n",
      "Adding debian:Staat_der_Nederlanden_Root_CA_-_G3.pem\n",
      "Adding debian:EC-ACC.pem\n",
      "Adding debian:AddTrust_External_Root.pem\n",
      "Adding debian:GeoTrust_Global_CA.pem\n",
      "Adding debian:Certplus_Root_CA_G2.pem\n",
      "Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem\n",
      "Adding debian:Network_Solutions_Certificate_Authority.pem\n",
      "Adding debian:thawte_Primary_Root_CA_-_G2.pem\n",
      "Adding debian:DigiCert_Global_Root_CA.pem\n",
      "Adding debian:GeoTrust_Primary_Certification_Authority_-_G3.pem\n",
      "Adding debian:GeoTrust_Primary_Certification_Authority.pem\n",
      "Adding debian:ACCVRAIZ1.pem\n",
      "Adding debian:thawte_Primary_Root_CA.pem\n",
      "Adding debian:CA_Disig_Root_R2.pem\n",
      "Adding debian:DigiCert_Assured_ID_Root_G2.pem\n",
      "Adding debian:NetLock_Arany_=Class_Gold=_Főtanúsítvány.pem\n",
      "Adding debian:Sonera_Class_2_Root_CA.pem\n",
      "Adding debian:VeriSign_Universal_Root_Certification_Authority.pem\n",
      "Adding debian:COMODO_ECC_Certification_Authority.pem\n",
      "Adding debian:LuxTrust_Global_Root_2.pem\n",
      "Adding debian:Amazon_Root_CA_2.pem\n",
      "Adding debian:IdenTrust_Commercial_Root_CA_1.pem\n",
      "Adding debian:Certplus_Root_CA_G1.pem\n",
      "Adding debian:Baltimore_CyberTrust_Root.pem\n",
      "Adding debian:DST_Root_CA_X3.pem\n",
      "Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem\n",
      "Adding debian:Izenpe.com.pem\n",
      "Adding debian:SSL.com_EV_Root_Certification_Authority_RSA_R2.pem\n",
      "Adding debian:certSIGN_ROOT_CA.pem\n",
      "Adding debian:AffirmTrust_Networking.pem\n",
      "Adding debian:Certinomis_-_Root_CA.pem\n",
      "Adding debian:Security_Communication_Root_CA.pem\n",
      "Adding debian:SSL.com_Root_Certification_Authority_ECC.pem\n",
      "Adding debian:XRamp_Global_CA_Root.pem\n",
      "Adding debian:E-Tugra_Certification_Authority.pem\n",
      "Adding debian:QuoVadis_Root_CA_2_G3.pem\n",
      "Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem\n",
      "Adding debian:Global_Chambersign_Root_-_2008.pem\n",
      "Adding debian:QuoVadis_Root_CA_2.pem\n",
      "Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2011.pem\n",
      "Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem\n",
      "Adding debian:Secure_Global_CA.pem\n",
      "Adding debian:OpenTrust_Root_CA_G3.pem\n",
      "Adding debian:T-TeleSec_GlobalRoot_Class_3.pem\n",
      "Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem\n",
      "Adding debian:Amazon_Root_CA_1.pem\n",
      "Adding debian:GDCA_TrustAUTH_R5_ROOT.pem\n",
      "Adding debian:USERTrust_ECC_Certification_Authority.pem\n",
      "Adding debian:TWCA_Global_Root_CA.pem\n",
      "Adding debian:GlobalSign_Root_CA.pem\n",
      "Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem\n",
      "Adding debian:Hongkong_Post_Root_CA_1.pem\n",
      "Adding debian:Certum_Trusted_Network_CA.pem\n",
      "Adding debian:Comodo_AAA_Services_root.pem\n",
      "Adding debian:AffirmTrust_Premium.pem\n",
      "done.\n",
      "Setting up default-jre (2:1.11-68ubuntu1~18.04.1) ...\n",
      "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
      "Processing triggers for ca-certificates (20180409) ...\n",
      "Updating certificates in /etc/ssl/certs...\n",
      "0 added, 0 removed; done.\n",
      "Running hooks in /etc/ca-certificates/update.d...\n",
      "\n",
      "done.\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "!sudo apt update -y\n",
    "!pip install nltk\n",
    "!pip install gensim\n",
    "!sudo apt-get install default-jre -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/jovyan/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "from os import path\n",
    "import re\n",
    "import nltk\n",
    "import gensim\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from os import path\n",
    "with open(path.expanduser('~/work/data/talk_meta.pkl'), \"rb\") as input_file:\n",
    "    talk_meta = pickle.load(input_file)\n",
    "    \n",
    "with open(path.expanduser('~/work/data/talk_sub.pkl'), \"rb\") as input_file:\n",
    "    talk_sub = pickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Thank you so much, Chris. to come to this stage twice;\\nI'm extremely grateful. I have been blown away by this conference, and I want to thank all of you\\nfor the many nice comments about what I had to say the other night. And I say that sincerely, partly because (Mock sob) I need that.\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talk_sub[(talk_sub.talk_id == 1) & (talk_sub.id==1)]['transcript'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "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>id</th>\n",
       "      <th>transcript</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>talk_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1415.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1619.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1792.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1793.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1794.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2010.0</td>\n",
       "      <td>216</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>254.0</td>\n",
       "      <td>221</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2209.0</td>\n",
       "      <td>231</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500.0</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>823.0</td>\n",
       "      <td>275</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2560 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  transcript\n",
       "talk_id                 \n",
       "1415.0     1           1\n",
       "1619.0     1           1\n",
       "1792.0     1           1\n",
       "1793.0     1           1\n",
       "1794.0     1           1\n",
       "...      ...         ...\n",
       "2010.0   216         216\n",
       "254.0    221         221\n",
       "2209.0   231         231\n",
       "500.0    256         256\n",
       "823.0    275         275\n",
       "\n",
       "[2560 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talk_sub.groupby('talk_id').count().sort_values('transcript')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "lem = nltk.stem.WordNetLemmatizer()\n",
    "def prepare_text(text):\n",
    "    text = re.sub(r'\\((.*?)\\)', ' ', text)\n",
    "    text = re.sub(r'\\d+', '', text)\n",
    "    text = nltk.tokenize.RegexpTokenizer(r'\\w+').tokenize(text.lower())\n",
    "    text = [w for w in text if w not in nltk.corpus.stopwords.words('english')]\n",
    "    text = [lem.lemmatize(w) for w in text]\n",
    "    text = [w for w in text if len(w)>=3]\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "extra_keywords = []\n",
    "talk_pre = talk_sub.copy()\n",
    "talk_pre['transcript'] = talk_pre['transcript'].apply(lambda x: prepare_text(x))\n",
    "talk_pre['transcript'] = talk_pre['transcript'].apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_words = talk_pre['transcript'].apply(lambda x: x.split()).values\n",
    "\n",
    "id2word = gensim.corpora.Dictionary(data_words)\n",
    "id2word.filter_n_most_frequent(20)\n",
    "\n",
    "\n",
    "corpus = [id2word.doc2bow(text) for text in data_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'away': 0,\n",
       " 'blown': 1,\n",
       " 'chris': 2,\n",
       " 'come': 3,\n",
       " 'comment': 4,\n",
       " 'conference': 5,\n",
       " 'extremely': 6,\n",
       " 'grateful': 7,\n",
       " 'many': 8,\n",
       " 'much': 9,\n",
       " 'need': 10,\n",
       " 'nice': 11,\n",
       " 'night': 12,\n",
       " 'partly': 13,\n",
       " 'say': 14,\n",
       " 'sincerely': 15,\n",
       " 'stage': 16,\n",
       " 'thank': 17,\n",
       " 'twice': 18,\n",
       " 'position': 19,\n",
       " 'put': 20,\n",
       " 'air': 21,\n",
       " 'eight': 22,\n",
       " 'flew': 23,\n",
       " 'force': 24,\n",
       " 'two': 25,\n",
       " 'airplane': 26,\n",
       " 'boot': 27,\n",
       " 'shoe': 28,\n",
       " 'take': 29,\n",
       " 'illustrate': 30,\n",
       " 'quick': 31,\n",
       " 'story': 32,\n",
       " 'tell': 33,\n",
       " 'bit': 34,\n",
       " 'every': 35,\n",
       " 'true': 36,\n",
       " 'house': 37,\n",
       " 'left': 38,\n",
       " 'soon': 39,\n",
       " 'tipper': 40,\n",
       " 'white': 41,\n",
       " 'driving': 42,\n",
       " 'east': 43,\n",
       " 'farm': 44,\n",
       " 'home': 45,\n",
       " 'little': 46,\n",
       " 'mile': 47,\n",
       " 'nashville': 48,\n",
       " 'sound': 49,\n",
       " 'back': 50,\n",
       " 'hit': 51,\n",
       " 'looked': 52,\n",
       " 'mirror': 53,\n",
       " 'motorcade': 54,\n",
       " 'rear': 55,\n",
       " 'sudden': 56,\n",
       " 'view': 57,\n",
       " 'heard': 58,\n",
       " 'limb': 59,\n",
       " 'pain': 60,\n",
       " 'phantom': 61,\n",
       " 'ford': 62,\n",
       " 'rented': 63,\n",
       " 'taurus': 64,\n",
       " 'big': 65,\n",
       " 'booth': 66,\n",
       " 'came': 67,\n",
       " 'chain': 68,\n",
       " 'commotion': 69,\n",
       " 'cost': 70,\n",
       " 'dinnertime': 71,\n",
       " 'eat': 72,\n",
       " 'exit': 73,\n",
       " 'family': 74,\n",
       " 'found': 75,\n",
       " 'got': 76,\n",
       " 'lebanon': 77,\n",
       " 'looking': 78,\n",
       " 'low': 79,\n",
       " 'made': 80,\n",
       " 'place': 81,\n",
       " 'restaurant': 82,\n",
       " 'sat': 83,\n",
       " 'shoney': 84,\n",
       " 'started': 85,\n",
       " 'tennessee': 86,\n",
       " 'waitress': 87,\n",
       " 'went': 88,\n",
       " 'couple': 89,\n",
       " 'former': 90,\n",
       " 'gore': 91,\n",
       " 'hear': 92,\n",
       " 'long': 93,\n",
       " 'lowered': 94,\n",
       " 'man': 95,\n",
       " 'next': 96,\n",
       " 'order': 97,\n",
       " 'president': 98,\n",
       " 'said': 99,\n",
       " 'saying': 100,\n",
       " 'strain': 101,\n",
       " 'took': 102,\n",
       " 'vice': 103,\n",
       " 'voice': 104,\n",
       " 'wife': 105,\n",
       " 'yes': 106,\n",
       " 'epiphany': 107,\n",
       " 'kind': 108,\n",
       " 'series': 109,\n",
       " 'across': 110,\n",
       " 'africa': 111,\n",
       " 'airport': 112,\n",
       " 'asleep': 113,\n",
       " 'atlantic': 114,\n",
       " 'azores': 115,\n",
       " 'began': 116,\n",
       " 'bunch': 117,\n",
       " 'call': 118,\n",
       " 'city': 119,\n",
       " 'continuing': 120,\n",
       " 'could': 121,\n",
       " 'day': 122,\n",
       " 'door': 123,\n",
       " 'energy': 124,\n",
       " 'fell': 125,\n",
       " 'fly': 126,\n",
       " 'fresh': 127,\n",
       " 'gave': 128,\n",
       " 'happened': 129,\n",
       " 'island': 130,\n",
       " 'lagos': 131,\n",
       " 'landed': 132,\n",
       " 'laughed': 133,\n",
       " 'middle': 134,\n",
       " 'nigeria': 135,\n",
       " 'opened': 136,\n",
       " 'paper': 137,\n",
       " 'piece': 138,\n",
       " 'plane': 139,\n",
       " 'pretty': 140,\n",
       " 'refueling': 141,\n",
       " 'remembered': 142,\n",
       " 'running': 143,\n",
       " 'runway': 144,\n",
       " 'shared': 145,\n",
       " 'speech': 146,\n",
       " 'telling': 147,\n",
       " 'thought': 148,\n",
       " 'told': 149,\n",
       " 'topic': 150,\n",
       " 'totally': 151,\n",
       " 'washington': 152,\n",
       " 'waving': 153,\n",
       " 'woke': 154,\n",
       " 'wrong': 155,\n",
       " 'yelling': 156,\n",
       " 'already': 157,\n",
       " 'america': 158,\n",
       " 'checked': 159,\n",
       " 'monterey': 160,\n",
       " 'printed': 161,\n",
       " 'service': 162,\n",
       " 'staff': 163,\n",
       " 'state': 164,\n",
       " 'turned': 165,\n",
       " 'united': 166,\n",
       " 'upset': 167,\n",
       " 'wire': 168,\n",
       " 'written': 169,\n",
       " 'announced': 170,\n",
       " 'quote': 171,\n",
       " 'yesterday': 172,\n",
       " 'named': 173,\n",
       " 'burger': 174,\n",
       " 'chef': 175,\n",
       " 'david': 176,\n",
       " 'fry': 177,\n",
       " 'hat': 178,\n",
       " 'jay': 179,\n",
       " 'leno': 180,\n",
       " 'letterman': 181,\n",
       " 'soil': 182,\n",
       " 'bill': 183,\n",
       " 'clinton': 184,\n",
       " 'colleague': 185,\n",
       " 'congratulation': 186,\n",
       " 'friend': 187,\n",
       " 'handwritten': 188,\n",
       " 'later': 189,\n",
       " 'letter': 190,\n",
       " 'new': 191,\n",
       " 'partner': 192,\n",
       " 'three': 193,\n",
       " 'celebrate': 194,\n",
       " 'life': 195,\n",
       " 'success': 196,\n",
       " 'another': 197,\n",
       " 'coming': 198,\n",
       " 'ecology': 199,\n",
       " 'habit': 200,\n",
       " 'information': 201,\n",
       " 'lifelong': 202,\n",
       " 'maybe': 203,\n",
       " 'plan': 204,\n",
       " 'since': 205,\n",
       " 'talk': 206,\n",
       " 'ted': 207,\n",
       " 'thinking': 208,\n",
       " 'anderson': 209,\n",
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       " 'person': 894,\n",
       " 'room': 895,\n",
       " 'training': 896,\n",
       " 'zambia': 897,\n",
       " 'basket': 898,\n",
       " 'creating': 899,\n",
       " 'enterprise': 900,\n",
       " 'etc': 901,\n",
       " 'finance': 902,\n",
       " 'line': 903,\n",
       " 'micro': 904,\n",
       " 'move': 905,\n",
       " 'poultry': 906,\n",
       " 'poverty': 907,\n",
       " 'promote': 908,\n",
       " 'rearing': 909,\n",
       " 'traditional': 910,\n",
       " 'best': 911,\n",
       " 'crop': 912,\n",
       " 'development': 913,\n",
       " 'educational': 914,\n",
       " 'effectively': 915,\n",
       " 'outside': 916,\n",
       " 'poor': 917,\n",
       " 'promoting': 918,\n",
       " 'rather': 919,\n",
       " 'rethink': 920,\n",
       " 'strategy': 921,\n",
       " 'incredible': 922,\n",
       " 'question': 923,\n",
       " 'someone': 924,\n",
       " 'ability': 925,\n",
       " 'amy': 926,\n",
       " 'disinfection': 927,\n",
       " 'improving': 928,\n",
       " 'maintain': 929,\n",
       " 'quality': 930,\n",
       " 'smith': 931,\n",
       " 'testing': 932,\n",
       " 'treat': 933,\n",
       " 'treatment': 934,\n",
       " 'bottleneck': 935,\n",
       " 'capitalist': 936,\n",
       " 'preventing': 937,\n",
       " 'stuff': 938,\n",
       " 'venture': 939,\n",
       " 'difficult': 940,\n",
       " 'fragmented': 941,\n",
       " 'large': 942,\n",
       " 'model': 943,\n",
       " 'expect': 944,\n",
       " 'frame': 945,\n",
       " 'implement': 946,\n",
       " 'assumption': 947,\n",
       " 'avoid': 948,\n",
       " 'critical': 949,\n",
       " 'debate': 950,\n",
       " 'dewey': 951,\n",
       " 'discussion': 952,\n",
       " 'doxa': 953,\n",
       " 'join': 954,\n",
       " 'mobilize': 955,\n",
       " 'must': 956,\n",
       " 'namely': 957,\n",
       " 'observed': 958,\n",
       " 'public': 959,\n",
       " 'realm': 960,\n",
       " 'spirit': 961,\n",
       " 'subject': 962,\n",
       " 'tyranny': 963,\n",
       " 'unquestioned': 964,\n",
       " 'willing': 965,\n",
       " 'acceptable': 966,\n",
       " 'acknowledge': 967,\n",
       " 'act': 968,\n",
       " 'action': 969,\n",
       " 'capitalism': 970,\n",
       " 'clearly': 971,\n",
       " 'consensus': 972,\n",
       " 'democratic': 973,\n",
       " 'economy': 974,\n",
       " 'exception': 975,\n",
       " 'fragile': 976,\n",
       " 'ground': 977,\n",
       " 'map': 978,\n",
       " 'matter': 979,\n",
       " 'moment': 980,\n",
       " 'norm': 981,\n",
       " 'polity': 982,\n",
       " 'provides': 983,\n",
       " 'provisional': 984,\n",
       " 'type': 985,\n",
       " 'aspect': 986,\n",
       " 'assistance': 987,\n",
       " 'blood': 988,\n",
       " 'capacity': 989,\n",
       " 'concerned': 990,\n",
       " 'creatively': 991,\n",
       " 'denial': 992,\n",
       " 'developing': 993,\n",
       " 'diamond': 994,\n",
       " 'emerald': 995,\n",
       " 'experience': 996,\n",
       " 'extractive': 997,\n",
       " 'face': 998,\n",
       " 'globe': 999,\n",
       " ...}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2word.token2id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_text(text, tokens):\n",
    "    filtered_text = []\n",
    "    for word in text.split():\n",
    "        if word in tokens:\n",
    "            filtered_text.append(word)\n",
    "    return filtered_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "talk_pre['transcript'] = talk_pre.transcript.apply(lambda x: filter_text(x, id2word.token2id))\n",
    "talk_pre['transcript'] = talk_pre.transcript.apply(lambda x: ' '.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "talk_pre['length'] = talk_pre['transcript'].apply(lambda x: len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "talk_pre = talk_pre[talk_pre.length >= 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49444"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_words = talk_pre['transcript'].apply(lambda x: x.split()).values\n",
    "\n",
    "id2word = gensim.corpora.Dictionary(data_words)\n",
    "\n",
    "corpus = [id2word.doc2bow(text) for text in data_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49122"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path.expanduser('~/work/data/talk_pre.pkl'), 'wb') as output:\n",
    "    pickle.dump(talk_pre, output, pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path.expanduser('~/work/data/talk_pre.pkl'), \"rb\") as input_file:\n",
    "    talk_pre = pickle.load(input_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "talk_pre['length'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
    "    \"\"\"\n",
    "    Compute c_v coherence for various number of topics\n",
    "\n",
    "    Parameters:\n",
    "    ----------\n",
    "    dictionary : Gensim dictionary\n",
    "    corpus : Gensim corpus\n",
    "    texts : List of input texts\n",
    "    limit : Max num of topics\n",
    "\n",
    "    Returns:\n",
    "    -------\n",
    "    model_list : List of LDA topic models\n",
    "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
    "    \"\"\"\n",
    "    coherence_values = []\n",
    "    model_list = []\n",
    "    for num_topics in range(start, limit, step):\n",
    "        start = time.time()\n",
    "        mallet_path = path.expanduser('~/work/modules/mallet-2.0.8/bin/mallet')\n",
    "        model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=num_topics, id2word=id2word, random_seed = 5, alpha = 0.1, iterations = 800, optimize_interval = 10)\n",
    "        dur = time.time() - start\n",
    "        print(\"Time Elapsed\",dur,'s')\n",
    "        model_list.append(model)\n",
    "        coherencemodel = gensim.models.CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
    "        coherence_per_topic = coherencemodel.get_coherence_per_topic()\n",
    "        coherence_values.append((min(coherence_per_topic),np.mean(coherence_per_topic)))\n",
    "        print('No. Topics ', num_topics,' - Mean Coherence ',np.mean(coherence_per_topic),' - Min Coherence ',min(coherence_per_topic))\n",
    "\n",
    "    return model_list, coherence_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Elapsed 109.08038473129272 s\n",
      "No. Topics  5  - Mean Coherence  0.45678589932822466  - Min Coherence  0.3818414899064245\n",
      "Time Elapsed 133.3303542137146 s\n",
      "No. Topics  10  - Mean Coherence  0.49149079011935254  - Min Coherence  0.40242422414715645\n",
      "Time Elapsed 150.06712174415588 s\n",
      "No. Topics  15  - Mean Coherence  0.5200666809904619  - Min Coherence  0.40917280671113965\n",
      "Time Elapsed 152.27088117599487 s\n",
      "No. Topics  20  - Mean Coherence  0.5222271483209874  - Min Coherence  0.3709827697437483\n",
      "Time Elapsed 158.40461993217468 s\n",
      "No. Topics  25  - Mean Coherence  0.5349924339857826  - Min Coherence  0.42738794499061095\n",
      "Time Elapsed 173.73261857032776 s\n",
      "No. Topics  30  - Mean Coherence  0.5161328839678657  - Min Coherence  0.3749419431018066\n",
      "Time Elapsed 185.2719020843506 s\n",
      "No. Topics  35  - Mean Coherence  0.5362783406217556  - Min Coherence  0.2899994791925109\n",
      "Time Elapsed 176.05911684036255 s\n",
      "No. Topics  40  - Mean Coherence  0.5319178976421575  - Min Coherence  0.37050387101085286\n",
      "Time Elapsed 180.17695331573486 s\n",
      "No. Topics  45  - Mean Coherence  0.5268677901465988  - Min Coherence  0.2769223810119311\n",
      "Time Elapsed 183.63627934455872 s\n",
      "No. Topics  50  - Mean Coherence  0.5321272684899013  - Min Coherence  0.3696117276255807\n",
      "Time Elapsed 215.98038363456726 s\n",
      "No. Topics  55  - Mean Coherence  0.5236254925730359  - Min Coherence  0.23946437881848065\n",
      "Time Elapsed 214.92591381072998 s\n",
      "No. Topics  60  - Mean Coherence  0.5165729040001038  - Min Coherence  0.30302497342257084\n",
      "Time Elapsed 191.7466721534729 s\n",
      "No. Topics  65  - Mean Coherence  0.5179176025091674  - Min Coherence  0.2694756302351193\n"
     ]
    }
   ],
   "source": [
    "limit=66\n",
    "start=5\n",
    "step=5\n",
    "# Can take a long time to run.\n",
    "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_words, start=start, limit=limit, step=step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show graph\n",
    "plt.figure(figsize=(15,10))\n",
    "x = range(start, limit, step)\n",
    "coherence_means, coherence_mins = zip(*coherence_values)\n",
    "plt.plot(x, coherence_means)\n",
    "plt.plot(x, coherence_mins)\n",
    "plt.xlabel(\"Num Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.legend(['Minimum Coherence', 'Mean Coherence'], loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time Elapsed 163.69370222091675 s\n",
      "\n",
      "Coherence Score:  0.5349924339857826 \n",
      "\n",
      "\n",
      "Topic 21 - Coherence 0.42738794499061095 \n",
      " 0.019*\"art\" + 0.012*\"film\" + 0.012*\"show\" + 0.011*\"image\" + 0.011*\"work\" + 0.010*\"artist\" + 0.010*\"kind\" + 0.009*\"movie\" + 0.009*\"made\" + 0.009*\"picture\" \n",
      "\n",
      "Topic 5 - Coherence 0.43114296442641925 \n",
      " 0.085*\"brain\" + 0.013*\"neuron\" + 0.010*\"body\" + 0.010*\"memory\" + 0.007*\"sleep\" + 0.007*\"system\" + 0.007*\"cell\" + 0.007*\"human\" + 0.007*\"signal\" + 0.007*\"area\" \n",
      "\n",
      "Topic 20 - Coherence 0.44130449832094953 \n",
      " 0.018*\"question\" + 0.015*\"good\" + 0.011*\"problem\" + 0.011*\"lot\" + 0.010*\"answer\" + 0.010*\"kind\" + 0.009*\"number\" + 0.007*\"study\" + 0.007*\"work\" + 0.007*\"find\" \n",
      "\n",
      "Topic 2 - Coherence 0.447509440291355 \n",
      " 0.019*\"god\" + 0.013*\"book\" + 0.011*\"great\" + 0.010*\"religion\" + 0.010*\"story\" + 0.009*\"century\" + 0.008*\"man\" + 0.008*\"culture\" + 0.008*\"history\" + 0.007*\"called\" \n",
      "\n",
      "Topic 8 - Coherence 0.45037682707226423 \n",
      " 0.029*\"life\" + 0.012*\"feel\" + 0.011*\"story\" + 0.010*\"love\" + 0.009*\"change\" + 0.008*\"experience\" + 0.008*\"human\" + 0.006*\"live\" + 0.006*\"future\" + 0.006*\"talk\" \n",
      "\n",
      "Topic 15 - Coherence 0.4515220031690628 \n",
      " 0.026*\"music\" + 0.024*\"sound\" + 0.023*\"word\" + 0.022*\"language\" + 0.012*\"hear\" + 0.012*\"song\" + 0.011*\"play\" + 0.011*\"voice\" + 0.008*\"english\" + 0.008*\"ted\" \n",
      "\n",
      "Topic 14 - Coherence 0.45301164934125726 \n",
      " 0.021*\"game\" + 0.016*\"robot\" + 0.013*\"video\" + 0.011*\"play\" + 0.009*\"move\" + 0.009*\"computer\" + 0.008*\"show\" + 0.007*\"machine\" + 0.007*\"light\" + 0.006*\"camera\" \n",
      "\n",
      "Topic 6 - Coherence 0.45577345309992606 \n",
      " 0.022*\"data\" + 0.015*\"information\" + 0.013*\"computer\" + 0.012*\"internet\" + 0.012*\"phone\" + 0.008*\"medium\" + 0.008*\"online\" + 0.007*\"book\" + 0.007*\"google\" + 0.006*\"technology\" \n",
      "\n",
      "Topic 7 - Coherence 0.45656346269897796 \n",
      " 0.014*\"hand\" + 0.012*\"back\" + 0.011*\"put\" + 0.008*\"guy\" + 0.008*\"head\" + 0.007*\"eye\" + 0.007*\"good\" + 0.005*\"yeah\" + 0.005*\"face\" + 0.005*\"big\" \n",
      "\n",
      "Topic 22 - Coherence 0.4747643128733972 \n",
      " 0.018*\"technology\" + 0.015*\"work\" + 0.013*\"idea\" + 0.013*\"problem\" + 0.011*\"system\" + 0.010*\"human\" + 0.009*\"design\" + 0.008*\"kind\" + 0.008*\"change\" + 0.006*\"create\" \n",
      "\n",
      "Topic 11 - Coherence 0.5052255631864517 \n",
      " 0.042*\"cell\" + 0.016*\"body\" + 0.011*\"material\" + 0.010*\"structure\" + 0.008*\"molecule\" + 0.008*\"blood\" + 0.008*\"tissue\" + 0.007*\"skin\" + 0.007*\"inside\" + 0.007*\"put\" \n",
      "\n",
      "Topic 4 - Coherence 0.5187873796550649 \n",
      " 0.024*\"day\" + 0.013*\"back\" + 0.011*\"thought\" + 0.010*\"started\" + 0.009*\"life\" + 0.008*\"home\" + 0.008*\"friend\" + 0.008*\"wanted\" + 0.008*\"story\" + 0.007*\"family\" \n",
      "\n",
      "Topic 0 - Coherence 0.536639193531724 \n",
      " 0.024*\"war\" + 0.008*\"police\" + 0.008*\"country\" + 0.008*\"violence\" + 0.007*\"military\" + 0.007*\"refugee\" + 0.006*\"prison\" + 0.006*\"soldier\" + 0.006*\"weapon\" + 0.006*\"conflict\" \n",
      "\n",
      "Topic 18 - Coherence 0.5387134976614774 \n",
      " 0.016*\"water\" + 0.015*\"earth\" + 0.011*\"ice\" + 0.010*\"ocean\" + 0.010*\"planet\" + 0.009*\"mar\" + 0.008*\"sea\" + 0.008*\"mile\" + 0.007*\"mountain\" + 0.007*\"foot\" \n",
      "\n",
      "Topic 9 - Coherence 0.5416671640778915 \n",
      " 0.025*\"food\" + 0.023*\"water\" + 0.020*\"energy\" + 0.011*\"oil\" + 0.010*\"plant\" + 0.009*\"carbon\" + 0.008*\"fuel\" + 0.008*\"eat\" + 0.007*\"lot\" + 0.007*\"power\" \n",
      "\n",
      "Topic 19 - Coherence 0.5696474644351069 \n",
      " 0.019*\"human\" + 0.019*\"gene\" + 0.015*\"dna\" + 0.014*\"specie\" + 0.010*\"virus\" + 0.010*\"genome\" + 0.009*\"animal\" + 0.009*\"genetic\" + 0.008*\"evolution\" + 0.008*\"ant\" \n",
      "\n",
      "Topic 1 - Coherence 0.5745453288176896 \n",
      " 0.047*\"city\" + 0.028*\"car\" + 0.024*\"building\" + 0.012*\"place\" + 0.011*\"street\" + 0.011*\"space\" + 0.008*\"road\" + 0.008*\"york\" + 0.008*\"built\" + 0.006*\"community\" \n",
      "\n",
      "Topic 10 - Coherence 0.5902639305658107 \n",
      " 0.024*\"money\" + 0.023*\"dollar\" + 0.021*\"company\" + 0.015*\"business\" + 0.012*\"cost\" + 0.012*\"market\" + 0.009*\"million\" + 0.009*\"job\" + 0.008*\"pay\" + 0.008*\"work\" \n",
      "\n",
      "Topic 24 - Coherence 0.6108931773725613 \n",
      " 0.076*\"woman\" + 0.029*\"men\" + 0.028*\"child\" + 0.020*\"girl\" + 0.015*\"baby\" + 0.014*\"mother\" + 0.013*\"young\" + 0.013*\"family\" + 0.012*\"sex\" + 0.010*\"black\" \n",
      "\n",
      "Topic 16 - Coherence 0.6111444838005902 \n",
      " 0.032*\"country\" + 0.029*\"percent\" + 0.019*\"africa\" + 0.011*\"state\" + 0.011*\"million\" + 0.011*\"china\" + 0.010*\"population\" + 0.009*\"india\" + 0.009*\"today\" + 0.009*\"number\" \n",
      "\n",
      "Topic 12 - Coherence 0.6273791440365895 \n",
      " 0.049*\"school\" + 0.032*\"kid\" + 0.028*\"student\" + 0.020*\"child\" + 0.018*\"teacher\" + 0.016*\"education\" + 0.011*\"high\" + 0.011*\"learning\" + 0.011*\"class\" + 0.010*\"university\" \n",
      "\n",
      "Topic 23 - Coherence 0.6432008044636636 \n",
      " 0.021*\"animal\" + 0.016*\"tree\" + 0.015*\"fish\" + 0.014*\"forest\" + 0.012*\"ocean\" + 0.012*\"specie\" + 0.008*\"bird\" + 0.008*\"coral\" + 0.007*\"water\" + 0.007*\"shark\" \n",
      "\n",
      "Topic 17 - Coherence 0.6482671250098597 \n",
      " 0.023*\"patient\" + 0.022*\"cancer\" + 0.020*\"disease\" + 0.019*\"health\" + 0.016*\"drug\" + 0.015*\"doctor\" + 0.012*\"care\" + 0.011*\"medical\" + 0.010*\"hospital\" + 0.010*\"treatment\" \n",
      "\n",
      "Topic 13 - Coherence 0.6744581853530904 \n",
      " 0.024*\"universe\" + 0.016*\"light\" + 0.015*\"planet\" + 0.015*\"star\" + 0.014*\"space\" + 0.013*\"earth\" + 0.010*\"galaxy\" + 0.009*\"sun\" + 0.009*\"particle\" + 0.008*\"energy\" \n",
      "\n",
      "Topic 3 - Coherence 0.6946218513927718 \n",
      " 0.015*\"government\" + 0.014*\"state\" + 0.012*\"country\" + 0.010*\"political\" + 0.009*\"law\" + 0.008*\"power\" + 0.008*\"public\" + 0.007*\"democracy\" + 0.007*\"change\" + 0.007*\"society\" \n",
      "\n"
     ]
    }
   ],
   "source": [
    "num_topics = 25\n",
    "start = time.time()\n",
    "mallet_path = path.expanduser('~/work/modules/mallet-2.0.8/bin/mallet') # update this path\n",
    "# prefix = path.expanduser('~/work/jupyter_notebooks/notebooks/mallet-dep/')\n",
    "model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=num_topics, id2word=id2word, random_seed = 5, alpha = 0.1, iterations = 800, optimize_interval = 10)\n",
    "\n",
    "dur = time.time() - start\n",
    "\n",
    "print(\"Time Elapsed\",dur,'s')\n",
    "\n",
    "coherence_model_ldamallet = gensim.models.CoherenceModel(model=model, texts=data_words, dictionary=id2word, coherence='c_v')\n",
    "coherence_ldamallet = coherence_model_ldamallet.get_coherence()\n",
    "print('\\nCoherence Score: ', coherence_ldamallet, '\\n\\n')\n",
    "\n",
    "coherence_ldamallet_per_topic = coherence_model_ldamallet.get_coherence_per_topic()\n",
    "# Show Topics\n",
    "for i in np.argsort(coherence_ldamallet_per_topic):\n",
    "    print('Topic', i, '- Coherence', coherence_ldamallet_per_topic[i], '\\n' ,model.print_topic(i, topn=10),'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_topics_sentences(ldamodel, corpus, texts):\n",
    "    # Init output\n",
    "    sent_topics_df = pd.DataFrame()\n",
    "\n",
    "    # Get main topic in each document\n",
    "    for i, row in enumerate(ldamodel[corpus]):\n",
    "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
    "        # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
    "        for j, (topic_num, prop_topic) in enumerate(row):\n",
    "            if j == 0:  # => dominant topic\n",
    "                wp = ldamodel.show_topic(topic_num)\n",
    "                topic_keywords = \", \".join([word for word, prop in wp])\n",
    "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
    "            else:\n",
    "                break\n",
    "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
    "\n",
    "    # Add original text to the end of the output\n",
    "    contents = pd.Series(texts)\n",
    "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
    "    return(sent_topics_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Document_No</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>thank much chris come stage twice extremely gr...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.4592</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>flew air force two eight</td>\n",
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       "      <td>2</td>\n",
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       "      <td>6.0</td>\n",
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       "      <td>life, change, feel, question, good, work, kind...</td>\n",
       "      <td>true story every bit true</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5764</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>soon tipper left white house</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.7452</td>\n",
       "      <td>city, car, building, street, place, community,...</td>\n",
       "      <td>driving home nashville little farm mile east n...</td>\n",
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       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4223</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>looked rear view mirror sudden hit motorcade back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6363</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>dinnertime started looking place eat got exit ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5284</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>took order went couple booth next lowered voic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>next day continuing totally true story got fly...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>data, book, information, internet, medium, lot...</td>\n",
       "      <td>turned staff extremely upset wire service nige...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Document_No  Dominant_Topic  Topic_Perc_Contrib  \\\n",
       "0            0             1.0              0.3156   \n",
       "1            1            11.0              0.4592   \n",
       "2            2             6.0              0.4213   \n",
       "3            3             1.0              0.5764   \n",
       "4            4             3.0              0.7452   \n",
       "5            5             1.0              0.4223   \n",
       "6            6             1.0              0.6363   \n",
       "7            7             1.0              0.5284   \n",
       "8            8             1.0              0.6467   \n",
       "9            9            14.0              0.4740   \n",
       "\n",
       "                                            Keywords  \\\n",
       "0  day, back, thought, started, night, room, guy,...   \n",
       "1  earth, planet, space, life, mar, star, sun, mo...   \n",
       "2  life, change, feel, question, good, work, kind...   \n",
       "3  day, back, thought, started, night, room, guy,...   \n",
       "4  city, car, building, street, place, community,...   \n",
       "5  day, back, thought, started, night, room, guy,...   \n",
       "6  day, back, thought, started, night, room, guy,...   \n",
       "7  day, back, thought, started, night, room, guy,...   \n",
       "8  day, back, thought, started, night, room, guy,...   \n",
       "9  data, book, information, internet, medium, lot...   \n",
       "\n",
       "                                                Text  \n",
       "0  thank much chris come stage twice extremely gr...  \n",
       "1                           flew air force two eight  \n",
       "2                          true story every bit true  \n",
       "3                       soon tipper left white house  \n",
       "4  driving home nashville little farm mile east n...  \n",
       "5  looked rear view mirror sudden hit motorcade back  \n",
       "6  dinnertime started looking place eat got exit ...  \n",
       "7  took order went couple booth next lowered voic...  \n",
       "8  next day continuing totally true story got fly...  \n",
       "9  turned staff extremely upset wire service nige...  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=model, corpus=corpus, texts=talk_pre['transcript'].values)\n",
    "\n",
    "# Format\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "\n",
    "# Show\n",
    "df_dominant_topic.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9537</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>taking shower hotel room sydney moment died mi...</td>\n",
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       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8767</td>\n",
       "      <td>school, kid, student, teacher, child, educatio...</td>\n",
       "      <td>geoffrey canada john percent kid graduated hig...</td>\n",
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       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.9678</td>\n",
       "      <td>city, car, building, street, place, community,...</td>\n",
       "      <td>another infill project parking lot office park...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.9241</td>\n",
       "      <td>woman, child, men, family, girl, mother, young...</td>\n",
       "      <td>yeah tumultuous fighting helping sober alcohol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.8757</td>\n",
       "      <td>game, play, film, movie, guy, show, video, pla...</td>\n",
       "      <td>old looking movie watch hbo many remember ferr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.9415</td>\n",
       "      <td>life, change, feel, question, good, work, kind...</td>\n",
       "      <td>topic gratefulness connection happiness gratef...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.9391</td>\n",
       "      <td>word, language, science, god, human, idea, que...</td>\n",
       "      <td>take three famous word western philosophy cogi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.9246</td>\n",
       "      <td>music, sound, song, play, hear, voice, dance, ...</td>\n",
       "      <td>lungi dance lungi dance lungi dance lungi danc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.9049</td>\n",
       "      <td>country, africa, india, china, state, african,...</td>\n",
       "      <td>fact million displaced home violence persecuti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9197</td>\n",
       "      <td>animal, specie, human, bird, plant, bee, ant, ...</td>\n",
       "      <td>newly mated queen fly somewhere drop wing dig ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.9154</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>planet orbiting star sun called exoplanet extr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.9505</td>\n",
       "      <td>percent, dollar, money, company, business, cou...</td>\n",
       "      <td>health impact fund work fixed reward pool ever...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.9476</td>\n",
       "      <td>water, ocean, sea, ice, fish, forest, tree, pl...</td>\n",
       "      <td>range coast redwood red largest individual spe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>data, book, information, internet, medium, lot...</td>\n",
       "      <td>pick book bookstore amazon rating projected co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.9594</td>\n",
       "      <td>brain, neuron, memory, body, pattern, informat...</td>\n",
       "      <td>happens brain beautiful attractive face activa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.9347</td>\n",
       "      <td>political, state, government, country, change,...</td>\n",
       "      <td>let begin first giving brief history may indul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.9372</td>\n",
       "      <td>disease, patient, health, cancer, doctor, drug...</td>\n",
       "      <td>novocure conducted first phase three randomize...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.9383</td>\n",
       "      <td>cell, dna, body, gene, cancer, human, molecule...</td>\n",
       "      <td>sense various reason took oncogene attached bl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.8901</td>\n",
       "      <td>art, building, design, work, artist, project, ...</td>\n",
       "      <td>hey hey black eyed susie hey hey black eyed su...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.9097</td>\n",
       "      <td>technology, computer, system, work, machine, i...</td>\n",
       "      <td>anyway excited inform allowed increasingly usi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>light, universe, space, line, number, black, p...</td>\n",
       "      <td>lot let take closer look look back towards gal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.9715</td>\n",
       "      <td>water, food, energy, oil, carbon, plant, fuel,...</td>\n",
       "      <td>molten salt reactor core heat exchanger hot sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.9463</td>\n",
       "      <td>war, police, state, violence, law, case, milit...</td>\n",
       "      <td>item recovered numerous mass graf across homel...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_Num  Topic_Perc_Contrib  \\\n",
       "0         0.0              0.9661   \n",
       "1         1.0              0.9537   \n",
       "2         2.0              0.8767   \n",
       "3         3.0              0.9678   \n",
       "4         4.0              0.9241   \n",
       "5         5.0              0.8757   \n",
       "6         6.0              0.9415   \n",
       "7         7.0              0.9391   \n",
       "8         8.0              0.9246   \n",
       "9         9.0              0.9049   \n",
       "10       10.0              0.9197   \n",
       "11       11.0              0.9154   \n",
       "12       12.0              0.9505   \n",
       "13       13.0              0.9476   \n",
       "14       14.0              0.9360   \n",
       "15       15.0              0.9594   \n",
       "16       16.0              0.9347   \n",
       "17       17.0              0.9372   \n",
       "18       18.0              0.9383   \n",
       "19       19.0              0.8901   \n",
       "20       20.0              0.9097   \n",
       "21       21.0              0.9711   \n",
       "22       22.0              0.9715   \n",
       "23       23.0              0.9463   \n",
       "\n",
       "                                             Keywords  \\\n",
       "0   hand, put, body, robot, move, back, foot, show...   \n",
       "1   day, back, thought, started, night, room, guy,...   \n",
       "2   school, kid, student, teacher, child, educatio...   \n",
       "3   city, car, building, street, place, community,...   \n",
       "4   woman, child, men, family, girl, mother, young...   \n",
       "5   game, play, film, movie, guy, show, video, pla...   \n",
       "6   life, change, feel, question, good, work, kind...   \n",
       "7   word, language, science, god, human, idea, que...   \n",
       "8   music, sound, song, play, hear, voice, dance, ...   \n",
       "9   country, africa, india, china, state, african,...   \n",
       "10  animal, specie, human, bird, plant, bee, ant, ...   \n",
       "11  earth, planet, space, life, mar, star, sun, mo...   \n",
       "12  percent, dollar, money, company, business, cou...   \n",
       "13  water, ocean, sea, ice, fish, forest, tree, pl...   \n",
       "14  data, book, information, internet, medium, lot...   \n",
       "15  brain, neuron, memory, body, pattern, informat...   \n",
       "16  political, state, government, country, change,...   \n",
       "17  disease, patient, health, cancer, doctor, drug...   \n",
       "18  cell, dna, body, gene, cancer, human, molecule...   \n",
       "19  art, building, design, work, artist, project, ...   \n",
       "20  technology, computer, system, work, machine, i...   \n",
       "21  light, universe, space, line, number, black, p...   \n",
       "22  water, food, energy, oil, carbon, plant, fuel,...   \n",
       "23  war, police, state, violence, law, case, milit...   \n",
       "\n",
       "                                                 Text  \n",
       "0   treadmill treadmill belt watch animal foot vid...  \n",
       "1   taking shower hotel room sydney moment died mi...  \n",
       "2   geoffrey canada john percent kid graduated hig...  \n",
       "3   another infill project parking lot office park...  \n",
       "4   yeah tumultuous fighting helping sober alcohol...  \n",
       "5   old looking movie watch hbo many remember ferr...  \n",
       "6   topic gratefulness connection happiness gratef...  \n",
       "7   take three famous word western philosophy cogi...  \n",
       "8   lungi dance lungi dance lungi dance lungi danc...  \n",
       "9   fact million displaced home violence persecuti...  \n",
       "10  newly mated queen fly somewhere drop wing dig ...  \n",
       "11  planet orbiting star sun called exoplanet extr...  \n",
       "12  health impact fund work fixed reward pool ever...  \n",
       "13  range coast redwood red largest individual spe...  \n",
       "14  pick book bookstore amazon rating projected co...  \n",
       "15  happens brain beautiful attractive face activa...  \n",
       "16  let begin first giving brief history may indul...  \n",
       "17  novocure conducted first phase three randomize...  \n",
       "18  sense various reason took oncogene attached bl...  \n",
       "19  hey hey black eyed susie hey hey black eyed su...  \n",
       "20  anyway excited inform allowed increasingly usi...  \n",
       "21  lot let take closer look look back towards gal...  \n",
       "22  molten salt reactor core heat exchanger hot sa...  \n",
       "23  item recovered numerous mass graf across homel...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group top 5 sentences under each topic\n",
    "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
    "\n",
    "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
    "\n",
    "for i, grp in sent_topics_outdf_grpd:\n",
    "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
    "                                             grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
    "                                            axis=0)\n",
    "\n",
    "# Reset Index    \n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Format\n",
    "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n",
    "\n",
    "# Show\n",
    "sent_topics_sorteddf_mallet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Keywords</th>\n",
       "      <th>Num_Documents</th>\n",
       "      <th>Perc_Documents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>1807.0</td>\n",
       "      <td>0.0348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>5051.0</td>\n",
       "      <td>0.0973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>life, change, feel, question, good, work, kind...</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>0.0356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>1386.0</td>\n",
       "      <td>0.0267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>city, car, building, street, place, community,...</td>\n",
       "      <td>2236.0</td>\n",
       "      <td>0.0431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51893.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51894.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51895.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51896.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51897.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>technology, computer, system, work, machine, i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51898 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dominant_Topic                                     Topic_Keywords  \\\n",
       "0.0                 1.0  day, back, thought, started, night, room, guy,...   \n",
       "1.0                11.0  earth, planet, space, life, mar, star, sun, mo...   \n",
       "2.0                 6.0  life, change, feel, question, good, work, kind...   \n",
       "3.0                 1.0  day, back, thought, started, night, room, guy,...   \n",
       "4.0                 3.0  city, car, building, street, place, community,...   \n",
       "...                 ...                                                ...   \n",
       "51893.0            11.0  earth, planet, space, life, mar, star, sun, mo...   \n",
       "51894.0             1.0  day, back, thought, started, night, room, guy,...   \n",
       "51895.0             1.0  day, back, thought, started, night, room, guy,...   \n",
       "51896.0            11.0  earth, planet, space, life, mar, star, sun, mo...   \n",
       "51897.0            20.0  technology, computer, system, work, machine, i...   \n",
       "\n",
       "         Num_Documents  Perc_Documents  \n",
       "0.0             1807.0          0.0348  \n",
       "1.0             5051.0          0.0973  \n",
       "2.0             1846.0          0.0356  \n",
       "3.0             1386.0          0.0267  \n",
       "4.0             2236.0          0.0431  \n",
       "...                ...             ...  \n",
       "51893.0            NaN             NaN  \n",
       "51894.0            NaN             NaN  \n",
       "51895.0            NaN             NaN  \n",
       "51896.0            NaN             NaN  \n",
       "51897.0            NaN             NaN  \n",
       "\n",
       "[51898 rows x 4 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Documents for Each Topic\n",
    "topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n",
    "\n",
    "# Percentage of Documents for Each Topic\n",
    "topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n",
    "\n",
    "# Topic Number and Keywords\n",
    "topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n",
    "\n",
    "# Concatenate Column wise\n",
    "df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n",
    "\n",
    "# Change Column names\n",
    "df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n",
    "\n",
    "# Show\n",
    "df_dominant_topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document_No</th>\n",
       "      <th>Dominant_Topic</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3156</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>thank much chris come stage twice extremely gr...</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.4592</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>flew air force two eight</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.4213</td>\n",
       "      <td>life, change, feel, question, good, work, kind...</td>\n",
       "      <td>true story every bit true</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5764</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>soon tipper left white house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.7452</td>\n",
       "      <td>city, car, building, street, place, community,...</td>\n",
       "      <td>driving home nashville little farm mile east n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4223</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>looked rear view mirror sudden hit motorcade back</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6363</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>dinnertime started looking place eat got exit ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5284</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>took order went couple booth next lowered voic...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6467</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>next day continuing totally true story got fly...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.4740</td>\n",
       "      <td>data, book, information, internet, medium, lot...</td>\n",
       "      <td>turned staff extremely upset wire service nige...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Document_No  Dominant_Topic  Topic_Perc_Contrib  \\\n",
       "0            0             1.0              0.3156   \n",
       "1            1            11.0              0.4592   \n",
       "2            2             6.0              0.4213   \n",
       "3            3             1.0              0.5764   \n",
       "4            4             3.0              0.7452   \n",
       "5            5             1.0              0.4223   \n",
       "6            6             1.0              0.6363   \n",
       "7            7             1.0              0.5284   \n",
       "8            8             1.0              0.6467   \n",
       "9            9            14.0              0.4740   \n",
       "\n",
       "                                            Keywords  \\\n",
       "0  day, back, thought, started, night, room, guy,...   \n",
       "1  earth, planet, space, life, mar, star, sun, mo...   \n",
       "2  life, change, feel, question, good, work, kind...   \n",
       "3  day, back, thought, started, night, room, guy,...   \n",
       "4  city, car, building, street, place, community,...   \n",
       "5  day, back, thought, started, night, room, guy,...   \n",
       "6  day, back, thought, started, night, room, guy,...   \n",
       "7  day, back, thought, started, night, room, guy,...   \n",
       "8  day, back, thought, started, night, room, guy,...   \n",
       "9  data, book, information, internet, medium, lot...   \n",
       "\n",
       "                                                Text  \n",
       "0  thank much chris come stage twice extremely gr...  \n",
       "1                           flew air force two eight  \n",
       "2                          true story every bit true  \n",
       "3                       soon tipper left white house  \n",
       "4  driving home nashville little farm mile east n...  \n",
       "5  looked rear view mirror sudden hit motorcade back  \n",
       "6  dinnertime started looking place eat got exit ...  \n",
       "7  took order went couple booth next lowered voic...  \n",
       "8  next day continuing totally true story got fly...  \n",
       "9  turned staff extremely upset wire service nige...  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_topic_sents_keywords = format_topics_sentences(ldamodel=model, corpus=corpus, texts=talk_pre['transcript'].values)\n",
    "\n",
    "# Format\n",
    "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
    "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
    "\n",
    "# Show\n",
    "df_dominant_topic.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.1774,\n",
       "        'patient, doctor, disease, drug, hospital, treatment, health, cancer, child, medicine',\n",
       "        'felt needed provide care child currently deprived treatment humanitarian mission scientific mission test limit visual plasticity mission thread together perfectly add impossible without implement twin mission ago launched project prakash prakash sanskrit word light bringing light child chance shedding light deepest mystery neuroscience logo though extremely irish derived indian symbol diya earthen lamp prakash overall effort component outreach identify child care medical treatment subsequent study short video clip illustrates component'],\n",
       "       [0.186,\n",
       "        'patient, doctor, disease, drug, hospital, treatment, health, cancer, child, medicine',\n",
       "        'spread infection child million child india last heard preamble india contagious word thank'],\n",
       "       [0.1913,\n",
       "        'patient, doctor, disease, drug, hospital, treatment, health, cancer, child, medicine',\n",
       "        'bma expanded include everyone young elderly disabled mentally challenged blind elite amateur runner mom baby theme included run environment breast cancer love lebanon peace simply run'],\n",
       "       ...,\n",
       "       [0.9097,\n",
       "        'patient, doctor, disease, drug, hospital, treatment, health, cancer, child, medicine',\n",
       "        'novocure conducted phase randomized trial patient recurrent gbm patient received surgery high dose radiation head line chemotherapy failed tumor grown divided patient group group received second line chemotherapy expected double versus treatment second group received tumor treating field therapy saw trial group chemotherapy treated group tumor treating field group importantly tumor treating field group suffered none side effect typical chemotherapy patient pain suffered none infection nausea diarrhea constipation fatigue expected'],\n",
       "       [0.9317,\n",
       "        'patient, doctor, disease, drug, hospital, treatment, health, cancer, child, medicine',\n",
       "        'hilma found went hospital pregnancy check nurse announced pregnant woman must tested hiv took test result showed positive found nurse bcome pregnant hiv positive pregnant living positive sure reason sterilized hiv positive form explain form nurse came already marked sign labor pain strength ask read signed'],\n",
       "       [0.9462,\n",
       "        'patient, doctor, disease, drug, hospital, treatment, health, cancer, child, medicine',\n",
       "        'approach healthcare approach improve health begin illustrate veronica veronica patient patient clinic south central los angeles came clinic chronic headache headache number particular episode troubling week came visit went emergency room los angeles emergency room doctor run test veronica result normal pain medication follow primary care doctor pain persists worsens']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "Time Elapsed 163.69370222091675 s\n",
    "\n",
    "Coherence Score:  0.5349924339857826 \n",
    "\n",
    "\n",
    "Topic 21 - Coherence 0.42738794499061095 \n",
    " 0.019*\"art\" + 0.012*\"film\" + 0.012*\"show\" + 0.011*\"image\" + 0.011*\"work\" + 0.010*\"artist\" + 0.010*\"kind\" + 0.009*\"movie\" + 0.009*\"made\" + 0.009*\"picture\" \n",
    "\n",
    "Topic 5 - Coherence 0.43114296442641925 \n",
    " 0.085*\"brain\" + 0.013*\"neuron\" + 0.010*\"body\" + 0.010*\"memory\" + 0.007*\"sleep\" + 0.007*\"system\" + 0.007*\"cell\" + 0.007*\"human\" + 0.007*\"signal\" + 0.007*\"area\" \n",
    "\n",
    "Topic 20 - Coherence 0.44130449832094953 \n",
    " 0.018*\"question\" + 0.015*\"good\" + 0.011*\"problem\" + 0.011*\"lot\" + 0.010*\"answer\" + 0.010*\"kind\" + 0.009*\"number\" + 0.007*\"study\" + 0.007*\"work\" + 0.007*\"find\" \n",
    "\n",
    "Topic 2 - Coherence 0.447509440291355 \n",
    " 0.019*\"god\" + 0.013*\"book\" + 0.011*\"great\" + 0.010*\"religion\" + 0.010*\"story\" + 0.009*\"century\" + 0.008*\"man\" + 0.008*\"culture\" + 0.008*\"history\" + 0.007*\"called\" \n",
    "\n",
    "Topic 8 - Coherence 0.45037682707226423 \n",
    " 0.029*\"life\" + 0.012*\"feel\" + 0.011*\"story\" + 0.010*\"love\" + 0.009*\"change\" + 0.008*\"experience\" + 0.008*\"human\" + 0.006*\"live\" + 0.006*\"future\" + 0.006*\"talk\" \n",
    "\n",
    "Topic 15 - Coherence 0.4515220031690628 \n",
    " 0.026*\"music\" + 0.024*\"sound\" + 0.023*\"word\" + 0.022*\"language\" + 0.012*\"hear\" + 0.012*\"song\" + 0.011*\"play\" + 0.011*\"voice\" + 0.008*\"english\" + 0.008*\"ted\" \n",
    "\n",
    "Topic 14 - Coherence 0.45301164934125726 \n",
    " 0.021*\"game\" + 0.016*\"robot\" + 0.013*\"video\" + 0.011*\"play\" + 0.009*\"move\" + 0.009*\"computer\" + 0.008*\"show\" + 0.007*\"machine\" + 0.007*\"light\" + 0.006*\"camera\" \n",
    "\n",
    "Topic 6 - Coherence 0.45577345309992606 \n",
    " 0.022*\"data\" + 0.015*\"information\" + 0.013*\"computer\" + 0.012*\"internet\" + 0.012*\"phone\" + 0.008*\"medium\" + 0.008*\"online\" + 0.007*\"book\" + 0.007*\"google\" + 0.006*\"technology\" \n",
    "\n",
    "Topic 7 - Coherence 0.45656346269897796 \n",
    " 0.014*\"hand\" + 0.012*\"back\" + 0.011*\"put\" + 0.008*\"guy\" + 0.008*\"head\" + 0.007*\"eye\" + 0.007*\"good\" + 0.005*\"yeah\" + 0.005*\"face\" + 0.005*\"big\" \n",
    "\n",
    "Topic 22 - Coherence 0.4747643128733972 \n",
    " 0.018*\"technology\" + 0.015*\"work\" + 0.013*\"idea\" + 0.013*\"problem\" + 0.011*\"system\" + 0.010*\"human\" + 0.009*\"design\" + 0.008*\"kind\" + 0.008*\"change\" + 0.006*\"create\" \n",
    "\n",
    "Topic 11 - Coherence 0.5052255631864517 \n",
    " 0.042*\"cell\" + 0.016*\"body\" + 0.011*\"material\" + 0.010*\"structure\" + 0.008*\"molecule\" + 0.008*\"blood\" + 0.008*\"tissue\" + 0.007*\"skin\" + 0.007*\"inside\" + 0.007*\"put\" \n",
    "\n",
    "Topic 4 - Coherence 0.5187873796550649 \n",
    " 0.024*\"day\" + 0.013*\"back\" + 0.011*\"thought\" + 0.010*\"started\" + 0.009*\"life\" + 0.008*\"home\" + 0.008*\"friend\" + 0.008*\"wanted\" + 0.008*\"story\" + 0.007*\"family\" \n",
    "\n",
    "Topic 0 - Coherence 0.536639193531724 \n",
    " 0.024*\"war\" + 0.008*\"police\" + 0.008*\"country\" + 0.008*\"violence\" + 0.007*\"military\" + 0.007*\"refugee\" + 0.006*\"prison\" + 0.006*\"soldier\" + 0.006*\"weapon\" + 0.006*\"conflict\" \n",
    "\n",
    "Topic 18 - Coherence 0.5387134976614774 \n",
    " 0.016*\"water\" + 0.015*\"earth\" + 0.011*\"ice\" + 0.010*\"ocean\" + 0.010*\"planet\" + 0.009*\"mar\" + 0.008*\"sea\" + 0.008*\"mile\" + 0.007*\"mountain\" + 0.007*\"foot\" \n",
    "\n",
    "Topic 9 - Coherence 0.5416671640778915 \n",
    " 0.025*\"food\" + 0.023*\"water\" + 0.020*\"energy\" + 0.011*\"oil\" + 0.010*\"plant\" + 0.009*\"carbon\" + 0.008*\"fuel\" + 0.008*\"eat\" + 0.007*\"lot\" + 0.007*\"power\" \n",
    "\n",
    "Topic 19 - Coherence 0.5696474644351069 \n",
    " 0.019*\"human\" + 0.019*\"gene\" + 0.015*\"dna\" + 0.014*\"specie\" + 0.010*\"virus\" + 0.010*\"genome\" + 0.009*\"animal\" + 0.009*\"genetic\" + 0.008*\"evolution\" + 0.008*\"ant\" \n",
    "\n",
    "Topic 1 - Coherence 0.5745453288176896 \n",
    " 0.047*\"city\" + 0.028*\"car\" + 0.024*\"building\" + 0.012*\"place\" + 0.011*\"street\" + 0.011*\"space\" + 0.008*\"road\" + 0.008*\"york\" + 0.008*\"built\" + 0.006*\"community\" \n",
    "\n",
    "Topic 10 - Coherence 0.5902639305658107 \n",
    " 0.024*\"money\" + 0.023*\"dollar\" + 0.021*\"company\" + 0.015*\"business\" + 0.012*\"cost\" + 0.012*\"market\" + 0.009*\"million\" + 0.009*\"job\" + 0.008*\"pay\" + 0.008*\"work\" \n",
    "\n",
    "Topic 24 - Coherence 0.6108931773725613 \n",
    " 0.076*\"woman\" + 0.029*\"men\" + 0.028*\"child\" + 0.020*\"girl\" + 0.015*\"baby\" + 0.014*\"mother\" + 0.013*\"young\" + 0.013*\"family\" + 0.012*\"sex\" + 0.010*\"black\" \n",
    "\n",
    "Topic 16 - Coherence 0.6111444838005902 \n",
    " 0.032*\"country\" + 0.029*\"percent\" + 0.019*\"africa\" + 0.011*\"state\" + 0.011*\"million\" + 0.011*\"china\" + 0.010*\"population\" + 0.009*\"india\" + 0.009*\"today\" + 0.009*\"number\" \n",
    "\n",
    "Topic 12 - Coherence 0.6273791440365895 \n",
    " 0.049*\"school\" + 0.032*\"kid\" + 0.028*\"student\" + 0.020*\"child\" + 0.018*\"teacher\" + 0.016*\"education\" + 0.011*\"high\" + 0.011*\"learning\" + 0.011*\"class\" + 0.010*\"university\" \n",
    "\n",
    "Topic 23 - Coherence 0.6432008044636636 \n",
    " 0.021*\"animal\" + 0.016*\"tree\" + 0.015*\"fish\" + 0.014*\"forest\" + 0.012*\"ocean\" + 0.012*\"specie\" + 0.008*\"bird\" + 0.008*\"coral\" + 0.007*\"water\" + 0.007*\"shark\" \n",
    "\n",
    "Topic 17 - Coherence 0.6482671250098597 \n",
    " 0.023*\"patient\" + 0.022*\"cancer\" + 0.020*\"disease\" + 0.019*\"health\" + 0.016*\"drug\" + 0.015*\"doctor\" + 0.012*\"care\" + 0.011*\"medical\" + 0.010*\"hospital\" + 0.010*\"treatment\" \n",
    "\n",
    "Topic 13 - Coherence 0.6744581853530904 \n",
    " 0.024*\"universe\" + 0.016*\"light\" + 0.015*\"planet\" + 0.015*\"star\" + 0.014*\"space\" + 0.013*\"earth\" + 0.010*\"galaxy\" + 0.009*\"sun\" + 0.009*\"particle\" + 0.008*\"energy\" \n",
    "\n",
    "Topic 3 - Coherence 0.6946218513927718 \n",
    " 0.015*\"government\" + 0.014*\"state\" + 0.012*\"country\" + 0.010*\"political\" + 0.009*\"law\" + 0.008*\"power\" + 0.008*\"public\" + 0.007*\"democracy\" + 0.007*\"change\" + 0.007*\"society\" \n",
    "\n",
    "'''\n",
    "df_dominant_topic[(df_dominant_topic.Dominant_Topic == 7) & (df_dominant_topic.Topic_Perc_Contrib > 0.1)].sort_values('Topic_Perc_Contrib')[['Topic_Perc_Contrib','Keywords','Text']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>talk_id</th>\n",
       "      <th>transcript</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4822</td>\n",
       "      <td>14.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>second result surprise people ant actually swi...</td>\n",
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      "text/plain": [
       "        id  talk_id                                         transcript\n",
       "4822  14.0    145.0  second result surprise people ant actually swi..."
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     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "talk_pre_extra_keywords[talk_pre_extra_keywords.transcript == 'second result surprise people ant actually switch task ant task whole life example extra food everybody else midden worker stop midden work get food become forager nest maintenance worker become forager patroller become forager every transition possible show works like say food collect patroller midden worker nest maintenance worker change forage patrol create disturbance extra patroller need nest maintenance worker switch patrol nest maintenance work need example bunch toothpick nobody ever switch back nest maintenance get nest maintenance worker inside nest foraging acts sink ant inside nest act source finally look like ant deciding moment moment whether active']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic_Num</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9661</td>\n",
       "      <td>hand, put, body, robot, move, back, foot, show...</td>\n",
       "      <td>treadmill treadmill belt watch animal foot vid...</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9537</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>taking shower hotel room sydney moment died mi...</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8767</td>\n",
       "      <td>school, kid, student, teacher, child, educatio...</td>\n",
       "      <td>geoffrey canada john percent kid graduated hig...</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.9678</td>\n",
       "      <td>city, car, building, street, place, community,...</td>\n",
       "      <td>another infill project parking lot office park...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.9241</td>\n",
       "      <td>woman, child, men, family, girl, mother, young...</td>\n",
       "      <td>yeah tumultuous fighting helping sober alcohol...</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.8757</td>\n",
       "      <td>game, play, film, movie, guy, show, video, pla...</td>\n",
       "      <td>old looking movie watch hbo many remember ferr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.9415</td>\n",
       "      <td>life, change, feel, question, good, work, kind...</td>\n",
       "      <td>topic gratefulness connection happiness gratef...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.9391</td>\n",
       "      <td>word, language, science, god, human, idea, que...</td>\n",
       "      <td>take three famous word western philosophy cogi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.9246</td>\n",
       "      <td>music, sound, song, play, hear, voice, dance, ...</td>\n",
       "      <td>lungi dance lungi dance lungi dance lungi danc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.9049</td>\n",
       "      <td>country, africa, india, china, state, african,...</td>\n",
       "      <td>fact million displaced home violence persecuti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9197</td>\n",
       "      <td>animal, specie, human, bird, plant, bee, ant, ...</td>\n",
       "      <td>newly mated queen fly somewhere drop wing dig ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.9154</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>planet orbiting star sun called exoplanet extr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.9505</td>\n",
       "      <td>percent, dollar, money, company, business, cou...</td>\n",
       "      <td>health impact fund work fixed reward pool ever...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.9476</td>\n",
       "      <td>water, ocean, sea, ice, fish, forest, tree, pl...</td>\n",
       "      <td>range coast redwood red largest individual spe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>data, book, information, internet, medium, lot...</td>\n",
       "      <td>pick book bookstore amazon rating projected co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.9594</td>\n",
       "      <td>brain, neuron, memory, body, pattern, informat...</td>\n",
       "      <td>happens brain beautiful attractive face activa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.9347</td>\n",
       "      <td>political, state, government, country, change,...</td>\n",
       "      <td>let begin first giving brief history may indul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.9372</td>\n",
       "      <td>disease, patient, health, cancer, doctor, drug...</td>\n",
       "      <td>novocure conducted first phase three randomize...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.9383</td>\n",
       "      <td>cell, dna, body, gene, cancer, human, molecule...</td>\n",
       "      <td>sense various reason took oncogene attached bl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.8901</td>\n",
       "      <td>art, building, design, work, artist, project, ...</td>\n",
       "      <td>hey hey black eyed susie hey hey black eyed su...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.9097</td>\n",
       "      <td>technology, computer, system, work, machine, i...</td>\n",
       "      <td>anyway excited inform allowed increasingly usi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>light, universe, space, line, number, black, p...</td>\n",
       "      <td>lot let take closer look look back towards gal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.9715</td>\n",
       "      <td>water, food, energy, oil, carbon, plant, fuel,...</td>\n",
       "      <td>molten salt reactor core heat exchanger hot sa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.9463</td>\n",
       "      <td>war, police, state, violence, law, case, milit...</td>\n",
       "      <td>item recovered numerous mass graf across homel...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_Num  Topic_Perc_Contrib  \\\n",
       "0         0.0              0.9661   \n",
       "1         1.0              0.9537   \n",
       "2         2.0              0.8767   \n",
       "3         3.0              0.9678   \n",
       "4         4.0              0.9241   \n",
       "5         5.0              0.8757   \n",
       "6         6.0              0.9415   \n",
       "7         7.0              0.9391   \n",
       "8         8.0              0.9246   \n",
       "9         9.0              0.9049   \n",
       "10       10.0              0.9197   \n",
       "11       11.0              0.9154   \n",
       "12       12.0              0.9505   \n",
       "13       13.0              0.9476   \n",
       "14       14.0              0.9360   \n",
       "15       15.0              0.9594   \n",
       "16       16.0              0.9347   \n",
       "17       17.0              0.9372   \n",
       "18       18.0              0.9383   \n",
       "19       19.0              0.8901   \n",
       "20       20.0              0.9097   \n",
       "21       21.0              0.9711   \n",
       "22       22.0              0.9715   \n",
       "23       23.0              0.9463   \n",
       "\n",
       "                                             Keywords  \\\n",
       "0   hand, put, body, robot, move, back, foot, show...   \n",
       "1   day, back, thought, started, night, room, guy,...   \n",
       "2   school, kid, student, teacher, child, educatio...   \n",
       "3   city, car, building, street, place, community,...   \n",
       "4   woman, child, men, family, girl, mother, young...   \n",
       "5   game, play, film, movie, guy, show, video, pla...   \n",
       "6   life, change, feel, question, good, work, kind...   \n",
       "7   word, language, science, god, human, idea, que...   \n",
       "8   music, sound, song, play, hear, voice, dance, ...   \n",
       "9   country, africa, india, china, state, african,...   \n",
       "10  animal, specie, human, bird, plant, bee, ant, ...   \n",
       "11  earth, planet, space, life, mar, star, sun, mo...   \n",
       "12  percent, dollar, money, company, business, cou...   \n",
       "13  water, ocean, sea, ice, fish, forest, tree, pl...   \n",
       "14  data, book, information, internet, medium, lot...   \n",
       "15  brain, neuron, memory, body, pattern, informat...   \n",
       "16  political, state, government, country, change,...   \n",
       "17  disease, patient, health, cancer, doctor, drug...   \n",
       "18  cell, dna, body, gene, cancer, human, molecule...   \n",
       "19  art, building, design, work, artist, project, ...   \n",
       "20  technology, computer, system, work, machine, i...   \n",
       "21  light, universe, space, line, number, black, p...   \n",
       "22  water, food, energy, oil, carbon, plant, fuel,...   \n",
       "23  war, police, state, violence, law, case, milit...   \n",
       "\n",
       "                                                 Text  \n",
       "0   treadmill treadmill belt watch animal foot vid...  \n",
       "1   taking shower hotel room sydney moment died mi...  \n",
       "2   geoffrey canada john percent kid graduated hig...  \n",
       "3   another infill project parking lot office park...  \n",
       "4   yeah tumultuous fighting helping sober alcohol...  \n",
       "5   old looking movie watch hbo many remember ferr...  \n",
       "6   topic gratefulness connection happiness gratef...  \n",
       "7   take three famous word western philosophy cogi...  \n",
       "8   lungi dance lungi dance lungi dance lungi danc...  \n",
       "9   fact million displaced home violence persecuti...  \n",
       "10  newly mated queen fly somewhere drop wing dig ...  \n",
       "11  planet orbiting star sun called exoplanet extr...  \n",
       "12  health impact fund work fixed reward pool ever...  \n",
       "13  range coast redwood red largest individual spe...  \n",
       "14  pick book bookstore amazon rating projected co...  \n",
       "15  happens brain beautiful attractive face activa...  \n",
       "16  let begin first giving brief history may indul...  \n",
       "17  novocure conducted first phase three randomize...  \n",
       "18  sense various reason took oncogene attached bl...  \n",
       "19  hey hey black eyed susie hey hey black eyed su...  \n",
       "20  anyway excited inform allowed increasingly usi...  \n",
       "21  lot let take closer look look back towards gal...  \n",
       "22  molten salt reactor core heat exchanger hot sa...  \n",
       "23  item recovered numerous mass graf across homel...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group top 5 sentences under each topic\n",
    "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
    "\n",
    "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
    "\n",
    "for i, grp in sent_topics_outdf_grpd:\n",
    "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
    "                                             grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
    "                                            axis=0)\n",
    "\n",
    "# Reset Index    \n",
    "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Format\n",
    "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n",
    "\n",
    "# Show\n",
    "sent_topics_sorteddf_mallet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_topics_sorteddf_mallet['Coherence'] = sent_topics_sorteddf_mallet['Topic_Num'].apply(lambda x: coherence_ldamallet_per_topic[int(x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sent_topics_sorteddf_mallet.to_csv(path.expanduser('~/work/tests/topics.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Topic_Num</th>\n",
       "      <th>Topic_Perc_Contrib</th>\n",
       "      <th>Keywords</th>\n",
       "      <th>Text</th>\n",
       "      <th>Coherence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9661</td>\n",
       "      <td>hand, put, body, robot, move, back, foot, show...</td>\n",
       "      <td>treadmill treadmill belt watch animal foot vid...</td>\n",
       "      <td>0.518444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9537</td>\n",
       "      <td>day, back, thought, started, night, room, guy,...</td>\n",
       "      <td>taking shower hotel room sydney moment died mi...</td>\n",
       "      <td>0.496757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.8767</td>\n",
       "      <td>school, kid, student, teacher, child, educatio...</td>\n",
       "      <td>geoffrey canada john percent kid graduated hig...</td>\n",
       "      <td>0.554796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.9678</td>\n",
       "      <td>city, car, building, street, place, community,...</td>\n",
       "      <td>another infill project parking lot office park...</td>\n",
       "      <td>0.554811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.9241</td>\n",
       "      <td>woman, child, men, family, girl, mother, young...</td>\n",
       "      <td>yeah tumultuous fighting helping sober alcohol...</td>\n",
       "      <td>0.619147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.8757</td>\n",
       "      <td>game, play, film, movie, guy, show, video, pla...</td>\n",
       "      <td>old looking movie watch hbo many remember ferr...</td>\n",
       "      <td>0.410137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.9415</td>\n",
       "      <td>life, change, feel, question, good, work, kind...</td>\n",
       "      <td>topic gratefulness connection happiness gratef...</td>\n",
       "      <td>0.414562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.9391</td>\n",
       "      <td>word, language, science, god, human, idea, que...</td>\n",
       "      <td>take three famous word western philosophy cogi...</td>\n",
       "      <td>0.449881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.9246</td>\n",
       "      <td>music, sound, song, play, hear, voice, dance, ...</td>\n",
       "      <td>lungi dance lungi dance lungi dance lungi danc...</td>\n",
       "      <td>0.514524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.9049</td>\n",
       "      <td>country, africa, india, china, state, african,...</td>\n",
       "      <td>fact million displaced home violence persecuti...</td>\n",
       "      <td>0.580470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.9197</td>\n",
       "      <td>animal, specie, human, bird, plant, bee, ant, ...</td>\n",
       "      <td>newly mated queen fly somewhere drop wing dig ...</td>\n",
       "      <td>0.484879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.9154</td>\n",
       "      <td>earth, planet, space, life, mar, star, sun, mo...</td>\n",
       "      <td>planet orbiting star sun called exoplanet extr...</td>\n",
       "      <td>0.604628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.9505</td>\n",
       "      <td>percent, dollar, money, company, business, cou...</td>\n",
       "      <td>health impact fund work fixed reward pool ever...</td>\n",
       "      <td>0.553010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.9476</td>\n",
       "      <td>water, ocean, sea, ice, fish, forest, tree, pl...</td>\n",
       "      <td>range coast redwood red largest individual spe...</td>\n",
       "      <td>0.615351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.9360</td>\n",
       "      <td>data, book, information, internet, medium, lot...</td>\n",
       "      <td>pick book bookstore amazon rating projected co...</td>\n",
       "      <td>0.433902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.9594</td>\n",
       "      <td>brain, neuron, memory, body, pattern, informat...</td>\n",
       "      <td>happens brain beautiful attractive face activa...</td>\n",
       "      <td>0.424319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.9347</td>\n",
       "      <td>political, state, government, country, change,...</td>\n",
       "      <td>let begin first giving brief history may indul...</td>\n",
       "      <td>0.670703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.9372</td>\n",
       "      <td>disease, patient, health, cancer, doctor, drug...</td>\n",
       "      <td>novocure conducted first phase three randomize...</td>\n",
       "      <td>0.646736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.9383</td>\n",
       "      <td>cell, dna, body, gene, cancer, human, molecule...</td>\n",
       "      <td>sense various reason took oncogene attached bl...</td>\n",
       "      <td>0.640919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.8901</td>\n",
       "      <td>art, building, design, work, artist, project, ...</td>\n",
       "      <td>hey hey black eyed susie hey hey black eyed su...</td>\n",
       "      <td>0.455280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.9097</td>\n",
       "      <td>technology, computer, system, work, machine, i...</td>\n",
       "      <td>anyway excited inform allowed increasingly usi...</td>\n",
       "      <td>0.510752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>light, universe, space, line, number, black, p...</td>\n",
       "      <td>lot let take closer look look back towards gal...</td>\n",
       "      <td>0.440435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.9715</td>\n",
       "      <td>water, food, energy, oil, carbon, plant, fuel,...</td>\n",
       "      <td>molten salt reactor core heat exchanger hot sa...</td>\n",
       "      <td>0.560177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.9463</td>\n",
       "      <td>war, police, state, violence, law, case, milit...</td>\n",
       "      <td>item recovered numerous mass graf across homel...</td>\n",
       "      <td>0.492238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic_Num  Topic_Perc_Contrib  \\\n",
       "0         0.0              0.9661   \n",
       "1         1.0              0.9537   \n",
       "2         2.0              0.8767   \n",
       "3         3.0              0.9678   \n",
       "4         4.0              0.9241   \n",
       "5         5.0              0.8757   \n",
       "6         6.0              0.9415   \n",
       "7         7.0              0.9391   \n",
       "8         8.0              0.9246   \n",
       "9         9.0              0.9049   \n",
       "10       10.0              0.9197   \n",
       "11       11.0              0.9154   \n",
       "12       12.0              0.9505   \n",
       "13       13.0              0.9476   \n",
       "14       14.0              0.9360   \n",
       "15       15.0              0.9594   \n",
       "16       16.0              0.9347   \n",
       "17       17.0              0.9372   \n",
       "18       18.0              0.9383   \n",
       "19       19.0              0.8901   \n",
       "20       20.0              0.9097   \n",
       "21       21.0              0.9711   \n",
       "22       22.0              0.9715   \n",
       "23       23.0              0.9463   \n",
       "\n",
       "                                             Keywords  \\\n",
       "0   hand, put, body, robot, move, back, foot, show...   \n",
       "1   day, back, thought, started, night, room, guy,...   \n",
       "2   school, kid, student, teacher, child, educatio...   \n",
       "3   city, car, building, street, place, community,...   \n",
       "4   woman, child, men, family, girl, mother, young...   \n",
       "5   game, play, film, movie, guy, show, video, pla...   \n",
       "6   life, change, feel, question, good, work, kind...   \n",
       "7   word, language, science, god, human, idea, que...   \n",
       "8   music, sound, song, play, hear, voice, dance, ...   \n",
       "9   country, africa, india, china, state, african,...   \n",
       "10  animal, specie, human, bird, plant, bee, ant, ...   \n",
       "11  earth, planet, space, life, mar, star, sun, mo...   \n",
       "12  percent, dollar, money, company, business, cou...   \n",
       "13  water, ocean, sea, ice, fish, forest, tree, pl...   \n",
       "14  data, book, information, internet, medium, lot...   \n",
       "15  brain, neuron, memory, body, pattern, informat...   \n",
       "16  political, state, government, country, change,...   \n",
       "17  disease, patient, health, cancer, doctor, drug...   \n",
       "18  cell, dna, body, gene, cancer, human, molecule...   \n",
       "19  art, building, design, work, artist, project, ...   \n",
       "20  technology, computer, system, work, machine, i...   \n",
       "21  light, universe, space, line, number, black, p...   \n",
       "22  water, food, energy, oil, carbon, plant, fuel,...   \n",
       "23  war, police, state, violence, law, case, milit...   \n",
       "\n",
       "                                                 Text  Coherence  \n",
       "0   treadmill treadmill belt watch animal foot vid...   0.518444  \n",
       "1   taking shower hotel room sydney moment died mi...   0.496757  \n",
       "2   geoffrey canada john percent kid graduated hig...   0.554796  \n",
       "3   another infill project parking lot office park...   0.554811  \n",
       "4   yeah tumultuous fighting helping sober alcohol...   0.619147  \n",
       "5   old looking movie watch hbo many remember ferr...   0.410137  \n",
       "6   topic gratefulness connection happiness gratef...   0.414562  \n",
       "7   take three famous word western philosophy cogi...   0.449881  \n",
       "8   lungi dance lungi dance lungi dance lungi danc...   0.514524  \n",
       "9   fact million displaced home violence persecuti...   0.580470  \n",
       "10  newly mated queen fly somewhere drop wing dig ...   0.484879  \n",
       "11  planet orbiting star sun called exoplanet extr...   0.604628  \n",
       "12  health impact fund work fixed reward pool ever...   0.553010  \n",
       "13  range coast redwood red largest individual spe...   0.615351  \n",
       "14  pick book bookstore amazon rating projected co...   0.433902  \n",
       "15  happens brain beautiful attractive face activa...   0.424319  \n",
       "16  let begin first giving brief history may indul...   0.670703  \n",
       "17  novocure conducted first phase three randomize...   0.646736  \n",
       "18  sense various reason took oncogene attached bl...   0.640919  \n",
       "19  hey hey black eyed susie hey hey black eyed su...   0.455280  \n",
       "20  anyway excited inform allowed increasingly usi...   0.510752  \n",
       "21  lot let take closer look look back towards gal...   0.440435  \n",
       "22  molten salt reactor core heat exchanger hot sa...   0.560177  \n",
       "23  item recovered numerous mass graf across homel...   0.492238  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent_topics_sorteddf_mallet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(path.expanduser('~/work/data/data.txt'),\"w+\")\n",
    "for text in talk_pre['transcript']:\n",
    "    f.write(text+'\\n')\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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