23_Ejercicio4_Topic_Modeling_Noticias_BBC.ipynb

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    "# 23 - Ejercicio: Topic Modeling - Noticias BBC News\n",
    "\n",
    "\n",
    "* En el siguiente ejercicio tiene como objetivo aplicar los conocimientos adquiridos de ***Topic Modeling***, utilizando el LDA.\n",
    "\n",
    "\n",
    "* Para ello vamos a trabajar con unos ***artículos de noticias publicados por la BBC*** que los tienen categorizados en 5 temas (Topics)\n",
    "\n",
    "    - Business\n",
    "    - Entertainment\n",
    "    - Politics\n",
    "    - Sport\n",
    "    - Tech\n",
    "\n",
    "\n",
    "* El objetivo de este ejercicio es el de ***obtener un número de temas (Topics) adecuados según los artículos y ver las relaciones entre los temas (Topics) y las palabras (Terms) que caracterizan a los temas (Topics)***.\n",
    "\n",
    "\n",
    "* Pasos a realizar en el ejercicio:\n",
    "\n",
    "    1. Carga de datos\n",
    "    2. Normalizar los textos\n",
    "    3. Creacción del diccionario y la bolsa de palabras\n",
    "    4. Selección del número óptimo de Topics\n",
    "    5. Creacción del Modelo\n",
    "    6. Visualización"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Carga de Datos\n",
    "\n",
    "\n",
    "* El fichero en formato csv contiene una serie de noticias en los que le asigna una temática.\n",
    "\n",
    "\n",
    "* Cada elemento del Json contiene:\n",
    "    - **news**: Contenido del artículo\n",
    "    - **type**: Temática\n",
    "    \n",
    "    \n",
    "* Leemos los datos y mostramos una muestra:"
   ]
  },
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      "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>news</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>Uganda bans Vagina Monologues\\n \\n Uganda's au...</td>\n",
       "      <td>entertainment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>Howard backs stem cell research\\n \\n Michael H...</td>\n",
       "      <td>politics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>US adds more jobs than expected\\n \\n The US ec...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>Profits stall at China's Lenovo\\n \\n Profits a...</td>\n",
       "      <td>business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861</th>\n",
       "      <td>Firefox browser takes on Microsoft\\n \\n Micros...</td>\n",
       "      <td>tech</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   news           type\n",
       "621   Uganda bans Vagina Monologues\\n \\n Uganda's au...  entertainment\n",
       "1090  Howard backs stem cell research\\n \\n Michael H...       politics\n",
       "14    US adds more jobs than expected\\n \\n The US ec...       business\n",
       "283   Profits stall at China's Lenovo\\n \\n Profits a...       business\n",
       "1861  Firefox browser takes on Microsoft\\n \\n Micros...           tech"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "\n",
    "file = './data/BBC_News.csv'\n",
    "df = pd.read_csv(file, encoding=\"iso-8859-1\")\n",
    "documents = [str(x.encode('utf8')) for x in df.news.values] # Lista de documentos\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Vemos los 5 tipos de artículos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>news</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>business</th>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entertainment</th>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>politics</th>\n",
       "      <td>417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sport</th>\n",
       "      <td>511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tech</th>\n",
       "      <td>401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "               news\n",
       "type               \n",
       "business        510\n",
       "entertainment   386\n",
       "politics        417\n",
       "sport           511\n",
       "tech            401"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['type']).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Normalizar los textos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Creacción del diccionario y la bolsa de palabras\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Selección del número óptimo de Topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Creacción del Modelo\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "\n",
    "## Visualización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO"
   ]
  }
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