Part 0.3: N_gram.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_Basics/blob/main/Part%200.3%3A%20N_gram.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVqPtVMBOlJL"
      },
      "source": [
        "# **N-gram and its Applications**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "7fNUsA3rqiuI",
        "outputId": "a01491ce-357a-48e9-ec56-2e71323583b8"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Dataset will be stored in a Pandas Dataframe\n",
        "url= 'https://raw.githubusercontent.com/DataScience-in-Tourism/Chapter-15-Introduction-Natural-Language-Processing/main/tourism_tweets_cleaned.csv'\n",
        "df = pd.read_csv(url)\n",
        "\n",
        "# Check first five columns of the dataset\n",
        "df.head(5)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  id1                                                url  \\\n",
              "0           0    1  https://twitter.com/_LoveCaribbean/statuses/12...   \n",
              "1           1    2  https://twitter.com/carolcruising/statuses/125...   \n",
              "2           3    4  https://twitter.com/clairesturz/statuses/12529...   \n",
              "3           4    5  https://twitter.com/grenadaexplorer/statuses/1...   \n",
              "4           5    6  https://twitter.com/ikefuller/statuses/1252922...   \n",
              "\n",
              "                                                url2                 date  \\\n",
              "0  https://twitter.com/_LoveCaribbean/statuses/12...  2020-04-22 07:47:13   \n",
              "1  https://twitter.com/carolcruising/statuses/125...  2020-04-22 07:44:47   \n",
              "2  https://twitter.com/clairesturz/statuses/12529...  2020-04-22 07:32:39   \n",
              "3  https://twitter.com/grenadaexplorer/statuses/1...  2020-04-22 07:32:02   \n",
              "4  https://twitter.com/ikefuller/statuses/1252922...  2020-04-22 07:30:12   \n",
              "\n",
              "          username                                              quote  \\\n",
              "0   _LoveCaribbean  RT @grenadaexplorer: A tropical treasure to ch...   \n",
              "1    carolcruising  RT @clairesturz: Backpacking in #Cuba on a Bud...   \n",
              "2      clairesturz  Experience Buenos Aires like a local #travelso...   \n",
              "3  grenadaexplorer  Who can\\'t wait for Grenada Sailing Festival 2...   \n",
              "4        ikefuller  RT @clairesturz: The Best Hostel in Barcelona ...   \n",
              "\n",
              "                                               tweet                as1  \\\n",
              "0  tropical treasure cherish forever exclusive  t...    Twitter Web App   \n",
              "1  backpack  cuba budget  travelsomeday  armchair...    Twitter Web App   \n",
              "2  experience burns air like local  travelsomeday...  Revive Social App   \n",
              "3  cant wait grenade sail festival 2021   exclusi...             Buffer   \n",
              "4  best hostel barcelona  fabrizzio  s petit revi...    Twitter Web App   \n",
              "\n",
              "          as2  ... lang  coords  Unnamed: 18  punctuation hastags stopwords  \\\n",
              "0  1356684936  ...  NaN     NaN          NaN           21       0         4   \n",
              "1   319114596  ...  NaN     NaN          NaN            9       0         3   \n",
              "2   874023301  ...  NaN     NaN          NaN            9       0         1   \n",
              "3    17083471  ...  NaN     NaN          NaN           25       0         3   \n",
              "4   283127000  ...  NaN     NaN          NaN            8       0         2   \n",
              "\n",
              "  lng lng_score char_count  avg_word  \n",
              "0  en  0.999996        232      10.6  \n",
              "1  en  0.999996         67       9.5  \n",
              "2  en  0.999997         88       8.3  \n",
              "3  en  0.996475        227      10.3  \n",
              "4  en  0.857140         94       7.9  \n",
              "\n",
              "[5 rows x 46 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-13f6a255-1dc6-4e04-801e-b0bda9340f69\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Unnamed: 0</th>\n",
              "      <th>id1</th>\n",
              "      <th>url</th>\n",
              "      <th>url2</th>\n",
              "      <th>date</th>\n",
              "      <th>username</th>\n",
              "      <th>quote</th>\n",
              "      <th>tweet</th>\n",
              "      <th>as1</th>\n",
              "      <th>as2</th>\n",
              "      <th>...</th>\n",
              "      <th>lang</th>\n",
              "      <th>coords</th>\n",
              "      <th>Unnamed: 18</th>\n",
              "      <th>punctuation</th>\n",
              "      <th>hastags</th>\n",
              "      <th>stopwords</th>\n",
              "      <th>lng</th>\n",
              "      <th>lng_score</th>\n",
              "      <th>char_count</th>\n",
              "      <th>avg_word</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>https://twitter.com/_LoveCaribbean/statuses/12...</td>\n",
              "      <td>https://twitter.com/_LoveCaribbean/statuses/12...</td>\n",
              "      <td>2020-04-22 07:47:13</td>\n",
              "      <td>_LoveCaribbean</td>\n",
              "      <td>RT @grenadaexplorer: A tropical treasure to ch...</td>\n",
              "      <td>tropical treasure cherish forever exclusive  t...</td>\n",
              "      <td>Twitter Web App</td>\n",
              "      <td>1356684936</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>en</td>\n",
              "      <td>0.999996</td>\n",
              "      <td>232</td>\n",
              "      <td>10.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>https://twitter.com/carolcruising/statuses/125...</td>\n",
              "      <td>https://twitter.com/carolcruising/statuses/125...</td>\n",
              "      <td>2020-04-22 07:44:47</td>\n",
              "      <td>carolcruising</td>\n",
              "      <td>RT @clairesturz: Backpacking in #Cuba on a Bud...</td>\n",
              "      <td>backpack  cuba budget  travelsomeday  armchair...</td>\n",
              "      <td>Twitter Web App</td>\n",
              "      <td>319114596</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>en</td>\n",
              "      <td>0.999996</td>\n",
              "      <td>67</td>\n",
              "      <td>9.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>https://twitter.com/clairesturz/statuses/12529...</td>\n",
              "      <td>https://twitter.com/clairesturz/statuses/12529...</td>\n",
              "      <td>2020-04-22 07:32:39</td>\n",
              "      <td>clairesturz</td>\n",
              "      <td>Experience Buenos Aires like a local #travelso...</td>\n",
              "      <td>experience burns air like local  travelsomeday...</td>\n",
              "      <td>Revive Social App</td>\n",
              "      <td>874023301</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>en</td>\n",
              "      <td>0.999997</td>\n",
              "      <td>88</td>\n",
              "      <td>8.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>https://twitter.com/grenadaexplorer/statuses/1...</td>\n",
              "      <td>https://twitter.com/grenadaexplorer/statuses/1...</td>\n",
              "      <td>2020-04-22 07:32:02</td>\n",
              "      <td>grenadaexplorer</td>\n",
              "      <td>Who can\\'t wait for Grenada Sailing Festival 2...</td>\n",
              "      <td>cant wait grenade sail festival 2021   exclusi...</td>\n",
              "      <td>Buffer</td>\n",
              "      <td>17083471</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>en</td>\n",
              "      <td>0.996475</td>\n",
              "      <td>227</td>\n",
              "      <td>10.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>https://twitter.com/ikefuller/statuses/1252922...</td>\n",
              "      <td>https://twitter.com/ikefuller/statuses/1252922...</td>\n",
              "      <td>2020-04-22 07:30:12</td>\n",
              "      <td>ikefuller</td>\n",
              "      <td>RT @clairesturz: The Best Hostel in Barcelona ...</td>\n",
              "      <td>best hostel barcelona  fabrizzio  s petit revi...</td>\n",
              "      <td>Twitter Web App</td>\n",
              "      <td>283127000</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>en</td>\n",
              "      <td>0.857140</td>\n",
              "      <td>94</td>\n",
              "      <td>7.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 46 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13f6a255-1dc6-4e04-801e-b0bda9340f69')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-13f6a255-1dc6-4e04-801e-b0bda9340f69 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-13f6a255-1dc6-4e04-801e-b0bda9340f69');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JivDz-pjrPCV"
      },
      "source": [
        "#Let's only use tweet column as data\n",
        "# Redefine df\n",
        "df= df.tweet"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_b5lvg6ZrfNV",
        "outputId": "e9932284-0075-4659-b31c-5f9e5aa1816a"
      },
      "source": [
        "# Check first six reviews\n",
        "df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    tropical treasure cherish forever exclusive  t...\n",
              "1    backpack  cuba budget  travelsomeday  armchair...\n",
              "2    experience burns air like local  travelsomeday...\n",
              "3    cant wait grenade sail festival 2021   exclusi...\n",
              "4    best hostel barcelona  fabrizzio  s petit revi...\n",
              "Name: tweet, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GnVAfQTNUZY2",
        "outputId": "2b59a484-f9ac-4262-e0ce-23f0611e99a4"
      },
      "source": [
        "# Make sure the reviews are in string format.\n",
        "df = str(df)\n",
        "\n",
        "# Check the type of the reviews. If it's str, we are in the correct path\n",
        "type(df)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQAzslSOQjn_"
      },
      "source": [
        "Basic data cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LfUyEwKUHLT"
      },
      "source": [
        "# Make a small data cleaning just in case we have uncleaned data\n",
        "# Get rid of chararacters that can't be read\n",
        "import re\n",
        "df = re.sub(r'[^a-zA-Z0-9\\s]', ' ', df)\n",
        "pattern = r'[0-9]'\n",
        "\n",
        "# Match all digits in the string and replace them by empty string\n",
        "df  = re.sub(pattern, '', df)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjcarVVMfg8B"
      },
      "source": [
        "# In the dataset, we make have '\\r' and '\\n' strings\n",
        "# Let's remove them by replacing with an empty string\n",
        "df = df.replace('\\r', '').replace('\\n', '')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_rpv2ZU-QpVH"
      },
      "source": [
        "Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78elZexUriCR",
        "outputId": "44f7d23c-dd79-40c3-e704-fa190654b8a7"
      },
      "source": [
        "# Import required libraries for n-gram\n",
        "import nltk\n",
        "from nltk.util import ngrams\n",
        "import re\n",
        "\n",
        "# Tokenize the reviews in the dataset\n",
        "tokens = [token for token in df.split(\" \") if token != \"\" ]\n",
        "\n",
        "#Let's check tokens for the first column\n",
        "tokens[:20]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['tropical',\n",
              " 'treasure',\n",
              " 'cherish',\n",
              " 'forever',\n",
              " 'exclusive',\n",
              " 't',\n",
              " 'backpack',\n",
              " 'cuba',\n",
              " 'budget',\n",
              " 'travelsomeday',\n",
              " 'armchair',\n",
              " 'experience',\n",
              " 'burns',\n",
              " 'air',\n",
              " 'like',\n",
              " 'local',\n",
              " 'travelsomeday',\n",
              " 'cant',\n",
              " 'wait',\n",
              " 'grenade']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZaEe83GDRFV",
        "outputId": "dc524ffb-5bf3-43f7-e960-75ffc771a4da"
      },
      "source": [
        "list(ngrams(tokens, 5))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('tropical', 'treasure', 'cherish', 'forever', 'exclusive'),\n",
              " ('treasure', 'cherish', 'forever', 'exclusive', 't'),\n",
              " ('cherish', 'forever', 'exclusive', 't', 'backpack'),\n",
              " ('forever', 'exclusive', 't', 'backpack', 'cuba'),\n",
              " ('exclusive', 't', 'backpack', 'cuba', 'budget'),\n",
              " ('t', 'backpack', 'cuba', 'budget', 'travelsomeday'),\n",
              " ('backpack', 'cuba', 'budget', 'travelsomeday', 'armchair'),\n",
              " ('cuba', 'budget', 'travelsomeday', 'armchair', 'experience'),\n",
              " ('budget', 'travelsomeday', 'armchair', 'experience', 'burns'),\n",
              " ('travelsomeday', 'armchair', 'experience', 'burns', 'air'),\n",
              " ('armchair', 'experience', 'burns', 'air', 'like'),\n",
              " ('experience', 'burns', 'air', 'like', 'local'),\n",
              " ('burns', 'air', 'like', 'local', 'travelsomeday'),\n",
              " ('air', 'like', 'local', 'travelsomeday', 'cant'),\n",
              " ('like', 'local', 'travelsomeday', 'cant', 'wait'),\n",
              " ('local', 'travelsomeday', 'cant', 'wait', 'grenade'),\n",
              " ('travelsomeday', 'cant', 'wait', 'grenade', 'sail'),\n",
              " ('cant', 'wait', 'grenade', 'sail', 'festival'),\n",
              " ('wait', 'grenade', 'sail', 'festival', 'exclusi'),\n",
              " ('grenade', 'sail', 'festival', 'exclusi', 'best'),\n",
              " ('sail', 'festival', 'exclusi', 'best', 'hostel'),\n",
              " ('festival', 'exclusi', 'best', 'hostel', 'barcelona'),\n",
              " ('exclusi', 'best', 'hostel', 'barcelona', 'fabrizzio'),\n",
              " ('best', 'hostel', 'barcelona', 'fabrizzio', 's'),\n",
              " ('hostel', 'barcelona', 'fabrizzio', 's', 'petit'),\n",
              " ('barcelona', 'fabrizzio', 's', 'petit', 'revi'),\n",
              " ('fabrizzio', 's', 'petit', 'revi', 'lip'),\n",
              " ('s', 'petit', 'revi', 'lip', 'dinner'),\n",
              " ('petit', 'revi', 'lip', 'dinner', 'berlin'),\n",
              " ('revi', 'lip', 'dinner', 'berlin', 'to'),\n",
              " ('lip', 'dinner', 'berlin', 'to', 'tower'),\n",
              " ('dinner', 'berlin', 'to', 'tower', 'restaurant'),\n",
              " ('berlin', 'to', 'tower', 'restaurant', 'travel'),\n",
              " ('to', 'tower', 'restaurant', 'travel', 'plan'),\n",
              " ('tower', 'restaurant', 'travel', 'plan', 'overseas'),\n",
              " ('restaurant', 'travel', 'plan', 'overseas', 'trip'),\n",
              " ('travel', 'plan', 'overseas', 'trip', 'sign'),\n",
              " ('plan', 'overseas', 'trip', 'sign', 'access'),\n",
              " ('overseas', 'trip', 'sign', 'access', 'travel'),\n",
              " ('trip', 'sign', 'access', 'travel', 'resou'),\n",
              " ('sign', 'access', 'travel', 'resou', 'colombia'),\n",
              " ('access', 'travel', 'resou', 'colombia', 'solo'),\n",
              " ('travel', 'resou', 'colombia', 'solo', 'travel'),\n",
              " ('resou', 'colombia', 'solo', 'travel', 'travel'),\n",
              " ('colombia', 'solo', 'travel', 'travel', 'colombia'),\n",
              " ('solo', 'travel', 'travel', 'colombia', 'alone'),\n",
              " ('travel', 'travel', 'colombia', 'alone', 're'),\n",
              " ('travel', 'colombia', 'alone', 're', 'camp'),\n",
              " ('colombia', 'alone', 're', 'camp', 'alone'),\n",
              " ('alone', 're', 'camp', 'alone', 'safely'),\n",
              " ('re', 'camp', 'alone', 'safely', 'solo'),\n",
              " ('camp', 'alone', 'safely', 'solo', 'camp'),\n",
              " ('alone', 'safely', 'solo', 'camp', 'tip'),\n",
              " ('safely', 'solo', 'camp', 'tip', 'travelsom'),\n",
              " ('solo', 'camp', 'tip', 'travelsom', 'find'),\n",
              " ('camp', 'tip', 'travelsom', 'find', 'best'),\n",
              " ('tip', 'travelsom', 'find', 'best', 'flight'),\n",
              " ('travelsom', 'find', 'best', 'flight', 'deal'),\n",
              " ('find', 'best', 'flight', 'deal', 'black'),\n",
              " ('best', 'flight', 'deal', 'black', 'friday'),\n",
              " ('flight', 'deal', 'black', 'friday', 'year'),\n",
              " ('deal', 'black', 'friday', 'year', 'tr'),\n",
              " ('black', 'friday', 'year', 'tr', 'Name'),\n",
              " ('friday', 'year', 'tr', 'Name', 'tweet'),\n",
              " ('year', 'tr', 'Name', 'tweet', 'Length'),\n",
              " ('tr', 'Name', 'tweet', 'Length', 'dtype'),\n",
              " ('Name', 'tweet', 'Length', 'dtype', 'object')]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8ACaSs_QsiO"
      },
      "source": [
        "###  **Creating n-gram models with different numbers of n**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7PD9f_WDs877",
        "outputId": "6062a552-0d7a-4d55-8d7d-21a05311073e"
      },
      "source": [
        "# Ngram with n=3\n",
        "list(ngrams(tokens, 3))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('tropical', 'treasure', 'cherish'),\n",
              " ('treasure', 'cherish', 'forever'),\n",
              " ('cherish', 'forever', 'exclusive'),\n",
              " ('forever', 'exclusive', 't'),\n",
              " ('exclusive', 't', 'backpack'),\n",
              " ('t', 'backpack', 'cuba'),\n",
              " ('backpack', 'cuba', 'budget'),\n",
              " ('cuba', 'budget', 'travelsomeday'),\n",
              " ('budget', 'travelsomeday', 'armchair'),\n",
              " ('travelsomeday', 'armchair', 'experience'),\n",
              " ('armchair', 'experience', 'burns'),\n",
              " ('experience', 'burns', 'air'),\n",
              " ('burns', 'air', 'like'),\n",
              " ('air', 'like', 'local'),\n",
              " ('like', 'local', 'travelsomeday'),\n",
              " ('local', 'travelsomeday', 'cant'),\n",
              " ('travelsomeday', 'cant', 'wait'),\n",
              " ('cant', 'wait', 'grenade'),\n",
              " ('wait', 'grenade', 'sail'),\n",
              " ('grenade', 'sail', 'festival'),\n",
              " ('sail', 'festival', 'exclusi'),\n",
              " ('festival', 'exclusi', 'best'),\n",
              " ('exclusi', 'best', 'hostel'),\n",
              " ('best', 'hostel', 'barcelona'),\n",
              " ('hostel', 'barcelona', 'fabrizzio'),\n",
              " ('barcelona', 'fabrizzio', 's'),\n",
              " ('fabrizzio', 's', 'petit'),\n",
              " ('s', 'petit', 'revi'),\n",
              " ('petit', 'revi', 'lip'),\n",
              " ('revi', 'lip', 'dinner'),\n",
              " ('lip', 'dinner', 'berlin'),\n",
              " ('dinner', 'berlin', 'to'),\n",
              " ('berlin', 'to', 'tower'),\n",
              " ('to', 'tower', 'restaurant'),\n",
              " ('tower', 'restaurant', 'travel'),\n",
              " ('restaurant', 'travel', 'plan'),\n",
              " ('travel', 'plan', 'overseas'),\n",
              " ('plan', 'overseas', 'trip'),\n",
              " ('overseas', 'trip', 'sign'),\n",
              " ('trip', 'sign', 'access'),\n",
              " ('sign', 'access', 'travel'),\n",
              " ('access', 'travel', 'resou'),\n",
              " ('travel', 'resou', 'colombia'),\n",
              " ('resou', 'colombia', 'solo'),\n",
              " ('colombia', 'solo', 'travel'),\n",
              " ('solo', 'travel', 'travel'),\n",
              " ('travel', 'travel', 'colombia'),\n",
              " ('travel', 'colombia', 'alone'),\n",
              " ('colombia', 'alone', 're'),\n",
              " ('alone', 're', 'camp'),\n",
              " ('re', 'camp', 'alone'),\n",
              " ('camp', 'alone', 'safely'),\n",
              " ('alone', 'safely', 'solo'),\n",
              " ('safely', 'solo', 'camp'),\n",
              " ('solo', 'camp', 'tip'),\n",
              " ('camp', 'tip', 'travelsom'),\n",
              " ('tip', 'travelsom', 'find'),\n",
              " ('travelsom', 'find', 'best'),\n",
              " ('find', 'best', 'flight'),\n",
              " ('best', 'flight', 'deal'),\n",
              " ('flight', 'deal', 'black'),\n",
              " ('deal', 'black', 'friday'),\n",
              " ('black', 'friday', 'year'),\n",
              " ('friday', 'year', 'tr'),\n",
              " ('year', 'tr', 'Name'),\n",
              " ('tr', 'Name', 'tweet'),\n",
              " ('Name', 'tweet', 'Length'),\n",
              " ('tweet', 'Length', 'dtype'),\n",
              " ('Length', 'dtype', 'object')]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_q7wcCaOtK1o",
        "outputId": "bb79ced9-8ded-4b49-f945-4734233332dd"
      },
      "source": [
        "# Ngram with n=4\n",
        "list(ngrams(tokens, 4))[:4]"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('tropical', 'treasure', 'cherish', 'forever'),\n",
              " ('treasure', 'cherish', 'forever', 'exclusive'),\n",
              " ('cherish', 'forever', 'exclusive', 't'),\n",
              " ('forever', 'exclusive', 't', 'backpack')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "nDZNAGobue5_",
        "outputId": "2bbebb57-5041-4c8b-b050-6eb11bfd836e"
      },
      "source": [
        "# Checking the n-grams with n=3 with random 10 samples\n",
        "pd.DataFrame(ngrams(tokens, 3)).sample(10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                0          1           2\n",
              "27              s      petit        revi\n",
              "39           trip       sign      access\n",
              "3         forever  exclusive           t\n",
              "56            tip  travelsom        find\n",
              "9   travelsomeday   armchair  experience\n",
              "53         safely       solo        camp\n",
              "60         flight       deal       black\n",
              "61           deal      black      friday\n",
              "66           Name      tweet      Length\n",
              "11     experience      burns         air"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a82c4de3-7508-4940-8cae-340a267be61f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>s</td>\n",
              "      <td>petit</td>\n",
              "      <td>revi</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>trip</td>\n",
              "      <td>sign</td>\n",
              "      <td>access</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>forever</td>\n",
              "      <td>exclusive</td>\n",
              "      <td>t</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>tip</td>\n",
              "      <td>travelsom</td>\n",
              "      <td>find</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>travelsomeday</td>\n",
              "      <td>armchair</td>\n",
              "      <td>experience</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>safely</td>\n",
              "      <td>solo</td>\n",
              "      <td>camp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>flight</td>\n",
              "      <td>deal</td>\n",
              "      <td>black</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>deal</td>\n",
              "      <td>black</td>\n",
              "      <td>friday</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Name</td>\n",
              "      <td>tweet</td>\n",
              "      <td>Length</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>experience</td>\n",
              "      <td>burns</td>\n",
              "      <td>air</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a82c4de3-7508-4940-8cae-340a267be61f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a82c4de3-7508-4940-8cae-340a267be61f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a82c4de3-7508-4940-8cae-340a267be61f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X62cMjGZcH4l",
        "outputId": "d5ed5f88-9ae3-4875-9dfe-1154f6d03e43"
      },
      "source": [
        "(pd.Series(nltk.ngrams(tokens, 2)).value_counts())[:10]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tropical, treasure)    1\n",
              "(colombia, solo)        1\n",
              "(re, camp)              1\n",
              "(alone, re)             1\n",
              "(colombia, alone)       1\n",
              "(travel, colombia)      1\n",
              "(travel, travel)        1\n",
              "(solo, travel)          1\n",
              "(resou, colombia)       1\n",
              "(alone, safely)         1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j8ppYxftppjP",
        "outputId": "5df55dcf-aea6-452d-c287-59ee64d7f47b"
      },
      "source": [
        "import collections\n",
        "bigram_counts = collections.Counter(pd.Series(nltk.ngrams(tokens, 2)))\n",
        "bigram_counts.most_common(20)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(('tropical', 'treasure'), 1),\n",
              " (('treasure', 'cherish'), 1),\n",
              " (('cherish', 'forever'), 1),\n",
              " (('forever', 'exclusive'), 1),\n",
              " (('exclusive', 't'), 1),\n",
              " (('t', 'backpack'), 1),\n",
              " (('backpack', 'cuba'), 1),\n",
              " (('cuba', 'budget'), 1),\n",
              " (('budget', 'travelsomeday'), 1),\n",
              " (('travelsomeday', 'armchair'), 1),\n",
              " (('armchair', 'experience'), 1),\n",
              " (('experience', 'burns'), 1),\n",
              " (('burns', 'air'), 1),\n",
              " (('air', 'like'), 1),\n",
              " (('like', 'local'), 1),\n",
              " (('local', 'travelsomeday'), 1),\n",
              " (('travelsomeday', 'cant'), 1),\n",
              " (('cant', 'wait'), 1),\n",
              " (('wait', 'grenade'), 1),\n",
              " (('grenade', 'sail'), 1)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jApK6JCgRLHG"
      },
      "source": [
        "### **Check the number of tokens in bigram model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "rcC2biYplY2B",
        "outputId": "003ff881-43ff-45f5-a980-e1d3a2fb5627"
      },
      "source": [
        "bigram_df = pd.DataFrame(bigram_counts.most_common(20),\n",
        "                             columns=['bigram', 'count'])\n",
        "\n",
        "bigram_df"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       bigram  count\n",
              "0        (tropical, treasure)      1\n",
              "1         (treasure, cherish)      1\n",
              "2          (cherish, forever)      1\n",
              "3        (forever, exclusive)      1\n",
              "4              (exclusive, t)      1\n",
              "5               (t, backpack)      1\n",
              "6            (backpack, cuba)      1\n",
              "7              (cuba, budget)      1\n",
              "8     (budget, travelsomeday)      1\n",
              "9   (travelsomeday, armchair)      1\n",
              "10     (armchair, experience)      1\n",
              "11        (experience, burns)      1\n",
              "12               (burns, air)      1\n",
              "13                (air, like)      1\n",
              "14              (like, local)      1\n",
              "15     (local, travelsomeday)      1\n",
              "16      (travelsomeday, cant)      1\n",
              "17               (cant, wait)      1\n",
              "18            (wait, grenade)      1\n",
              "19            (grenade, sail)      1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e28354c3-888d-42f4-8cae-e0869b80ce23\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>bigram</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(tropical, treasure)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(treasure, cherish)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(cherish, forever)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(forever, exclusive)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(exclusive, t)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(t, backpack)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(backpack, cuba)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(cuba, budget)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(budget, travelsomeday)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(travelsomeday, armchair)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>(armchair, experience)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>(experience, burns)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(burns, air)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>(air, like)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(like, local)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>(local, travelsomeday)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(travelsomeday, cant)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>(cant, wait)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(wait, grenade)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>(grenade, sail)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e28354c3-888d-42f4-8cae-e0869b80ce23')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e28354c3-888d-42f4-8cae-e0869b80ce23 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e28354c3-888d-42f4-8cae-e0869b80ce23');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-pS57AnVoniO",
        "outputId": "443038d6-9bdf-4ad6-9012-1f8d1ca4b42d"
      },
      "source": [
        "pd.Series(nltk.ngrams(tokens, 2)).value_counts()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tropical, treasure)    1\n",
              "(colombia, solo)        1\n",
              "(re, camp)              1\n",
              "(alone, re)             1\n",
              "(colombia, alone)       1\n",
              "                       ..\n",
              "(revi, lip)             1\n",
              "(lip, dinner)           1\n",
              "(dinner, berlin)        1\n",
              "(berlin, to)            1\n",
              "(dtype, object)         1\n",
              "Length: 70, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pqce9siRq2da"
      },
      "source": [
        "Data Visualization for Bigram Networks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msn0y7g-pMWf"
      },
      "source": [
        "# Create dictionary of bigrams and their counts\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import networkx as nx\n",
        "\n",
        "sns.set(font_scale=1.5)\n",
        "sns.set_style(\"whitegrid\")\n",
        "\n",
        "d = bigram_df.set_index('bigram').T.to_dict('records')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbO6UWuzqQkp"
      },
      "source": [
        "# Create network plot \n",
        "G = nx.Graph()\n",
        "\n",
        "# Create connections between nodes\n",
        "for k, v in d[0].items():\n",
        "    G.add_edge(k[0], k[1], weight=(v * 10))\n",
        "\n",
        "G.add_node(\"china\", weight=100)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "3h4FkedEqsJv",
        "outputId": "b594311a-5c55-4362-9f44-dc2d5d122275"
      },
      "source": [
        "sns.set_style(\"darkgrid\")\n",
        "fig, ax = plt.subplots(figsize=(9, 8))\n",
        "\n",
        "pos = nx.spring_layout(G, k=2)\n",
        "\n",
        "# Plot networks\n",
        "nx.draw_networkx(G, pos,\n",
        "                 font_size=16,\n",
        "                 width=3,\n",
        "                 edge_color='lightsalmon',\n",
        "                 node_color='lightblue',\n",
        "                 with_labels = False,\n",
        "                 ax=ax)\n",
        "\n",
        "# Create offset labels\n",
        "for key, value in pos.items():\n",
        "    x, y = value[0]+.135, value[1]+.045\n",
        "    ax.text(x, y,\n",
        "            s=key,\n",
        "            bbox=dict(facecolor='indigo', alpha=0.25),\n",
        "            horizontalalignment='center', fontsize=13)\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}