She_Ra_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "She-Ra Project.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyNT5sP3c+fyahn/i1Pyw9i7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prishanmu/She-Ra/blob/master/She_Ra_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kny5qFLWTUbS",
        "colab_type": "text"
      },
      "source": [
        "# A Look at the Princesses of Power\n",
        "\n",
        "GitHub Repo: https://github.com/prishanmu/She-Ra\n",
        "\n",
        "**Research Questions**: \n",
        "\n",
        "\n",
        "*   Who are the main or most active characters of this show? Does this change by season? \n",
        "*   What are the most common words used by characters in this show?\n",
        "* What are the most \"important\" words spoken by certain characters on this show? \n",
        "* Data Modeling: Can we predict which lines were spoken by Adora? or Catra? \n",
        "* What are some relationships among words on this show? \n",
        "* Sentiment Analysis by Character (and eventually season)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcHOubwoDvmT",
        "colab_type": "text"
      },
      "source": [
        "# Import Statements & Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMO2LMeSrX2f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "twrh80QP-gjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import re\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator \n",
        "from collections import Counter\n",
        "import nltk\n",
        "import string\n",
        "import itertools\n",
        "import collections\n",
        "from os import path\n",
        "from PIL import Image"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyMaNGCiJJpX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "outputId": "a17c2a64-6b56-4ccc-ff24-6b1442d39991"
      },
      "source": [
        "df = pd.read_csv(\"she_ra_dialogue.csv\")\n",
        "df = df[df.dialogue!=' ']\n",
        "df.dialogue.astype('str')\n",
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>character</th>\n",
              "      <th>dialogue</th>\n",
              "      <th>season</th>\n",
              "      <th>episode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adora</td>\n",
              "      <td>. Hey, princess. You looking at me?</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Woman over PA</td>\n",
              "      <td>All squadrons, report to training area immedi...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Adora</td>\n",
              "      <td>Psst. Anyone seen Catra?</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Adora</td>\n",
              "      <td>Not again.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Unknown Blue Horde Member</td>\n",
              "      <td>At attention, cadets. Your simulation is abou...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   character  ... episode\n",
              "0                      Adora  ...       1\n",
              "1              Woman over PA  ...       1\n",
              "2                      Adora  ...       1\n",
              "4                      Adora  ...       1\n",
              "5  Unknown Blue Horde Member  ...       1\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H0ZH2Rmwk8iG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# to-do: clean up df's character column - catch any spelling mistakes"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oISrAeE7EGUW",
        "colab_type": "text"
      },
      "source": [
        "# Who are the main or most active characters of this show? Does this change by season?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8DphYViESzU",
        "colab_type": "text"
      },
      "source": [
        "Let's look at the characters with the highest number of lines: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NlWwF3BiGX1h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "outputId": "b605df26-6f08-41d6-d2bc-f6b2a6287a08"
      },
      "source": [
        "sns.set(style=\"darkgrid\")\n",
        "\n",
        "s = df.character.value_counts()[:10]\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "s.plot(kind=\"bar\")\n",
        "plt.show()\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqEr2gOP464A",
        "colab_type": "text"
      },
      "source": [
        "Since most of the episodes are transcribed for season 1, how about the main characters of just season 1? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWjiTcHc5BMx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "outputId": "ef2876db-21b5-4ff4-8cba-2d98c0600436"
      },
      "source": [
        "sns.set(style=\"darkgrid\")\n",
        "\n",
        "s1 = df[df.season ==1].character.value_counts()[:15] #top 15\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "s1.plot(kind=\"bar\")\n",
        "plt.show()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DEsJNhoKLk0d",
        "colab_type": "text"
      },
      "source": [
        "Unsurprisingly, Adora, aka She-Ra, has the most lines in the show and is the main character. Glimmer, Bow, and Catra, some of the people closest to Adora, are also top contenders for most lines in the show. \n",
        "\n",
        "Let's see how this might differ depending on season. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fh1WyHAJGwXZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "bf6c6e7f-b745-42c8-db3a-3db9d51c11c2"
      },
      "source": [
        "sns.set(style=\"darkgrid\", palette = 'husl')\n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "\n",
        "ax = sns.countplot(x=\"character\", data=df, hue='season', order=df.character.value_counts().iloc[:10].index)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sF-G7DpkZmB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# add line graph across seasons with each line being a character, top 10 characters?"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YoGyrkWLPQqy",
        "colab_type": "text"
      },
      "source": [
        "**add conclusion here once more seasons are added**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ld5jz2EKWXNe",
        "colab_type": "text"
      },
      "source": [
        "# What are the most common words used by characters in this show?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LjnVq32XgRe",
        "colab_type": "text"
      },
      "source": [
        "First, lets look at a wordcloud of the most common words on this show: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zALegO2U7r8l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# set up wordcloud\n",
        "stopwords = set(STOPWORDS)\n",
        "\n",
        "#x, y = np.ogrid[:300, :300]\n",
        "#mask = (x - 150) ** 2 + (y - 150) ** 2 > 130 ** 2 #change mask to heart\n",
        "#mask = 255 * mask.astype(int)\n",
        "\n",
        "mask = np.array(Image.open(path.join( \"heart_mask.jpg\")))\n",
        "\n",
        "wc = WordCloud(background_color=\"white\", max_words=1000, stopwords=stopwords, mask = mask)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNDDHZOEZVUP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "2f45d892-9993-4bdb-a53c-b91a0515916d"
      },
      "source": [
        "# show wordcloud of most common words in all dialogue\n",
        "all_text = str(df.dialogue.values)\n",
        "all_text = all_text.lower()\n",
        "\n",
        "image_colors = ImageColorGenerator(mask)\n",
        "\n",
        "wordcloud = wc.generate(all_text)\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "plt.imshow(wordcloud)\n",
        "plt.imshow(wc.recolor(color_func=image_colors), interpolation=\"bilinear\")\n",
        "plt.axis(\"off\")\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHqteSjGbRcY",
        "colab_type": "text"
      },
      "source": [
        "Now, let's look at the most common word spoken by the 20 most prominent characters. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzmeS2aeMYXa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "c35724a5-ed2a-40ef-fc92-fd91e19bb721"
      },
      "source": [
        "from nltk.corpus import stopwords\n",
        "nltk.download('stopwords')\n",
        "\n",
        "stopwords_nltk = list(stopwords.words('english'))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wawFEUhmL4U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "punctuations = string.punctuation + ''"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVwKqngsiajP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# word counter function\n",
        "\n",
        "def ct(text):\n",
        "  new_text=[]\n",
        "  for words in text:\n",
        "    words = words.split()\n",
        "    for word in words:\n",
        "      word = str(word)\n",
        "      word = word.lower()\n",
        "      if word not in stopwords_nltk:\n",
        "        new_word=''\n",
        "        for char in word:\n",
        "          if char not in punctuations:\n",
        "            new_word = new_word + char\n",
        "        new_text.append(new_word)\n",
        "        if '' in new_text: \n",
        "          new_text.remove('')\n",
        "    \n",
        "  total = len(new_text)\n",
        "  c = Counter(new_text)\n",
        "  word = c.most_common(1)[0][0]\n",
        "  word_count = c.most_common(1)[0][1]\n",
        "  return word, word_count, total, c, new_text"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plmCf0Y-gMRt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "top_20_characters = list(df.character.value_counts().iloc[:20].index)\n",
        "\n",
        "most_common_word = []\n",
        "word_count = []\n",
        "word_proportion = []\n",
        "\n",
        "for ch in top_20_characters: \n",
        "  s = df[df.character == ch]\n",
        "  text = list(s.dialogue.values)\n",
        "  word, count, total, c, text = ct(text)\n",
        "  most_common_word.append(word)\n",
        "  word_count.append(count)\n",
        "  proportion = str(float(count) / float(total) * 100) + '%'\n",
        "  word_proportion.append(proportion)\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ilrGNZCnOTU",
        "colab_type": "text"
      },
      "source": [
        "Below is a table of the most common word spoken by the 20 characters with the most lines: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ytzX67kcaIo-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "outputId": "d57719dd-edb9-41c8-c833-4390bc4ae37b"
      },
      "source": [
        "most_common_word_df = pd.DataFrame(data = {'character':top_20_characters, 'most common word':most_common_word, 'number of instances': word_count, 'proportion of dialogue': word_proportion})\n",
        "print(\"Most Common Word Spoken By the 20 Most Prominent Characters\")\n",
        "most_common_word_df"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Most Common Word Spoken By the 20 Most Prominent Characters\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>character</th>\n",
              "      <th>most common word</th>\n",
              "      <th>number of instances</th>\n",
              "      <th>proportion of dialogue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Adora</td>\n",
              "      <td>im</td>\n",
              "      <td>90</td>\n",
              "      <td>1.6863406408094435%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Glimmer</td>\n",
              "      <td>us</td>\n",
              "      <td>57</td>\n",
              "      <td>1.3070396698005045%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bow</td>\n",
              "      <td>glimmer</td>\n",
              "      <td>73</td>\n",
              "      <td>2.154663518299882%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Catra</td>\n",
              "      <td>adora</td>\n",
              "      <td>68</td>\n",
              "      <td>2.1376925495127317%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Scorpia</td>\n",
              "      <td>catra</td>\n",
              "      <td>40</td>\n",
              "      <td>2.0294266869609334%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Entrapta</td>\n",
              "      <td>it</td>\n",
              "      <td>31</td>\n",
              "      <td>1.7386427369601793%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Sea Hawk</td>\n",
              "      <td>im</td>\n",
              "      <td>18</td>\n",
              "      <td>1.6201620162016201%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>She-Ra</td>\n",
              "      <td>no</td>\n",
              "      <td>19</td>\n",
              "      <td>2.490170380078637%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Mermista</td>\n",
              "      <td>im</td>\n",
              "      <td>12</td>\n",
              "      <td>1.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Shadow Weaver</td>\n",
              "      <td>adora</td>\n",
              "      <td>33</td>\n",
              "      <td>3.606557377049181%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Swift Wind</td>\n",
              "      <td>im</td>\n",
              "      <td>19</td>\n",
              "      <td>2.6206896551724137%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Perfuma</td>\n",
              "      <td>shera</td>\n",
              "      <td>13</td>\n",
              "      <td>2.380952380952381%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Angella</td>\n",
              "      <td>glimmer</td>\n",
              "      <td>25</td>\n",
              "      <td>5.05050505050505%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Frosta</td>\n",
              "      <td>you</td>\n",
              "      <td>7</td>\n",
              "      <td>1.9498607242339834%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Hordak</td>\n",
              "      <td>force</td>\n",
              "      <td>8</td>\n",
              "      <td>1.7937219730941705%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Lonnie</td>\n",
              "      <td>got</td>\n",
              "      <td>7</td>\n",
              "      <td>2.380952380952381%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Light Hope</td>\n",
              "      <td>adora</td>\n",
              "      <td>10</td>\n",
              "      <td>2.6178010471204187%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Razz</td>\n",
              "      <td>dearie</td>\n",
              "      <td>13</td>\n",
              "      <td>3.7900874635568513%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Castaspella</td>\n",
              "      <td>glimmer</td>\n",
              "      <td>18</td>\n",
              "      <td>3.6885245901639343%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Micah</td>\n",
              "      <td>me</td>\n",
              "      <td>7</td>\n",
              "      <td>2.1806853582554515%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        character most common word  number of instances proportion of dialogue\n",
              "0           Adora               im                   90    1.6863406408094435%\n",
              "1         Glimmer               us                   57    1.3070396698005045%\n",
              "2             Bow          glimmer                   73     2.154663518299882%\n",
              "3           Catra            adora                   68    2.1376925495127317%\n",
              "4         Scorpia            catra                   40    2.0294266869609334%\n",
              "5        Entrapta               it                   31    1.7386427369601793%\n",
              "6        Sea Hawk               im                   18    1.6201620162016201%\n",
              "7          She-Ra               no                   19     2.490170380078637%\n",
              "8        Mermista               im                   12                   1.6%\n",
              "9   Shadow Weaver            adora                   33     3.606557377049181%\n",
              "10     Swift Wind               im                   19    2.6206896551724137%\n",
              "11        Perfuma            shera                   13     2.380952380952381%\n",
              "12        Angella          glimmer                   25      5.05050505050505%\n",
              "13         Frosta              you                    7    1.9498607242339834%\n",
              "14         Hordak            force                    8    1.7937219730941705%\n",
              "15         Lonnie              got                    7     2.380952380952381%\n",
              "16     Light Hope            adora                   10    2.6178010471204187%\n",
              "17           Razz           dearie                   13    3.7900874635568513%\n",
              "18    Castaspella          glimmer                   18    3.6885245901639343%\n",
              "19          Micah               me                    7    2.1806853582554515%"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3trOxXMnaij",
        "colab_type": "text"
      },
      "source": [
        "Most character tend to refer to another character or themselves - showing who is their main focus and priority throughout the show. Some of these characters require the context of the show. For example, Entrapta's most commmon word is \"it,\" which seems uninformative. However, she has a strong affinity to robots and reguarly talks about them, so there is no doubt that her robits are the subject of most of her lines. \n",
        "\n",
        "Interestingly, Adora's most used word is \"know\" and when she is She-Ra, \"no.\" To me, this might be indicative of her goal to seek knowledge - about her own past, about how to save Etheria - but also how she convinces others to have hope, as that is her main role. When she is She-Ra, her role is so defeat villians, and the word \"no\" perfectly summarizes just that. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5PaNwrEpKxU",
        "colab_type": "text"
      },
      "source": [
        "# What are the most \"important\" words spoken by characters on this show? \n",
        "\n",
        "For this, I will be utilizing TF-IDF, as this will filter out the words that are used so often they don't add much meaning to the text analysis. This method will also filter out the words that are used so little they are insignificant. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whKDqCsjjXrn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text = []\n",
        "for line in list(df.dialogue.values): \n",
        "  line = str(line)\n",
        "  line = line.split()\n",
        "  new_line = ''\n",
        "  for word in line: \n",
        "    word = str(word)\n",
        "    word = word.lower()\n",
        "    if word not in stopwords_nltk:\n",
        "      new_word=''\n",
        "      for char in word:\n",
        "        if char not in punctuations:\n",
        "          new_word = new_word + char\n",
        "      new_line = new_line + \" \" + new_word\n",
        "  text.append(new_line)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mnVsG7kdXnK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "9459fb78-f8f0-4f1a-98f9-50c66d79ff5a"
      },
      "source": [
        "print(text[0:10])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['  hey princess looking me', ' squadrons report training area immediately evaluation', ' psst anyone seen catra', ' again', ' attention cadets simulation begin heres scenario passing treacherous whispering woods reach heart rebel insurgency bright moon', ' mission defeat queen princesses liberate bright moon lord hordaks name wheres catra', ' here promise', ' mmhmm whispering woods full princesses vicious violent instigatorsthey take given chance give them good luck recruits', ' watch out', ' oh no no no no no']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEx5xJMAunpz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# to-do: parameter tune later\n",
        "\n",
        "cv = CountVectorizer(strip_accents='ascii',\n",
        "                     lowercase=True,\n",
        "                     tokenizer=None,\n",
        "                     stop_words='english', #enlgish stop words dictionary\n",
        "                     ngram_range=(1, 1), #broken down into one word units, you can change to 1+2 word units, etc\n",
        "                     max_df=1.0,\n",
        "                     min_df=0,\n",
        "                     max_features=None,\n",
        "                     vocabulary=None)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gW3207oiunoJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv_text = cv.fit_transform(text)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIaUwvO1unld",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 777
        },
        "outputId": "91d8b309-4a64-4cb7-b638-29464d170c65"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "\n",
        "tfidf = TfidfTransformer(norm='l2', use_idf=True, smooth_idf=False, sublinear_tf=False).fit(cv_text)\n",
        "\n",
        "tfidf_df = pd.DataFrame(tfidf.idf_, index=cv.get_feature_names(),columns=[\"weights\"])\n",
        "print(\"top 25 most important words\")\n",
        "tfidf_df.sort_values(by=['weights']).head(25)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "top 25 most important words\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>weights</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>im</th>\n",
              "      <td>3.612511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>know</th>\n",
              "      <td>3.724580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>adora</th>\n",
              "      <td>3.815094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>glimmer</th>\n",
              "      <td>4.029503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>like</th>\n",
              "      <td>4.060411</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>okay</th>\n",
              "      <td>4.106291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>horde</th>\n",
              "      <td>4.174278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oh</th>\n",
              "      <td>4.189468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>right</th>\n",
              "      <td>4.274626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>catra</th>\n",
              "      <td>4.291433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>need</th>\n",
              "      <td>4.302797</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>going</th>\n",
              "      <td>4.314291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>come</th>\n",
              "      <td>4.343619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>think</th>\n",
              "      <td>4.456947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time</th>\n",
              "      <td>4.504738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shera</th>\n",
              "      <td>4.607770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bow</th>\n",
              "      <td>4.615552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thats</th>\n",
              "      <td>4.680091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>got</th>\n",
              "      <td>4.705409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>really</th>\n",
              "      <td>4.722651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hey</th>\n",
              "      <td>4.758052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>uh</th>\n",
              "      <td>4.767102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>princess</th>\n",
              "      <td>4.823192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ive</th>\n",
              "      <td>4.823192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stop</th>\n",
              "      <td>4.862413</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           weights\n",
              "im        3.612511\n",
              "know      3.724580\n",
              "adora     3.815094\n",
              "glimmer   4.029503\n",
              "like      4.060411\n",
              "okay      4.106291\n",
              "horde     4.174278\n",
              "oh        4.189468\n",
              "right     4.274626\n",
              "catra     4.291433\n",
              "need      4.302797\n",
              "going     4.314291\n",
              "come      4.343619\n",
              "think     4.456947\n",
              "time      4.504738\n",
              "shera     4.607770\n",
              "bow       4.615552\n",
              "thats     4.680091\n",
              "got       4.705409\n",
              "really    4.722651\n",
              "hey       4.758052\n",
              "uh        4.767102\n",
              "princess  4.823192\n",
              "ive       4.823192\n",
              "stop      4.862413"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iojUnoUlY2X4",
        "colab_type": "text"
      },
      "source": [
        "## Part of Speech Tagging\n",
        "\n",
        "Coming Soon."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gvzB_mOZ0b-",
        "colab_type": "text"
      },
      "source": [
        "# Data Modeling: Data Modeling: Can we predict which lines were spoken by Adora? or Catra? \n",
        "\n",
        "Since the show revolves around these two characters' relationship, I decided to make a model to predict each characters' lines. Let's see how well it works!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YPwOIichhgH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# more import statements\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5U8HLJP8lZoz",
        "colab_type": "text"
      },
      "source": [
        "## Predicting Adora's lines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OYyGUwQxZ9ib",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df['dialogue'],\n",
        "                                                    df['character'], test_size=0.33, random_state=42)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikAsjxwDnllm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv_train = cv.fit_transform(X_train)\n",
        "tfidf = TfidfTransformer(norm='l2', use_idf=True, smooth_idf=False, sublinear_tf=False).fit(cv_train)\n",
        "X_train_processed = tfidf.transform(cv_train)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPtlm8S9oMIv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv_test = cv.transform(X_test)\n",
        "X_test_processed = tfidf.transform(cv_test)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6YBjRAYpGJK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "2aff1b60-2d58-45bf-cbe1-ceb142414677"
      },
      "source": [
        "# split classes into Adora = 1, Everyone else = 0\n",
        "new_y_train = y_train.copy()\n",
        "y_train = []\n",
        "for line in list(new_y_train.values): \n",
        "  if line == 'Adora': \n",
        "    y_train.append(1)\n",
        "  else: \n",
        "    y_train.append(0)\n",
        "\n",
        "new_y_test = y_test.copy()\n",
        "y_test = []\n",
        "for line in list(new_y_test.values): \n",
        "  if line == 'Adora': \n",
        "    y_test.append(1)\n",
        "  else: \n",
        "    y_test.append(0)\n",
        "print(y_train[0:10])\n",
        "print(y_test[0:10])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 0, 0, 1, 0, 1, 0, 1, 0]\n",
            "[0, 0, 1, 1, 0, 0, 0, 0, 0, 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvGeZDQipYr3",
        "colab_type": "text"
      },
      "source": [
        "### Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "N-jyDfjwmr58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "5cd52070-916a-4b64-8730-ce587e3ef57f"
      },
      "source": [
        "nb = MultinomialNB()\n",
        "\n",
        "param_grid = { \n",
        "    'alpha': [0, .5, 1],\n",
        "    'fit_prior':[True, False], \n",
        "}\n",
        "\n",
        "CV_nb = GridSearchCV(estimator=nb, param_grid=param_grid, cv= 5)\n",
        "CV_nb.fit(X_train_processed, y_train)\n",
        "print(CV_nb.best_params_)\n",
        "print(CV_nb.best_score_)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'alpha': 1, 'fit_prior': True}\n",
            "0.8150943396226416\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NkmDP_gemr6A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "4a1bd956-b16a-4bc2-9e00-8a1e4c927afc"
      },
      "source": [
        "nb = MultinomialNB(alpha=1)\n",
        "\n",
        "nb.fit(X_train_processed, y_train)\n",
        "y_pred = nb.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 80.22%\n",
            "\n",
            "F1 Score: 8.82\n",
            "\n",
            "Precision: 83.33%\n",
            "\n",
            "Recall: 4.66%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1242    3]\n",
            " [ 307   15]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hb-fpxZspgPM",
        "colab_type": "text"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZnJmSo9pw9b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5b258ddc-5e73-4ac4-e962-4adbcf6161de"
      },
      "source": [
        "lg = LogisticRegression() \n",
        "\n",
        "param_grid = { \n",
        "    'C': [1,10,100, 200],\n",
        "    \"penalty\":[\"l1\",\"l2\"]\n",
        "}\n",
        "\n",
        "CV_lg = GridSearchCV(estimator=lg, param_grid=param_grid, cv= 5)\n",
        "CV_lg.fit(X_train_processed, y_train)\n",
        "print(CV_lg.best_params_)\n",
        "print(CV_lg.best_score_)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 1, 'penalty': 'l2'}\n",
            "0.8188679245283019\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tJ3i4XZxkKpL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "20dc4822-1664-404b-b0d4-bbf5dd87a91f"
      },
      "source": [
        "lg = LogisticRegression(C=1, penalty='l2')\n",
        "\n",
        "lg.fit(X_train_processed, y_train)\n",
        "y_pred = lg.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 80.66%\n",
            "\n",
            "F1 Score: 13.18\n",
            "\n",
            "Precision: 85.19%\n",
            "\n",
            "Recall: 7.14%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1241    4]\n",
            " [ 299   23]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fuXb4321pwWB",
        "colab_type": "text"
      },
      "source": [
        "### SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X85qMeqGpwCE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "84367337-a218-4d1b-f958-9ea5e681bcbd"
      },
      "source": [
        "lsvc = LinearSVC()\n",
        "\n",
        "param_grid = {\n",
        "    'penalty': ['l2'],\n",
        "    'C': [.01,.1, 1],\n",
        "    'loss' : [\"squared_hinge\", \"hinge\"]\n",
        "}\n",
        "\n",
        "CV_lsvc = GridSearchCV(estimator=lsvc, param_grid=param_grid, cv = 5)\n",
        "CV_lsvc.fit(X_train_processed, y_train)\n",
        "\n",
        "print(CV_lsvc.best_params_)\n",
        "print(CV_lsvc.best_score_)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 0.1, 'loss': 'squared_hinge', 'penalty': 'l2'}\n",
            "0.818553459119497\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JL6qrqHBky-X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8d9ff5b4-c3e7-4a36-cc8d-0695c4526364"
      },
      "source": [
        "lsvc = LinearSVC(C=1,loss='hinge', penalty='l2')\n",
        "\n",
        "lsvc.fit(X_train_processed, y_train)\n",
        "y_pred = lsvc.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 80.66%\n",
            "\n",
            "F1 Score: 15.60\n",
            "\n",
            "Precision: 75.68%\n",
            "\n",
            "Recall: 8.70%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1236    9]\n",
            " [ 294   28]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ecYbrM4Cp0Gx",
        "colab_type": "text"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhGLzObyp3cu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "97dda300-a4b1-40a6-cb19-a3a138457dde"
      },
      "source": [
        "#Random Forest Grid Search\n",
        "rfc = RandomForestClassifier() \n",
        "\n",
        "param_grid = { \n",
        "    'n_estimators': [15, 20, 30],\n",
        "    'max_depth':[15, 20, 30]\n",
        "}\n",
        "\n",
        "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
        "CV_rfc.fit(X_train_processed, y_train)\n",
        "print(CV_rfc.best_params_)\n",
        "print(CV_rfc.best_score_)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_depth': 30, 'n_estimators': 20}\n",
            "0.8150943396226417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iyNkgQ-i-Lx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "13704dca-207b-4e5f-85a5-5afcf0a23ad7"
      },
      "source": [
        "rfc = RandomForestClassifier(max_depth=30, n_estimators=20) \n",
        "\n",
        "rfc.fit(X_train_processed, y_train)\n",
        "y_pred = rfc.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 79.96%\n",
            "\n",
            "F1 Score: 5.99\n",
            "\n",
            "Precision: 83.33%\n",
            "\n",
            "Recall: 3.11%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1243    2]\n",
            " [ 312   10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ugKt-sitp2wV",
        "colab_type": "text"
      },
      "source": [
        "### Gradient Boosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "113gBZl_lmH-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "dfe85072-9af4-439a-805c-345dab8ac8f8"
      },
      "source": [
        "gbc = GradientBoostingClassifier() \n",
        "\n",
        "param_grid = { \n",
        "    'n_estimators': [15, 20, 30],\n",
        "    'max_depth':[15, 20, 30]\n",
        "}\n",
        "\n",
        "CV_gbc = GridSearchCV(estimator=gbc, param_grid=param_grid, cv= 5)\n",
        "CV_gbc.fit(X_train_processed, y_train)\n",
        "print(CV_gbc.best_params_)\n",
        "print(CV_gbc.best_score_)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_depth': 15, 'n_estimators': 30}\n",
            "0.808490566037736\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Zk4koxqBlmII",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "ffd52551-4db5-4354-b8e8-b256b5c975f3"
      },
      "source": [
        "gbc = GradientBoostingClassifier(max_depth=20, n_estimators=30) \n",
        "\n",
        "gbc.fit(X_train_processed, y_train)\n",
        "y_pred = gbc.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 78.94%\n",
            "\n",
            "F1 Score: 16.67\n",
            "\n",
            "Precision: 44.59%\n",
            "\n",
            "Recall: 10.25%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1204   41]\n",
            " [ 289   33]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7EQSXakppB3L",
        "colab_type": "text"
      },
      "source": [
        "## Predicting Catra's lines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "__GE1wnBpjjM",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "df.dropna(inplace=True)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df['dialogue'],\n",
        "                                                    df['character'], test_size=0.33, random_state=42)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "MrCdPSyYpjjR",
        "colab": {}
      },
      "source": [
        "cv_train = cv.fit_transform(X_train)\n",
        "tfidf = TfidfTransformer(norm='l2', use_idf=True, smooth_idf=False, sublinear_tf=False).fit(cv_train)\n",
        "X_train_processed = tfidf.transform(cv_train)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T3LIVqYOpjjS",
        "colab": {}
      },
      "source": [
        "cv_test = cv.transform(X_test)\n",
        "X_test_processed = tfidf.transform(cv_test)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1fbTyLwqpFSQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "775d4d88-2c6d-41ef-879b-42136e041df8"
      },
      "source": [
        "# split classes into Catra = 1, Everyone else = 0\n",
        "new_y_train = y_train.copy()\n",
        "y_train = []\n",
        "for line in list(new_y_train.values): \n",
        "  if line == 'Catra': \n",
        "    y_train.append(1)\n",
        "  else: \n",
        "    y_train.append(0)\n",
        "\n",
        "new_y_test = y_test.copy()\n",
        "y_test = []\n",
        "for line in list(new_y_test.values): \n",
        "  if line == 'Catra': \n",
        "    y_test.append(1)\n",
        "  else: \n",
        "    y_test.append(0)\n",
        "print(y_train[0:10])\n",
        "print(y_test[0:10])"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "[0, 0, 0, 0, 0, 1, 0, 0, 0, 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EOtI9oHPpFST"
      },
      "source": [
        "### Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JDxLLub-pFST",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "c69136a0-fbb2-421b-994a-633656f47b8e"
      },
      "source": [
        "nb = MultinomialNB()\n",
        "\n",
        "param_grid = { \n",
        "    'alpha': [0, .5, 1],\n",
        "    'fit_prior':[True, False], \n",
        "}\n",
        "\n",
        "CV_nb = GridSearchCV(estimator=nb, param_grid=param_grid, cv= 5)\n",
        "CV_nb.fit(X_train_processed, y_train)\n",
        "print(CV_nb.best_params_)\n",
        "print(CV_nb.best_score_)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'alpha': 1, 'fit_prior': True}\n",
            "0.8977987421383646\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OqOaj3copFSV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "cbe95dfb-eea8-439d-8bb9-e2a868cf7faa"
      },
      "source": [
        "nb = MultinomialNB(alpha=1)\n",
        "\n",
        "nb.fit(X_train_processed, y_train)\n",
        "y_pred = nb.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 89.85%\n",
            "\n",
            "F1 Score: 0.00\n",
            "\n",
            "Precision: 0.00%\n",
            "\n",
            "Recall: 0.00%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1408    0]\n",
            " [ 159    0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mWfD-0LfpFSX"
      },
      "source": [
        "### Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mDF2SGGrpFSX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "caa5e6ac-d340-4f4d-dc8e-7a61797febd7"
      },
      "source": [
        "lg = LogisticRegression() \n",
        "\n",
        "param_grid = { \n",
        "    'C': [1,10,100, 200],\n",
        "    \"penalty\":[\"l1\",\"l2\"]\n",
        "}\n",
        "\n",
        "CV_lg = GridSearchCV(estimator=lg, param_grid=param_grid, cv= 5)\n",
        "CV_lg.fit(X_train_processed, y_train)\n",
        "print(CV_lg.best_params_)\n",
        "print(CV_lg.best_score_)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 1, 'penalty': 'l2'}\n",
            "0.8977987421383646\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Yb0nbTdupFSZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "92ef8a20-3717-4193-efbd-0a9821888229"
      },
      "source": [
        "lg = LogisticRegression(C=1, penalty='l2')\n",
        "\n",
        "lg.fit(X_train_processed, y_train)\n",
        "y_pred = lg.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 89.85%\n",
            "\n",
            "F1 Score: 0.00\n",
            "\n",
            "Precision: 0.00%\n",
            "\n",
            "Recall: 0.00%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1408    0]\n",
            " [ 159    0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iLBsZfzCpFSb"
      },
      "source": [
        "### SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xmNpDlNBpFSc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "812d6fe7-97dd-4920-fe32-5dd740ba87e9"
      },
      "source": [
        "lsvc = LinearSVC()\n",
        "\n",
        "param_grid = {\n",
        "    'penalty': ['l2'],\n",
        "    'C': [.01,.1, 1],\n",
        "    'loss' : [\"squared_hinge\", \"hinge\"]\n",
        "}\n",
        "\n",
        "CV_lsvc = GridSearchCV(estimator=lsvc, param_grid=param_grid, cv = 5)\n",
        "CV_lsvc.fit(X_train_processed, y_train)\n",
        "\n",
        "print(CV_lsvc.best_params_)\n",
        "print(CV_lsvc.best_score_)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 0.01, 'loss': 'squared_hinge', 'penalty': 'l2'}\n",
            "0.8977987421383646\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DB_-Fu18pFSd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "dbd325b6-376f-4800-f1c6-135826400288"
      },
      "source": [
        "lsvc = LinearSVC(C=0.1,loss='squared_hinge', penalty='l2')\n",
        "\n",
        "lsvc.fit(X_train_processed, y_train)\n",
        "y_pred = lsvc.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 89.85%\n",
            "\n",
            "F1 Score: 0.00\n",
            "\n",
            "Precision: 0.00%\n",
            "\n",
            "Recall: 0.00%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1408    0]\n",
            " [ 159    0]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "rhaZGccepFSf"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "AOqLgoFypFSf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d22bd0e9-e4a7-4f02-fc8d-f12aca32a588"
      },
      "source": [
        "#Random Forest Grid Search\n",
        "rfc = RandomForestClassifier() \n",
        "\n",
        "param_grid = { \n",
        "    'n_estimators': [15, 20, 30],\n",
        "    'max_depth':[15, 20, 30]\n",
        "}\n",
        "\n",
        "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
        "CV_rfc.fit(X_train_processed, y_train)\n",
        "print(CV_rfc.best_params_)\n",
        "print(CV_rfc.best_score_)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_depth': 30, 'n_estimators': 15}\n",
            "0.8987421383647799\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "C_p7uyfPpFSh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "396261f9-245c-42ed-96a8-440f73ec5a73"
      },
      "source": [
        "rfc = RandomForestClassifier(max_depth=30, n_estimators=15) \n",
        "\n",
        "rfc.fit(X_train_processed, y_train)\n",
        "y_pred = rfc.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 89.79%\n",
            "\n",
            "F1 Score: 1.23\n",
            "\n",
            "Precision: 33.33%\n",
            "\n",
            "Recall: 0.63%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1406    2]\n",
            " [ 158    1]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jEcmsUDhpFSj"
      },
      "source": [
        "### Gradient Boosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "pk5QdJgnpFSj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fb33c119-9452-4ce7-aa1c-dac4ab78a70f"
      },
      "source": [
        "gbc = GradientBoostingClassifier() \n",
        "\n",
        "param_grid = { \n",
        "    'n_estimators': [15, 20, 30],\n",
        "    'max_depth':[15, 20, 30]\n",
        "}\n",
        "\n",
        "CV_gbc = GridSearchCV(estimator=gbc, param_grid=param_grid, cv= 5)\n",
        "CV_gbc.fit(X_train_processed, y_train)\n",
        "print(CV_gbc.best_params_)\n",
        "print(CV_gbc.best_score_)"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'max_depth': 15, 'n_estimators': 15}\n",
            "0.8977987421383649\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PYoRH04zpFSm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6cba48eb-5e9a-4b75-df0e-529064ab5895"
      },
      "source": [
        "gbc = GradientBoostingClassifier(max_depth=20, n_estimators=15) \n",
        "\n",
        "gbc.fit(X_train_processed, y_train)\n",
        "y_pred = gbc.predict(X_test_processed)\n",
        "\n",
        "print(\"\\nAccuracy: {:.2f}%\".format(accuracy_score(y_test, y_pred) * 100))\n",
        "print(\"\\nF1 Score: {:.2f}\".format(f1_score(y_test, y_pred) * 100)) #harmonic mean of precision & recall (want a higher F1 score)\n",
        "print(\"\\nPrecision: {:.2f}%\".format(precision_score(y_test, y_pred) * 100))\n",
        "print(\"\\nRecall: {:.2f}%\".format(recall_score(y_test, y_pred) * 100)) \n",
        "print(\"\\nConfusion Matrix:\\n\", confusion_matrix(y_test, y_pred))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 89.73%\n",
            "\n",
            "F1 Score: 6.94\n",
            "\n",
            "Precision: 42.86%\n",
            "\n",
            "Recall: 3.77%\n",
            "\n",
            "Confusion Matrix:\n",
            " [[1400    8]\n",
            " [ 153    6]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HLK90Lhup5o",
        "colab_type": "text"
      },
      "source": [
        "# What are some relationships among words on this show? \n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eY5Oz43BqgK0",
        "colab_type": "text"
      },
      "source": [
        "## Word Network Graph\n",
        "\n",
        "For this section, I will be creating a word network graph, showcasing the common bigrams, or pairs, of words used together on this show. This is one way that we can show connections between words within the dialogue of the show. \n",
        "\n",
        "source: https://www.earthdatascience.org/courses/use-data-open-source-python/intro-to-apis/calculate-tweet-word-bigrams/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YlbPZlWu17c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from nltk import bigrams\n",
        "import networkx as nx\n",
        "\n",
        "sns.set(font_scale=1.5)\n",
        "sns.set_style(\"whitegrid\")"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfDSRnobQlO9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f62942a6-44a2-448c-9b4d-7a3221b8b105"
      },
      "source": [
        "text[0:5]"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['  hey princess looking me',\n",
              " ' squadrons report training area immediately evaluation',\n",
              " ' psst anyone seen catra',\n",
              " ' again',\n",
              " ' attention cadets simulation begin heres scenario passing treacherous whispering woods reach heart rebel insurgency bright moon']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nyxfFa6qQuQN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a6dc1bea-dd4d-446a-fd2c-5c55c98e1a6c"
      },
      "source": [
        "new_text = []\n",
        "for line in text: \n",
        "  new_line =[]\n",
        "  line = line.split()\n",
        "  for word in line: \n",
        "    new_line.append(word)\n",
        "  new_text.append(new_line)\n",
        "new_text[0:5]"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['hey', 'princess', 'looking', 'me'],\n",
              " ['squadrons', 'report', 'training', 'area', 'immediately', 'evaluation'],\n",
              " ['psst', 'anyone', 'seen', 'catra'],\n",
              " ['again'],\n",
              " ['attention',\n",
              "  'cadets',\n",
              "  'simulation',\n",
              "  'begin',\n",
              "  'heres',\n",
              "  'scenario',\n",
              "  'passing',\n",
              "  'treacherous',\n",
              "  'whispering',\n",
              "  'woods',\n",
              "  'reach',\n",
              "  'heart',\n",
              "  'rebel',\n",
              "  'insurgency',\n",
              "  'bright',\n",
              "  'moon']]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dQpauaNOece",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create list of lists containing bigrams in script in each line(?)\n",
        "\n",
        "terms_bigram = [list(bigrams(line)) for line in new_text]"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHV0OqKEOujC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Flatten list of bigrams in script\n",
        "bigrams = list(itertools.chain(*terms_bigram))"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVXhnlMPPBEw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create counter of words in bigrams\n",
        "bigram_counts = collections.Counter(bigrams)"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6ixdbZwPIPd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bigram_df = pd.DataFrame(bigram_counts.most_common(60), #adjust the number of bigrams\n",
        "                             columns=['bigram', 'count'])"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aWcWp4edPYVH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create dictionary of bigrams and their counts\n",
        "d = bigram_df.set_index('bigram').T.to_dict('records')"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSG5MGSgPlrV",
        "colab_type": "code",
        "colab": {}
      },
      "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))"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rdhbNzmlPwW4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "0937e541-8f3b-4a0e-82f7-12d91edeba7e"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(25, 15))\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='pink',\n",
        "                 node_color='purple',\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='red', alpha=0.25),\n",
        "            horizontalalignment='center', fontsize=13)\n",
        "  \n",
        "plt.show()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CiORqrErecUe",
        "colab_type": "text"
      },
      "source": [
        "## Word2Vec Exploration\n",
        "\n",
        "Word2Vec produces word embeddings to group vectors of similar words together. Similar words tend to be close to each other in a visualization and have higher similarity scores. In the context of a script, we can see which characters are often talked about in a similar manner and which other words (objects, actions, ajectives, etc.) are commonly associated with specific characters. \n",
        "\n",
        "source: https://www.kaggle.com/pierremegret/gensim-word2vec-tutorial#Gensim-Word2Vec%C2%A0Tutorial\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fvKjQxjKkFkd",
        "colab_type": "text"
      },
      "source": [
        "### Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7LW0Fk8kQI5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a1d7c506-62ea-4d4f-814f-abf49ee282c4"
      },
      "source": [
        "#convert list of responses to df\n",
        "text_df = pd.DataFrame(text)\n",
        "text_df.head()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hey princess looking me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>squadrons report training area immediately ev...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>psst anyone seen catra</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>again</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>attention cadets simulation begin heres scena...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   0\n",
              "0                            hey princess looking me\n",
              "1   squadrons report training area immediately ev...\n",
              "2                             psst anyone seen catra\n",
              "3                                              again\n",
              "4   attention cadets simulation begin heres scena..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DoeAkvOGkPAQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "import spacy\n",
        "\n",
        "nlp = spacy.load('en', disable=['ner', 'parser'])"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pphq5VPekO-j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cleaning(doc):\n",
        "    # Lemmatizes and removes stopwords\n",
        "    # doc needs to be a spacy Doc object\n",
        "    txt = [token.lemma_ for token in doc if not token.is_stop]\n",
        "    # Word2Vec uses context words to learn the vector representation of a target word,\n",
        "    # if a sentence is only one or two words long,\n",
        "    # the benefit for the training is very small\n",
        "    if len(txt) > 2:\n",
        "        return ' '.join(txt)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yd2syMAMmD6f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "brief_cleaning = (re.sub(\"[^A-Za-z']+\", ' ', str(row)).lower() for row in text_df[0])\n",
        "\n",
        "txt = [cleaning(doc) for doc in nlp.pipe(brief_cleaning, batch_size=5000, n_threads=-1)]"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfC92xjymD4o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "3e42491e-afd4-4f4a-e111-da6133ce6cf4"
      },
      "source": [
        "df_clean = pd.DataFrame({'clean': txt})\n",
        "df_clean = df_clean.dropna().drop_duplicates()\n",
        "df_clean.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3544, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKaiMjTimXfr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "text = [row.split() for row in df_clean['clean']]"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wpk211ppkI5X",
        "colab_type": "text"
      },
      "source": [
        "### Gensim Word2Vec\n",
        "\n",
        "tutorial/source code: https://www.kaggle.com/pierremegret/gensim-word2vec-tutorial#Gensim-Word2Vec%C2%A0Tutorial"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jTv2f3srhYpS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "0edc3ad4-423a-4c7d-f0e7-1440811d8560"
      },
      "source": [
        "pip install --upgrade gensim"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting gensim\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/2b/e0/fa6326251692056dc880a64eb22117e03269906ba55a6864864d24ec8b4e/gensim-3.8.3-cp36-cp36m-manylinux1_x86_64.whl (24.2MB)\n",
            "\u001b[K     |████████████████████████████████| 24.2MB 165kB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: smart-open>=1.8.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (2.0.0)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.18.5)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.4.1)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.8.1->gensim) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: boto in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.8.1->gensim) (2.49.0)\n",
            "Requirement already satisfied, skipping upgrade: boto3 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.8.1->gensim) (1.14.9)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.8.1->gensim) (2.9)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.8.1->gensim) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.8.1->gensim) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.8.1->gensim) (2020.6.20)\n",
            "Requirement already satisfied, skipping upgrade: botocore<1.18.0,>=1.17.9 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.8.1->gensim) (1.17.9)\n",
            "Requirement already satisfied, skipping upgrade: s3transfer<0.4.0,>=0.3.0 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.8.1->gensim) (0.3.3)\n",
            "Requirement already satisfied, skipping upgrade: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.8.1->gensim) (0.10.0)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.6/dist-packages (from botocore<1.18.0,>=1.17.9->boto3->smart-open>=1.8.1->gensim) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.18.0,>=1.17.9->boto3->smart-open>=1.8.1->gensim) (0.15.2)\n",
            "Installing collected packages: gensim\n",
            "  Found existing installation: gensim 3.6.0\n",
            "    Uninstalling gensim-3.6.0:\n",
            "      Successfully uninstalled gensim-3.6.0\n",
            "Successfully installed gensim-3.8.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNJk22_IehYc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from gensim.models.phrases import Phrases, Phraser"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3EXXUYFexEu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# testing out with all data\n",
        "phrases = Phrases(text, min_count=30, progress_per=10000)"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrQkQG45hGdT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#bigrams detected\n",
        "bigram = Phraser(phrases)\n",
        "\n",
        "bigrams = bigram[text]"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ar-W-gaShSqF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fea06642-9a9d-4449-ef90-6892997c2589"
      },
      "source": [
        "from collections import defaultdict\n",
        "word_freq = defaultdict(int)\n",
        "for text in bigrams:\n",
        "    for i in text:\n",
        "        word_freq[i] += 1\n",
        "len(word_freq)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3312"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FggxO5-iSWp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "316d6fa0-fa8c-4758-dd8c-d13375876a41"
      },
      "source": [
        "sorted(word_freq, key=word_freq.get, reverse=True)[:10]"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['s', 'know', 'be', 'adora', 'like', 'go', 'come', 'glimmer', 'horde', 'okay']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G742OXU6ik3V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import multiprocessing\n",
        "from gensim.models import Word2Vec\n",
        "\n",
        "cores = multiprocessing.cpu_count()\n",
        "\n",
        "w2v_model = Word2Vec(min_count=5,\n",
        "                     window=2,\n",
        "                     size=300,\n",
        "                     sample=6e-5, \n",
        "                     alpha=0.03, \n",
        "                     min_alpha=0.0007, \n",
        "                     negative=20,\n",
        "                     workers=cores-1)\n"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "it4bwMGsjI0D",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w2v_model.build_vocab(bigrams, progress_per=10000)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5maniuEvjIdu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8addd841-38cb-432f-9a56-06ca9559c637"
      },
      "source": [
        "w2v_model.train(bigrams, total_examples=w2v_model.corpus_count, epochs=30, report_delay=1)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(114448, 640980)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmMdFE6MjLRl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "w2v_model.init_sims(replace=True)"
      ],
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HnKDWa4qm12v",
        "colab_type": "text"
      },
      "source": [
        "#### Exploring \"Most Similar\" Function\n",
        "\n",
        "use this to input a word and find the top 10 most similar words in the corpus based on the data given. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "he9-m8e7jVQd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "549c793a-f5fd-4614-d771-cddcdf730fd5"
      },
      "source": [
        "w2v_model.wv.most_similar(positive=[\"shera\"])"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('tech', 0.999935507774353),\n",
              " ('mad', 0.9999310970306396),\n",
              " ('act', 0.9999303221702576),\n",
              " ('break', 0.9999291896820068),\n",
              " ('attack', 0.9999288320541382),\n",
              " ('hit', 0.9999284744262695),\n",
              " ('bright_moon', 0.9999282360076904),\n",
              " ('call', 0.999927818775177),\n",
              " ('signal', 0.999927818775177),\n",
              " ('sound', 0.9999277591705322)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFBSCuLSjchK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "01956611-fea9-41c3-ffa3-664dc84a4023"
      },
      "source": [
        "w2v_model.wv.most_similar(positive=[\"glimmer\"])"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('weapon', 0.999930202960968),\n",
              " ('lot', 0.9999299049377441),\n",
              " ('prisoner', 0.999929666519165),\n",
              " ('clearly', 0.999929666519165),\n",
              " ('track', 0.9999291896820068),\n",
              " ('year', 0.9999284744262695),\n",
              " ('best', 0.9999279975891113),\n",
              " ('blow', 0.9999270439147949),\n",
              " ('don_t', 0.9999268054962158),\n",
              " ('entrapta', 0.9999266862869263)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YX0S7PqqjcdU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fa5b9ac1-f53e-4a02-e318-e0004dc61dad"
      },
      "source": [
        "w2v_model.wv.most_similar(positive=[\"catra\"])"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('begin', 0.9999312162399292),\n",
              " ('stuff', 0.9999310970306396),\n",
              " ('sure', 0.9999306201934814),\n",
              " ('weapon', 0.9999306201934814),\n",
              " ('quick', 0.999930202960968),\n",
              " ('signal', 0.9999300241470337),\n",
              " ('quest', 0.9999297857284546),\n",
              " ('um', 0.9999293088912964),\n",
              " ('speak', 0.9999290704727173),\n",
              " ('important', 0.99992835521698)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzPJzM6TnLRU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fd5dc811-7390-4ad3-968c-e7bad5b22675"
      },
      "source": [
        "w2v_model.wv.most_similar(negative=[\"adora\"])"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('prom', -0.9998788833618164),\n",
              " ('murder', -0.9998819828033447),\n",
              " ('distress', -0.9998879432678223),\n",
              " ('planning', -0.9998884201049805),\n",
              " ('light', -0.999889075756073),\n",
              " ('entire', -0.9998915195465088),\n",
              " ('isn', -0.9998922348022461),\n",
              " ('yay', -0.99989253282547),\n",
              " ('princesse', -0.9998930096626282),\n",
              " ('black', -0.9998931288719177)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZNFNzstnVCw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "43e06f63-5f3d-4f21-bf1b-d155c619d189"
      },
      "source": [
        "w2v_model.wv.most_similar(negative=[\"glimmer\"])"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('prom', -0.999873697757721),\n",
              " ('distress', -0.9998807907104492),\n",
              " ('grayskull', -0.9998888969421387),\n",
              " ('cheer', -0.9998899698257446),\n",
              " ('aw', -0.9998908042907715),\n",
              " ('panic', -0.9998910427093506),\n",
              " ('ok', -0.9998917579650879),\n",
              " ('sonic', -0.9998923540115356),\n",
              " ('write', -0.9998927116394043),\n",
              " ('gotcha', -0.9998931884765625)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KYTb9bxm7SM",
        "colab_type": "text"
      },
      "source": [
        "#### Exploring \"Similarity\" Function\n",
        "\n",
        "use this to find the cosine similarity between two words you input. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggb3dLLujcbl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "59409610-1153-4c63-c383-fed3505a681e"
      },
      "source": [
        "# similarities\n",
        "w2v_model.wv.similarity(\"adora\", 'shera')"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.99992037"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5xzFsglyj3p6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fe7d33b8-1269-426d-a5f7-5a9568a90ed2"
      },
      "source": [
        "w2v_model.wv.similarity(\"adora\", 'catra')"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999097"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndYrHi95j3TF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1abf9b21-96e5-4df8-9172-abfa052efab1"
      },
      "source": [
        "w2v_model.wv.similarity(\"shadow_weaver\", 'horde')"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9999212"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDZMgQarqKV0",
        "colab_type": "text"
      },
      "source": [
        "### Visualization Using t-SNE\n",
        "\n",
        "\"we will look at the relationships between a query word (in **red**), its most similar words in the model (in **blue**), and other words from the vocabulary (in **green**)\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D3BOPr20p58x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        " \n",
        "import seaborn as sns\n",
        "sns.set_style(\"darkgrid\")\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zIx3R6nHrhm9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def tsnescatterplot(model, word, list_names):\n",
        "    \"\"\" Plot in seaborn the results from the t-SNE dimensionality reduction algorithm of the vectors of a query word,\n",
        "    its list of most similar words, and a list of words.\n",
        "    \"\"\"\n",
        "    arrays = np.empty((0, 300), dtype='f')\n",
        "    word_labels = [word]\n",
        "    color_list  = ['red']\n",
        "\n",
        "    # adds the vector of the query word\n",
        "    arrays = np.append(arrays, model.wv.__getitem__([word]), axis=0)\n",
        "    \n",
        "    # gets list of most similar words\n",
        "    close_words = model.wv.most_similar([word])\n",
        "    \n",
        "    # adds the vector for each of the closest words to the array\n",
        "    for wrd_score in close_words:\n",
        "        wrd_vector = model.wv.__getitem__([wrd_score[0]])\n",
        "        word_labels.append(wrd_score[0])\n",
        "        color_list.append('blue')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "    \n",
        "    # adds the vector for each of the words from list_names to the array\n",
        "    for wrd in list_names:\n",
        "        wrd_vector = model.wv.__getitem__([wrd])\n",
        "        word_labels.append(wrd)\n",
        "        color_list.append('green')\n",
        "        arrays = np.append(arrays, wrd_vector, axis=0)\n",
        "        \n",
        "    # Reduces the dimensionality from 300 to 50 dimensions with PCA\n",
        "    reduc = PCA(n_components=20).fit_transform(arrays)\n",
        "    \n",
        "    # Finds t-SNE coordinates for 2 dimensions\n",
        "    np.set_printoptions(suppress=True)\n",
        "    \n",
        "    Y = TSNE(n_components=2, random_state=0, perplexity=15).fit_transform(reduc)\n",
        "    \n",
        "    # Sets everything up to plot\n",
        "    df = pd.DataFrame({'x': [x for x in Y[:, 0]],\n",
        "                       'y': [y for y in Y[:, 1]],\n",
        "                       'words': word_labels,\n",
        "                       'color': color_list})\n",
        "    \n",
        "    fig, _ = plt.subplots()\n",
        "    fig.set_size_inches(20,20)\n",
        "    \n",
        "    # Basic plot\n",
        "    p1 = sns.regplot(data=df,\n",
        "                     x=\"x\",\n",
        "                     y=\"y\",\n",
        "                     fit_reg=False,\n",
        "                     marker=\"o\",\n",
        "                     scatter_kws={'s': 40,\n",
        "                                  'facecolors': df['color']\n",
        "                                 }\n",
        "                    )\n",
        "    \n",
        "    # Adds annotations one by one with a loop\n",
        "    for line in range(0, df.shape[0]):\n",
        "         p1.text(df[\"x\"][line],\n",
        "                 df['y'][line],\n",
        "                 '  ' + df[\"words\"][line].title(),\n",
        "                 horizontalalignment='left',\n",
        "                 verticalalignment='bottom', size='medium',\n",
        "                 color=df['color'][line],\n",
        "                 weight='normal'\n",
        "                ).set_size(15)\n",
        "\n",
        "    \n",
        "    plt.xlim(Y[:, 0].min()-50, Y[:, 0].max()+50)\n",
        "    plt.ylim(Y[:, 1].min()-50, Y[:, 1].max()+50)\n",
        "            \n",
        "    plt.title('t-SNE visualization for {}'.format(word.title()))"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aen7kpeIuYU_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "81d56d37-2345-4a4d-b221-53b5cbfe2aa7"
      },
      "source": [
        "tsnescatterplot(w2v_model, 'adora', [i[0] for i in w2v_model.wv.most_similar(negative=[\"adora\"])])"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MQwBYVi0bvzIDAJrEon15ZsyYUa+8ZcuWGTRoUL2vRculXn/99U8NYxbtCdTY0qpPOuGEE1JbW9sg6FnShg8fnjlz5uS2227LuHHjMmjQoHTp0uVzj2vsLY3rr79+kvr3q0ePHnn66aczZ86curIZM2bklltuaXB8aWlpg6WIs2fPzqhRoxb3chp4+umnc8opp2TAgAE588wzGw2mFs2S+uS5X3zxxbp9qv7Top+LxkK7xvoeMmRInn/++fz1r3+tV3f55ZenpqYmO+yww2Jfz1ex6DyXX355vWt98cUX88ADD2TjjTf+zJcQfJ677rors2fPzgEHHJChQ4c2+Prud7+bVq1a5eabb06ycHP+8vLyXHfddfV+Pt59993cfvvt9fpekvexrKwsScP/3zfeeGOqqqq+8HUDwLLOzC0AWIH169cvpaWlueyyyzJjxoy0bt06q622Wt0MrMZMnDgxxx13XDbddNNsttlm6dq1a+bMmZNnnnkm48aNS5s2bTJy5MjPPXfPnj0zfPjwumVejZk5c2bGjh3baF2vXr0azM5pzDbbbJMOHTrknHPOyaxZszJ8+PDPPSZJdtlll2ywwQbp379/KisrU1VVlRtvvDHNmzfPrrvuWtfu4IMPzo9+9KN885vfzJ577pkPP/wwN910U1ZdddUGQcLOO++cG264IT/84Q8zaNCgfPDBB7n55ps/d4njZznmmGOyYMGCDB06NOPGjatX16ZNm+ywww7p2bNn1llnnfz+97/P3Llzs+aaa+a1117LDTfckHXXXTf/+te/6h03YMCAXHvttTnttNOyzTbbpHnz5unfv3+j+2QlC/cne+SRRzJy5MgcdNBB6d69e5588sncdddd2XTTTRf7nn9VgwcPzrBhw3LnnXdmxowZGTJkSKqqqvKnP/0pLVu2zM9+9rOv1P/o0aPTqlWrbLXVVo3Wt2rVqm6m4HvvvZcuXbrkBz/4Qc4666wccMAB2WuvvTJnzpxcf/316dGjR55//vl6xy+p+7j11lunVatWOemkk3LIIYekffv2eeqpp/LXv/413bt3b3TJLAAUmXALAFZgq666an71q1/liiuuyGmnnZb58+dn+PDhnxlubbrppjnppJPy8MMP55ZbbskHH3yQ2trarLLKKtl7773z7W9/O2usscZinf/73/9+7rjjjsydO7fR+nfffTcnnXRSo3VHHXXUYoVbLVq0yG677ZZrr702bdu2XezZL4cffngeeuihXHPNNZk5c2Y6d+6cAQMG5Mgjj6x33j322CPvv/9+rrvuupxxxhlZffXVc8wxx6S0tDTPPPNMvT5//OMfp02bNhk3blzuv//+rLLKKtl///3Tr1+/HHbYYYs1rk9atDl8Y/uhdevWLTvssEPKysryu9/9LmeddVbGjBmTOXPmZJ111slZZ52ViRMnNgi3dtttt0yYMCF33nlnxo0bl5qamrpra0y3bt1y44035sILL8xtt92WmTNnpkuXLjnyyCNz9NFHN/o2v6XlnHPOyXrrrZcxY8bkzDPPTOvWrbPpppvmBz/4QXr16vWl+33ppZfyzDPPZKeddvrMfbp22mmn3HPPPRkzZkyOOuqoHH744WndunWuvvrqnHvuuVlllVVy+OGHp127dvnJT35S79gldR+7d++eK664Iuedd14uu+yylJWVZaONNso111yTX/7yl3nrrbe+9H0AgGVRSe0n5ysDAAAAQEHYcwsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACF1aypB7A8mjbto9TU1Db1MPj/OndumylTZjX1MIAvyLMLxeO5hWLy7EIxrUjPbmlpSTp2bPOp9cKtpaCmpla4tYzx/wOKybMLxeO5hWLy7EIxeXYXsiwRAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAL42lZe0T+Ul7XPTC9c3qLvphevr6pemCVOeT+Ul7fPwW39bqucB4Osh3AIAAL5WbZq3za0v39ygfMxLo9OmedsmGBEARSbcAgAAvlY79xiav0x6INPnTqsrmzZ3ah6a/GB27jFsiZyjuqY686rnLZG+Ps386vmprqlequcA4PMJtwAAgK/VJl02S9c2q+SOV2+rK7vj1dvStc0q2bTrZvXaPvzW31J5SftMmPJ8vfK9bt0lh48bUff52PuPyo43bZO7Xr0jW/15s6x+eUWeeu/JJMlVz12RDf7QJz0u75pD7twv781+t8GYamprcuFT52WzawdktctWzsDrNsz1E69r9Jx//NfV2fTa/ln98oq8+9E7X/l+APDVNGvqAQAAACuWkpKS7Ln23hnz0ugcst43kyxckrjX2t/4Sv1OmvlGfvHoqTlhk5NT2bpLurdfI3e/dmdO+esJ+eb6386wNXfNo28/nB8+MLLBsT/+24m5YeKfc8KmJ6f/ygPy0OQH88MHR6ZTeafs9B+zyZ5497G8/uFr+fkWv0irZq3SvuXS3R8MgM8n3AIAAL52w9f+Ri59+qK8P/v91KY2j7z995w2+Fd54p1Hv3SfU+dOzU173JZ+K/evK/vWuIOzXfcd8uttfpMk2a77Dpky54NcO+EPdW1enfFKRj13ZS7Y7pIc0PvgJMk2qw/Jex+9m3P+98x64daHH8/IA/s9nMrWlV96nAAsWZYlAgAAX7t+FQOy5kpr5baXb8ltL9+SniutXS+U+jJWabNqvT4W1CzIs1XPZGiPXeu123Wt3et9/tvkh1JaUppd19o9C2oW1H1ttdo2eW7K/9XbV6t/xQaCLYBljJlbAABAk9hz7b0z5uWbU1tbmz3X3vsr91fxidBpytwpqa6tzsqtKuqVf/Lz1DkL2/X8/WqN9vve7HezattujZ4DgKYn3AIAAJrE8LX3yXlPnp0kuWC7Sxpt07KsZZJkfk39Nx9O/3h6OpV3rldWkpJ6nzuXd05ZSVk+mFNVr/yTnzuUd0yz0ma5Y/i9KS1puLjlP8OwT54DgKYn3AIAAJrEup16ZcR630qSrNNx3UbbLJox9eK0F9K/YoMkyVszJ+flaS9mrZV6fmb/zUqbpd/K/TPu9TtzWN9v15Xf+ert9dpt1W2bVNdU58N5H2bb1bf70tcDQNMQbgEAAE3mnG3P/8z6Vdt2ywYVG+bMJ/4nrZq1Tk1tTS546tx0KO+4WP3/YOMT861xB+dHDx2XXdbcLY++/XAeeHN8vTZrd1wn31z/8Bx577cycsMfZoPKDfPxgrmZOG1iXp3+cn4z5OIvfX0ALH3CLQAA4Aub8/GCPPTspDz+4mupTbL5Oj2y7YDuadVyyf8T47KdrsrxDx6bkeOPyCptV83Pt/hlfvfMbxfr2F3X2j1nbPXrXPjUb3LjC3/KoFW3zG+GXJz97xher91ZW5+Xnh3WzrXP/yFnP/E/adeiXdbt1DsH9R6xxK8HgCWrpLa2trapB7G8mTJlVmpq3NZlRUVFu1RVzWzqYQBfkGcXisdzu+KYv6AmvxnzWJ6e8WCmtfpHktp0mLNxNmg/JMcNH5gWzcuaeoh8AZ5dKKYV6dktLS1J585tP73+axwLAACwHHju1SmZOOOpVLW/NwtaTMmCFlPzQfv7MvHDf+TZV6Y09fAAWMEItwAAgC/kuUnvpKrZM6n34sCS5INmz+a5SW812bgAWDEJtwAAgC+kfavyNK9p16C8WU3btG/VuglGBMCKrHDh1rPPPpvTTjstu+yySzbYYINsu+22Oe644/LGG280aPvUU0/lwAMPzIABAzJ48OCcfvrpmTNnToN28+bNy69//etsueWW6d+/f/bbb788+uijX8flAABA4WzWq1tWq94qpQv+vf9J6YI2Wa1662zee7UmHBkAK6LCvS3x97//fZ566qkMHTo0vXr1SlVVVa677rrstddeGT16dHr27JkkmTBhQg477LCsvfbaOeWUU/Luu+/mqquuyuTJk3PZZZfV6/OUU07Jvffem0MPPTRrrLFGxowZkyOOOCLXXHNNNtxww6a4TAAAWGat0rlNDhy0eW58pEWmlj6f2tSmc8362WeLDdJt5TZNPTwAVjCFe1viU089lb59+6ZFixZ1Za+//np233337LrrrjnzzDOTJEcccUReeOGF3H333WnTZuEfsDfddFN+9rOfZdSoUdliiy2SLJwJtu++++bHP/5xDjvssCTJxx9/nN122y2VlZW57rrrvvAYvS1x2bIivUEClicryrNbeUn7uu/Ly8qz5kpr5Zt9v53D1v92SksKN8GaFdyK8tzyb7PmzM+Lk6antjZZd/WV0q51i88/iGWOZxeKaUV6dpe7tyVutNFG9YKtJOnRo0fWWWedvPLKK0mSWbNm5ZFHHslee+1VF2wlyZ577pnWrVvn7rvvrisbN25cmjdvnn333beurGXLltlnn33yj3/8I++///5SviIAVnRHDzg2d+09PtftelMGd9sqp/z1hFz93BVNPSyAz9W2VfNstG5FNu5VIdgCoMkULtxqTG1tbT744IN07NgxSfLCCy9kwYIF6du3b712LVq0SJ8+fTJhwoS6sgkTJmTNNdesF4IlSf/+/VNbW1uvLQAsDd3bd88mXTfLVqttk19t9etstdq2GfXclZ/afu6CuV/j6AAAYNm2XIRbt912W957770MGzYsSVJVVZUkqaioaNC2oqKi3mysqqqqVFZWNtouiZlbAHztBlRskEkz30ySXD/xulRe0j5Pvfdk9rp1l3T/XWV++/QFSZK/TX4oQ0cPyeq/q8h6V/fMSQ8dl1nzZ9X18/Bbf0vlJe3z18l/yaF3HZAel3fN5tdtkAffvD/VNdX570d+lt5X9Uj/P/TKpU9f3CTXCgAAX1XhNpT/pFdeeSW/+MUvsvHGG2fPPfdMksydu/A32p9cvpgsXHK4qH5R2+bNmzfaLlm4/9YX9VnrQGkaFRUNX1UNLPtWlGe3bdvyetf6/ry307Vd11RUtEu7t8qTJMc88J0cs8kxOX2HX6RDeYe8X/tmDrhj7+zYc8f8YvvTMunDSTll/Cl5Z+7kjDtkXJKkw0etkyQn/+24HLnxkTluyx/k7IfPznfuPTQH9zs4tWW1+fM+f86dL96Z/3rkJ9m5z3bZfLXNv/4bwHJlRXluYXnj2YVi8uwuVOhwq6qqKkceeWRWWmmlXHDBBSktXTgRrbx84T8E5s2b1+CYjz/+uK5+Udv58+c32i75d8j1RdhQftmyIm2yB8uTFenZ/XDm7Lzz3rTMXTAnd756e25+/uZ8t/8xqaqamW+/uTEAACAASURBVJkzF/5C5vD1vptD1/lu3THfvfewrNZu9Vy5/XUpKy3LZh2TZtu0yhH3Hpa7/m98Nu26eaZPn50k2Xvt/XLYukclSf578zOy1fWb5bl3n88te96RJBmw8eb58/9dn2ufuj5rtVzva756licr0nMLyxPPLhTTivTsft6G8oUNt2bOnJkjjjgiM2fOzJ///Od6SxAXfb9oeeJ/+uQyxE8uU/zPdkkaXbIIAEvST/9+cn7695OTJCUpyX69DsyPNvtxvTY79Ni53uen3n8qu6+1Z8pKy+rKdltrzzQrbZbH33ksm3b99wysrVbbtu77NVdaK0myZbet68pKS0qzRvseeWfW20vsmgAA4OtSyHDr448/zlFHHZXXX389o0aNylprrVWvft11102zZs3y3HPPZaeddqornzdvXiZMmJDdd9+9rqx379655ppr8tFHH9XbVP6ZZ56pqweApWnkBj/InmsPT3mzVlmjfY+0ataqQZvKVvV/2fL+R++monX9srLSsnRs2SnT506rV75Si5Xqvm9RtnDJ/kotV6rXpkVZi3xc/cWX4gMAQFMr3Iby1dXV+eEPf5inn346F1xwQTbYYIMGbdq1a5ctttgiY8eOzUcffVRXPnbs2MyePTtDhw6tKxs6dGjmz5+fm266qa5s3rx5ueWWW7LRRhulS5cuS/eCAFjhrdZutWxQuVF6d+rTaLCVJCkpqfexsk3XfDCn/gzl6prqTPt4ajqUd1xaQwUAgGVO4WZunXnmmXnggQcyZMiQTJ8+PWPHjq2ra9OmTXbYYYckyXHHHZcDDjggI0aMyL777pt33303V199dbbeeusMGjSo7pgBAwZk6NChOeecc1JVVZXu3btnzJgxefvtt3PGGWd87dcHAItj48qNc9ert+enm/9X3dLEO1+9LQtqFmTzVQY28egAAODrU7hwa+LEiUmSBx98MA8++GC9um7dutWFW+uvv36uvvrqnHPOOTnjjDPStm3b7Lfffjn++OMb9Hn22Wfn/PPPz9ixYzNjxoz06tUrl19+eTbeeOOlf0EA8CUct8lJ2f7GLfPNuw/MYX2/nbdnvZ1fPvZfGbL69vX22wIAgOVd4cKta665ZrHbbrLJJrn++us/t13Lli1z8skn5+STT/4qQwNgBfbmezNz15Mv5LX3pmalNi2z44De2aRXZUo+sZxwSendqU/+vNvN+dVjp+Vb4w5JuxbtMnztb+Tng365VM4HAADLqpLa2traph7E8mbKlFmpqXFblxUr0utRYXlSpGf39Xc/zPm3/yWvNL8988onpWxBh3Sbs2P26rdNdh24dlMPD742RXpugX/z7EIxrUjPbmlpSTp3bvvp9V/jWABguXTrY//Kyy3GZG6bF1NTNifzW76TN9rdkPuenZhZc+Y39fAAAGC5JtwCgK+gtrY2r787Ix+3er1+ednH+ajZ5LxVNatpBgYAACsI4RYAfAUlJSVp1bIsZQs+MU26Nmles1LalDdvmoEBAMAKQrgFAF/RNn17pvKjIUntv/9YbT27T1Zru1q6VbRpwpEBAMDyr3BvSwSAZc0OG/XIO9O2y1Ovr5EPm72aVjWV6da6R747bJOl9rZEAABgIeEWAHxFzcpK862dNsgu09bN2x98lHatW2StVdunVLAFAABLnXALAJaQLh1bp0vH1k09DAAAWKHYcwsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAAAAAApLuAUAAABAYQm3AAAAACgs4RYAAAAAhSXcAgAAAKCwhFsAAAAAFJZwCwAAAIDCEm4BAAAAUFjCLQAAAAAKS7gFAAAAQGEJtwAAAAAoLOEWAAAAAIUl3AIAAACgsIRbAAAAABSWcAsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMISbgEAAABQWMItAACWe5Mnl+SHP2yZ/v3bZLXV2mbjjdvkpz9tmSlTSpp6aADAV9SsqQcAAABL08SJpRk+vFVWXrk2P/nJx+nevTYvvVSa889vkXvvbZbbb5+drl1rm3qYAMCXJNwCAGC5VVubHHNMeVZaKbnrrtlp125h+aBB1dlppwXZdts2OemklvnjH+c27UABgC/NskQAAJZbjz5alueeK8txx31cF2wtssoqtfnOd+blnnua5c03Fy5PfPjhslRWtsuDD5bl4INbpUePttlwwzYZNap5g74fe6wse+7ZKmus0Ta9erXN8ce3zKxZ/66//vpmqaxsl+efL80++yzsa9Cg1rnjDr9fBoAlSbgFAMBy69FHy5Iku+yyoNH6YcMWpLa2JI8/Xlav/LjjyrPeetW56qo52X77BTnppPLce++/2zz+eFn22adVKitrc+WVc/LLX87N+PHN8v3vlzc4x9FHl2fo0AW5+uo5WWut2hx5ZHneftteXwCwpPi1EQAAy6133inJSivVNpi1tcjqq9f8/3b1f+e7/fYL8tOfzkuSbLdddV5/vTS/+U3L7LTT7CTJ6ae3yKabVueKK/69nHGVVWrzjW+0zoQJ89KnT01d+ZFHzstBBy0M1wYMmJP112+be+9tlsMOm7/ErhMAVmRmbgEAwCd8cqbXrrsuyDPPlKa6Opk9O3nyybLssceCLFiQuq/NN69O8+a1eeaZ+n/F3nbb6rrvO3VKVl65Nu+8Y+YWACwpZm4BALDcWmWV2syYUZKZM9Po7K1Jk0r/f7uaeuUrr1zb4POCBSWZMqUk1dVJdXVJTj65PCef3HAZ4ttv1w+32rev31eLFsncucItAFhShFsAACy3tthi4aypceOaZd99G+67dc89zVJSUpuBA6vrlX/wQUmDz82a1aZz59rMnZuUlNTmRz+alx12aNhn1661DcoAgKVHuAUAwHJriy2q07dvdc47r2WGDVuQtm3/XffeeyW54ormGTp0QVZfvX4gddddzbL99tX1Pg8YUJOysqRNm2TjjWvy8sulOfHE+jO+AICvn3ALAIDCqKmtzfvT5iRJKju2SmnJZy/vKylJfvvbudl771bZZZfWGTlyXrp3r81LL5Xm/PNbpF275KyzPm5w3P33N8uvflWbLbaozp13NstDDzXLH/84u67+5z//OPvs0yrHHFOe3XdfkLZta/PWWyW5775m+clPPk7PnmZvAcDXRbgFAEAhvPbOh/njHW+kqmrhnlYrr1yTb+6+RtZcpf1nHtenT03uu292zjmnRU4/vWWmTi1Jly61GTZsQY4/fl46d24YRJ133txcfnmL/O53LdKhQ23OPHNuhg7990yugQOrM3bs7Jx9dsuMHFmemppktdVqMmRIdSoqBFsA8HUqqa2t9afvEjZlyqzU1Lity4qKinapqprZ1MMAviDPLhTP0nxup838OP9z5YRM+FufzPuwQ5KkRfvp6bPVhPz0233SsV3LJXKehx8uy/DhrfPQQx+lTx9LDlkx+DMXimlFenZLS0vSuXPbT6//GscCAABfyhPPV2XSxC6Z92HHJCVJSjLvw455c0KXPPGvqqYeHgDQhIRbAAAs896pmpfZ0xv+xnbOjHZ554N5TTAiAGBZYc8tAACWeT26lad9xYzMndKlXnm7iunpsWr5EjvP4MHVef/9FWOJBwAsL8zcAgBgmbdJ74qs0WdKWnd5OympSUpq0rrL2+nRe0o26VPR1MMDAJqQmVsAACzz2rZqnh8etG5urHgzL73xapJknTXaZL8d103bVs2beHQAQFMSbgEAUAiVHVrle/v2ytx5C5Ik5S38VRYAEG4BAFAwQi0A4D/ZcwsAAACAwhJuAQAAAFBYwi0AAAAACku4BQAAAEBhCbcAAAAAKCzhFgAAAACFJdwCAAAAoLCEWwAAAAAUlnALAAAAgMJq1tQDAAAAAPg8lZe0r/u+vKw8nVutnAEVG+bAPodk5x7D6rU99v6jMnHqhNy370OL1feDb96fF6dNzJEDRi7RMfP1EG4BAAAAhXD0gGOze889s6BmQd6aNTn3vH5XDr3rgBzQ++BcsN0lde2O3+SkzF0wd7H7/cukB3LHq2OFWwUl3AIAAAAKoXv77tmk62Z1n7+x7n7ZdvXt88MHR2aLVQfngN4HJ0nWXGmtpXL+6prqVNdWp0VZi6XSP19OIffcev/993POOedkxIgR2XDDDdOrV688/vjjjba9//77M3z48PTr1y/bbrttLr744ixYsKBBuw8//DCnnnpqBg4cmA022CCHHnpoJkyYsLQvBQAAAPgKDuozIht32SR/+NeVdWXH3n9Udrxpm7rPMz6enuMe/F76jVo3q/+uIhv+cb0c/+CxSZKzn/hVLn3mokya+WYqL2mfykva59j7j6rXz12v3pGt/rxZVr+8Ik+992SS5O7X7syON22T1X9XkfWvXjunPXJq5lfPrzvn27Peynfu+WbWu3qtdP9dZTa9tn/OfPyXdfUTp07I/rcPz7pXdk+Py7tm8J82yZX/d/lSvVfLq0LO3HrttddyxRVXZI011kivXr3yz3/+s9F2Dz30UEaOHJmBAwfm1FNPzYsvvpjf/va3mTZtWk499dS6djU1Nfnud7+bF198MYcffng6duyYP/3pTxkxYkRuueWWdO/e/eu6NAAAAOAL2ma1Ibnwn7/J/Or5aV7WvEH9zx/+Sf733cfzy8FnpLJ1l7w1a3Iee+eRJMkh630zr854JX9/668ZNfS6JEnnVivXHTtp5hv5xaOn5oRNTk5l6y7p3n6NjH35lhx53+E5dL1v5Seb/zyvf/ha/uex01JTW5PTBv9PkuR79x+ZOQvm5JxtLsxKLVfKGx++npemvVjX7yF37Z91O6ybS3a4Ii3KWubl6S9l1ryZS/M2LbcKGW6tv/76eeyxx9KxY8eMHz8+I0c2vib27LPPznrrrZcrr7wyZWVlSZI2bdrk8ssvz4gRI9KjR48kybhx4/LPf/4zv/3tb7PDDjskSYYNG5add945F198cc4+++yv5boAAACAL26Vtt2yoGZBpn08LZWtKxvU//P9f+Twvkdkr3W+UVe2b68DkiSrtu2WLq27pmVZy3pLHheZOndqbtrjtvRbuX+SpLa2Nqc9cmr263Vgzt7mN3XtWpa1zCl/PSE/2Pj4dCrvnKfe+0d+t9NVdZvdD+62VV3bKXOm5M0PX88fh/0563VeP0my9WrbfvUbsYIq5LLEtm3bpmPHjp/Z5uWXX87LL7+c/fffvy7YSpKDDjooNTU1uffee+vK7rnnnlRWVmb77bevK+vUqVOGDRuW8ePHZ/78+QEAAACWTbW1tZ9Zv37nfvnt0xfmqueuyCvTX/pCfa/SZtW6YCtJXpn+cibPmpQ9ew7PgpoFdV9bdts6c6vnZuKUhVsc9V25X/7nsf/O9ROvy+SZk+r12bG8Y7q1XS0/euiHufWlm1M1u+oLjYn6ChluLY7nn38+SdK3b9965V26dEnXrl3r6pNkwoQJWX/99VNSUlKvbb9+/fLRRx/lzTffXPoDBgAAAL6Udz96O81Lm6djy8Ynwpy59TkZtuauOfd/z8oWf9o4m1+3Qca8NHqx+q74xEywKXOnJEkOvHOfrHpZp7qvTa7tlyR5a9bkJMkVO43KgIoNc+rDP85G16yfITcMzl8n/yVJUlpSmht3vzWVrbvkhw+OTN9Ra2f3MTvn/6qe+TKXv8Ir5LLExVFVtTD1rKioaFBXUVGR999/v17bgQMHNmhXWbnwB/j9999Pz549l9JIAVhRVVa2q/u+pKQ2lZW1GTiwOj/72cdZY43P/u3jF/Xww2UZPrx1Hnroo/TpU7NE+wYAaGp/mfRA+lds0Oh+W0myUssO+dVWv86vtvp1/vXBc7n4n+fn6PHfyXqd+6ZXp96f2XdJ6k+EWRSgnbvthfVmdC3Svf0aSZJV2q6ai7a/LDW1NXnqvSfz6/89I4fedUCeOvRf6VTeOet0XDdXD70286vn57F3HskvH/15Drpz3zzzzYkpLVlu5yItFcttuDV37twkSYsWDV/P2bJly8yZM6de28baLSpb1Nfi6ty57Rdqz9JXUdHu8xsBy5wV4dk94YRkn32S2tqSvPZaSf7rv0pz6KHN8+yzSbMl+Kd0hw4L/9upU5s08nsfWGJWhOcWlkeeXYqibdvyBj+vV/3zqjz1/j/yh73+UFdXXt48zZqVNvqzvW3FFum1Wo/cfN6Neb9mUras2DQd2rXNvJqPG7RvrJ/OK2+Ubu26ZcqCd7Pj+scu1riHVW6fDh1aZ9BVg/JRs6npVdGjXv3eXXfLx81m5qBbDkrzdtXp1GqlxerXs7vQchtulZeXJ0nmzZvXoO7jjz+uq1/UtrF2i8r+s+3imDJlVmpqluxv3PnyKiraparKGyegaFaMZ7ddVl55bnr2XLi349prJyUlZTnooNZ57LGP0qvXkpthNX16WZLWmTr1o1RVmbnF0rFiPLew/PHs0hRqamszb351WjQvS+kntgj6LM+//WLu/r/7s6BmQd7+6K2Me+3OjH15TA7qPSLDVh1e97M8d+78LFhQU/d5t1t2yi5r7Z7enfqkJCW5dsIf0rpZm/QsXy9VVTOzaos18t5H7+Wiv12W3p36pFN553Rvv0aDfv4fe3ce31SV93H8e2/2blC6AZayUxCllR0VkUVARBFQVBTHEYdBHXV0HNQH0VFxXHnceEBgXBhEEARkUVxAx2UUBEVEBQQUAYHSlq2lbdok9/kjkBrKKtCQ9vN+vfrSnHvuzS95cSD95pxzD3iw42jduniYcvbkqXvGRXKYTv2yd6MW/rxAL/WaIl+gTFct6K8rM69R4xpNVOr3avzKsUqNSVOy0vWf1V/oH5+P1OVNBqp+jQbaXbJbY5Y/oZZJZ8tf6FBu4dHHZHUau6ZpHHEiUZUNtw4sR8zNzQ0tLzwgNzdX55xzTljf3y5TPOBA28HnAwBwqsTt/zf7t/cy+eADmyZMcOqHH0yVlBjKzAxoxAivunb1h537/femHnvMpSVLbPL5pMzMgO67z6sLLwzvd8CcOXbddptbjzziVffuPrVtG6fXXitSz57l/W+7za01a0x98EGRJOnJJ516+WWHJk8u0f/8j0s//miqSZOAHnvMq44dD/08AAAAB1iWpSU/bNPby1drb0mRYpxuXZTdTF2zM44p5Bq/8gWNX/mCXDaXktzJyk5trX/3mR66I+HhtK3dXm+smapNBZtkM2w6O7mVpvedpbpxZ0iS+jUZoP9u/VQPfzFKecV5uipzsF7o/uJhr3d504GKd8br2a/HaNrq12QaNtVPaKCeDXrJaXPKZtrUolZLTfp2vLYW/iqP3aM2ae0049K35LF7lBqTppSYVD3z9dPK2bdNCc4aOv+MzhrV6eHje0MhqQqHWy1atJAkfffdd2rZsmWoPScnR9u3bw8dl6TmzZtrxYoVsiwrbFP5b7/9VjExMcrIyKi8wgEA1UogIPl8kmVJmzYZevJJpxo1CoTti7Vpk6levXy65ZaATFNavNiua67xaO7cYnXoEAyU1q0z1bdvjJo0Ceipp0qUmGhp5Uqbtm499IfEadPsuvtut8aMKdHVV/u0adOxf2NaXGzo1lvduv32UqWlBTR+vFPXXOPRkiX7lJbGzGUAAHB4n3+/Vf/+fLE2xb4lX6182cpqKHf5JSop7a5LOjQ54rk7btl7zM9zcDD1j3NH6x/njj5sf7fdree7jT/qdX6re/2e6l6/5yGP2WXX/3Z94bDnpsSkaFyPSYc9juNTZcOtpk2bqlGjRnrjjTd0xRVXyGazSZKmTZsm0zTVs2f5H8DevXvrvffe0+LFi9WjRw9J0s6dO/Xuu++qe/fucjgOvSEdAAAnauRIt0aOLF/+XrduQK+/Xqz9/2xJkoYOLZ/GFQhI55/v19q1pl5/3REKt55+2qmEBEvz5hXJ4wn2PdyMrVdfdej++10aO7ZE/fv7jrvm4mJD991XooEDg+eed16xWreO08SJDo0aVXGZPwAAgCQFApbeXr5av8TNkt+xW5Lkd+zRlvg5+nBVhrqf00BuZ5WNKXAKRe2fmnHjxkmSNmzYIEmaO3euvvrqKyUkJOi6666TJI0YMUI333yzhg4dqj59+ujHH3/U1KlTddVVV6lhw4aha/Xq1UvZ2dkaMWKEbrzxRiUmJmratGkKBAK67bZj2xwOAIDf49ZbS9WvXzC8yssz9MorTg0e7NHChUWqUyc4C2rrVkP//KdLn3xiU06OIcsKzrJq3748mPr0U5uuuMIXCrYO51//cmjGDIcmTixRnz7HH2wd8Ntz4+KkLl18WrHCdoQzAABAdVdYUqbC0kL5Y3eHtQdsxSoxdmpXgVd1kqI2pkAERe2fmueeey7s8axZsyRJZ5xxRijc6tq1q8aOHauxY8fqkUceUa1atXTzzTfrlltuCTvXZrNp4sSJevLJJzVlyhR5vV6dffbZeuKJJ1S/fv3KeUEAgGopPT2g7OzyJYidOxcrOztWL77o1EMPeRUISEOGeFRYaOiee0rVsGFAMTGWnnjCpby88qWEu3YZSks7+kbxCxY41LBhQBdc8PuDrdhYq0KIlpxs6YcfuGU1AAA4vBiXXU7TLdMXo4C9KNRuBOxy+GsoPsYZweoQzaI23Fq7du0x9evRo0doqeGR1KhRQ48++qgeffTREy0NAIDfzeWS6te3tG5dMCj6+WdDq1bZNH16kbp1K19mWFISfl5ioqWcnKOHS+PHF2vECLeGDPFo2rRiHbghsMsV/G9pafjeW3v2VLzGvn2GiosVFnDl5RnstwUAAI7IbjN1QctGyv2+l7bFL5BllkkBm5ILu6l1owzFedgSCL8PX7ECAHAaKSmRNm40VLducBZWcXEwbHL+5ovMzZsNffll+BLACy7wa948e4XQ62B16lh6880ibdhg6sYbPaG7MqakWHI4ykM1SSoslJYtO/RSw3fesYf1+/hju845h7slAgCAI+vTvon6NuupM/f8VU32/lFn7vmretbrq6u6tDz6ycBhRO3MLQAAqoJNm0wtXx4MlPLzDb38slN79xq69tpg6tS0aUB16wb04IMu3XuvV4WFhp580hXaj+uAu+/2qmfPWPXrF6Obby5VYqKlVatM1aplafDg8CWIDRpYmjmzWJdf7tEtt7g1YUKJTFPq3dunCRMcSk8PqEYNS+PHO0Mzu37L47H02GMu7dtnqHbtgMaNc6q0VBo2rKxiZwAAgN+w20xdecGZurhdU+XvKVHNeJdqxLIcESeGmVsAAJwgy7K0cfte/XfVNq1cnydv2bHPYBo/3qk+fWLVp0+s7rjDrdJSacaMYp1zTnDmlsslvfJKsex2aehQjx5/3KU77vCqU6fw52jSxNL8+UWqVcvSnXe6dcMNHi1Y4FB6+qGXCmZmBjRjRrE++siuu+5yybKkxx7zqn17v+65x61773Wrf3+fzj+/4mvxeCyNHVuiV15x6MYbPdq929C0acUsSwQAAMcszuNQ/drxBFs4KQzLsvgkepLl5xcqEOBtPV2kpMQrN7cg0mUAOE7RMnZLy/yaNHe9vl/tV+7mRMUnFqt2xl7dMqiJ0lPiIl3eSffkk069/LJDa9bsi3QpOA1Fy7gFEI6xC0Sn6jR2TdNQLdyqkwAAIABJREFUUtLhP1uzLBEAgBPwwZdb9fmnbu34obkkQ7t/lnb8lK9J9nV68E9nyzSNo14DAAAAwO/HskQAAE7AZyt2Kn9DA0nlIZZ3d5J2bHdq4/bq8U0aAAAAEEmEWwAAnABvmU+Bsoq3rS7zOo9r761oMWJEKUsSAQAAcFoh3AIA4ARkNkiQOyk3rM20lyo+eY8y0qrenlsAAADA6YY9twAAOAGXXVBX6zet04YVPhXvTJTNXaL0szbqks5pinVXnNEFAAAA4OQi3AIA4ATUSYrViD9kavGZ2/Xjxu1KTHCoe4faalE/MdKlAQAAANUC4RYAACcopaZHV1/UMNJlAAAAANUSe24BAAAAAAAgahFuAadA6riE0E/GhFR1md5RL383SQErIEnatPcXpY5L0PsbF0asxulrpip1XIIKywojVgMAAAAAACeKZYnAKXJz1m26tHE/FfuKtfDnBbr3k7/JsgIaevafI12aJKlH/V56Z8AixdhjIl0KAAAAAAC/G+EWcIpkJGSobe32kqTO6V20dtdavfrdS6dNuJXsSVayJznSZQAAAAAAcEJYlghUkqyUbG0u2HTY42+seV19Z/dUs5cy1PSlDPV/6xJ9s+PrsD63LR6ui2Z20X82f6gu0zupwcTa6ju7p9bsXB3WL3VcgiauHKdHlzykFi831JmvNNI9n9wlr98b6nPwssQDSyXnrp+tv/3nDjX+V7qyJjfXE18+GlpOecC89XPUYWq2Miakqv9bl2hV7kqljkvQ9DVTT/RtAgAAAADguBBuAZVk895NSolJPfzxgk0alHmN/tXr33qxx79UN+4MXTantzbu+Tms36+Fm/XQ56N0Z5u79eJFLyuvOFfD3r9BlmWF9Ru/cqy279um/+sxSbdk36F/f/+KJn47/qh1PvzFA4p1xOqlXv/WFc2u0pjlT2j+hrdCx7/Z8bWGffBHtUrO1qsXT1WvhhfrT+/fcHxvBgAAAAAAJwnLEoFTJGAF5Av4VOIr1ts/zdeCn+ZqWKtbDtv/7nb3hp3bpV43rdjxld788Y2wY7tKdmlB//fVqGaTUN8b3h2s9bvXqWlis1C/evEZeqH7i5Kkbhk99OX2JXrnp3m67Zy/HrHujnXO1cPn/VOSdGG9bvpw0yK9/dM89WsyQJL0wopn1SwxUxN7viLDMNQt4yKVBXx65IsHjvMdAgAAAADgxBFuAafIyM/u0cjP7pEkGTI0KPMa/b39fYft/+POtXp06UNatn2p8opzQ+0bdq8P61cvPiMUbElSZq1MSdLWwl/Dwq0L63ULOy8zsblW7lhx1LornFcrU1sKtoQef7Pja/VvcoUMwwi19W7Qh3ALAAAAABARhFvAKXJr9h3q16S/3HaP6ic0kMfuOWzfwtICDZp/uVJiUvXwef9UenyG3DaX7vzotrB9siSphqtm2GOH6ZQkef0lB/WrcVA/R4U+h1LxPGdYDTuKcpR00Eb0SZ6ko14XAAAAAIBTgXALOEXS49OVndr6mPou2/6ltu77VTMvmxs2+6qgdO+pKu93S41JU35xXlhbfnF+hKoBAAAAAFR3bCgPnAZK9s+octqcobYvty3VpoJfIlXSYWWnttb7vywM28D+3Y3vRLAiAAAAAEB1xswt4DTQJq2dYh1xuus/t+sv2Xdo276temrZY6oTWzfSpVVw2zl/Ve9Z3TTs/T/qmhbX6sdda/XaD69KCu4tBgAAAABAZWLmFnAU23cWaeYnq/XCvCV6Z+kG7Sn0Hv2k45Qak6qXek1WblGO/rDwGk1YOU5PdXlGDWs0OunPdaKyU1trwkUva2XuCv1h4WAt2DBPT17wjCQp3pkQ4eoAAAAAANWNYf12bRFOivz8QgUCvK2ni5SUeOXmFvyuc7//OV+TFv1Xm+wfymvLU2xZAzUyL9Bf+52v2rViTnKl0Wvm2um6dfEwLbvuW9VPaBDpclBFnMjYBRAZjFsgOjF2gehUncauaRpKSoo77HGWJQKH4fMH9NrHX2ttzGT5nMEN1Es9m/VDYa5m/bembr20fYQrjJy/f3ynuqR3VU13TX2bu1LPfPWULqrfi2ALAAAAAFDpCLeAw9iWX6Q9vhz5YsPvDFgc+6N+3JqnMl9ADnv1XNm7q2Sn7vnkLu3y7lSiq5b6NR6gB899ONJlAQAAAACqIcIt4DBM05Ahm2RJv90n3bBMGTJkVOO90//Va3KkSwAAAAAAQBIbygOHVScpRqnuOnKWZIS1x+3LVlaDOrLbGD4AAAAAAEQaM7eAwzANQ3+8qI2K3vZqy97lKjB+VVKguRp4sjTgvBaRLg8AAAAAAIhwCziijLR4jbqmm1asO0t5ewuVkZKosxslyemwRbo0AAAAAAAgwi3gqOI8DnVudUakywAAAAAAAIfApkEAAAAAAACIWoRbAAAAAAAAiFqEWwAAAAAAAIhahFsAAAAAcIKys2N1552usLbCQqlOnTi1bx9bof8ll8Toyis9x3z9Nm1i9eCDrqN3BIBqiHALAAAAAE5Qu3Z+LVsWfkftr7+2yeWSNm40lZtrhNpLS6VvvzXVvr2/sssEgCqJcAsAAAAATlD79n6tW2dq9+7ytuXLberUya969QJhwdfKlaa8XkPt2hFuAcDJQLgFAAAAACeoXTu/LMvQ8uXlIdayZTa1betX27bhs7qWLbPJZrPUtm0w3CopkR56yKXs7Filp8fpwgtjtGiRrcJzHOyLL2zq39+jBg3i1LhxnC6/3KNVq8p/xVu1ytTAgR7Vrx+npk3jNHy4Wzt2lM8g27TJUGpqvKZPl26/3a1GjeKUlRWrmTPtkqQXXnDq7LNj1aJFrB5+2KlAIPz5V682NXiwRw0bxqlhwzgNHepWTo4hAKhshFsAAAAAcILOOiugmBgrFGJZlvTVV78Nt8p/9Vq2zKYWLQKKiws+HjrUozf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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kuYblUntu3DM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1b58a8da-7ae4-402d-c1b4-62d1b8fa28ed"
      },
      "source": [
        "tsnescatterplot(w2v_model, 'catra', [i[0] for i in w2v_model.wv.most_similar(negative=[\"catra\"])])"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxZD-QkCtXcJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "edd07090-a34e-4382-a15c-5461fa0c2bc8"
      },
      "source": [
        "tsnescatterplot(w2v_model, 'glimmer', [i[0] for i in w2v_model.wv.most_similar(negative=[\"glimmer\"])])"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjP6FURXuF5C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e9223dcf-2561-4c84-bd8e-9536f90a4be6"
      },
      "source": [
        "tsnescatterplot(w2v_model, 'bow', [i[0] for i in w2v_model.wv.most_similar(negative=[\"bow\"])])"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUTopp23w2Ik",
        "colab_type": "text"
      },
      "source": [
        "# Sentiment Analysis by Characters\n",
        "\n",
        "Coming Soon."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qtJ4Bbiw7As",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 96,
      "outputs": []
    }
  ]
}