Confidence_Scores - v2.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lsG9GvLbICeB"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "#%cd drive/MyDrive/ISB\\ Project/YouTubeKids/\n",
        "#%cd <into drive folder>"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaJLufB_sFoo"
      },
      "source": [
        "import json\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import nltk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjBMn22B-TEG"
      },
      "source": [
        "#Confidence intervals for slurs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Bw2IIaGr7Uh"
      },
      "source": [
        "channels=[]\n",
        "for file in os.listdir(\"Transcripts/New Trans\"):\n",
        "  channels.append(file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1W7NODUsAT2",
        "outputId": "7ced0f0d-a7b1-4e7c-a093-00836f38357b"
      },
      "source": [
        "tokens=[]\n",
        "confidence=[]\n",
        "for file in channels:\n",
        "  try:\n",
        "      print(file)\n",
        "      for f in os.listdir(\"Transcripts/New Trans/\"+file):\n",
        "        op=open(\"Transcripts/New Trans/\"+file+\"/\"+f,)\n",
        "        data=json.load(op)\n",
        "        for i in data['results']['items']:\n",
        "          tok=i['alternatives'][0]['content']\n",
        "          conf=i['alternatives'][0]['confidence']\n",
        "          tokens.append(tok)\n",
        "          confidence.append(conf)\n",
        "  except Exception as e:\n",
        "    print(e)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UC7Gf2tZ8coTX2ckTPgn62iQ\n",
            "UC5PYHgAzJ1wLEidB58SK6Xw\n",
            "UCax-4TdDh3oetlyh-GoL7UA\n",
            "UC8MR0wSTbzs5Yo7DgP04P\n",
            "UC68XdlxjeqSsjoAdR3nw9Vg\n",
            "UC5mMf2NE17_MiTL94ihRN2w\n",
            "UCadtap8RYMNy8w4K9lXhzpQ\n",
            "UC-5E0ZHSkN6hExW4rDCY4vw\n",
            "UC0bcaAsU6jN8O9TVuqGCSQQ\n",
            "UCFU1IubzLAHnoPA8uRt-SMA\n",
            "UCBnZ16ahKA2DZ_T5W0FPUXg\n",
            "UCDHU0rh47wAgRbI9iwVmq8g\n",
            "UCJcc5qvHowxRshW9P6YZwKA\n",
            "UChGJGhZ9SOOHvBB0Y4DOO_w\n",
            "UCmKsULSe7oGtdGcv3bz-_bw\n",
            "UCJplp5SjeGSdVdwsfb9Q7lQ\n",
            "UC_8PAD0Qmi6_gpe77S1Atgg\n",
            "UCoookXUzPciGrEZEXmh4Jjg\n",
            "UCKcQ7Jo2VAGHiPMfDwzeRUw\n",
            "UC_nVcTv8k1SETJ1QFrwz7tA\n",
            "UCzGoN1TCO2uiAQWyiNrGhuw\n",
            "UCVHO-W63u8RrXa6BAHxfLtQ\n",
            "UCZA2NJWgiWtLQZPfMhhEZUQ\n",
            "UCtBOJ9bEAoMOY9keGK6p2hQ\n",
            "UCTRNV3t2jxbkZgBjhyDv8UQ\n",
            "UCrZ9CtlECK_Jl92di5fCfSg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7hxIjTo48mnN"
      },
      "source": [
        "lex=[]\n",
        "with open('final_lexicon.txt','r') as file:\n",
        "    for line in file:\n",
        "        for word in line.split():\n",
        "          lex.append(word.lower())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WpD1SdEJ53Mb",
        "outputId": "f897a390-1278-463d-d0a2-ed67b686584d"
      },
      "source": [
        "df['Word'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              ".                1298284\n",
              ",                 571627\n",
              "the               293240\n",
              "to                195055\n",
              "and               178751\n",
              "                  ...   \n",
              "Agus                   1\n",
              "Marszalek              1\n",
              "Ickarus                1\n",
              "millionaire's          1\n",
              "snaring                1\n",
              "Name: Word, Length: 61491, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NB0B8EFAkutv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "212310db-1595-4ce6-a725-db88af79d3c5"
      },
      "source": [
        "print(len(lex))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1383\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UFAOwIhLIwjP"
      },
      "source": [
        "print(confidence)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_b-oBe2l8GC0"
      },
      "source": [
        "conf_slurs=[]\n",
        "all_slurs=[]\n",
        "for i in range(len(tokens)):\n",
        "  if(tokens[i] in lex):\n",
        "    conf_slurs.append(confidence[i])\n",
        "    all_slurs.append(tokens[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eUIFQhqO9eKP"
      },
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJwiHGcKlXnM"
      },
      "source": [
        "#tokens_= [str(i)+'\\n' for i in tokens]\n",
        "#confidence_= [str(i)+'\\n' for i in confidence]\n",
        "all_slurs= [str(i)[:-1] for i in all_slurs]\n",
        "conf_slurs= [float(i[:-1]) for i in conf_slurs]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Po_wYN9I4nom",
        "outputId": "2e4fdd62-826b-4199-a9a3-99fcc4e1212e"
      },
      "source": [
        "print(len(tokens_))\n",
        "print(len(confidence_))\n",
        "print(len(all_slurs))\n",
        "print(len(conf_slurs))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11224496\n",
            "11224496\n",
            "49078\n",
            "49078\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cH2PD_Uc5rjN"
      },
      "source": [
        "dict_={'Word':tokens, 'Confidence':confidence}\n",
        "df=pd.DataFrame(dict_)\n",
        "#df.to_csv(\"CSV/Results/Confidence_Words.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JdPmh5Td5asN"
      },
      "source": [
        "dict_={'Slur':all_slurs, 'Confidence':conf_slurs}\n",
        "df=pd.DataFrame(dict_)\n",
        "#df.to_csv(\"CSV/Results/Confidence_Slurs.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CDwg7ZcC651_"
      },
      "source": [
        "slurs=pd.read_csv(\"CSV/Results/Confidence_Slurs.csv\")\n",
        "words=pd.read_csv(\"CSV/Results/Confidence_Words.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuCOXmm6unn6"
      },
      "source": [
        "conf_slurs=[]\n",
        "for i in range(len(slurs)):\n",
        "  if(slurs['Slur'][i] in lex):\n",
        "    conf_slurs.append(float(slurs['Confidence'][i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOSUM7OBvXY5"
      },
      "source": [
        "conf_words=[]\n",
        "for i in range(len(words)):\n",
        "  conf_words.append(float(words['Confidence'][i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ve1frI1sKPV5"
      },
      "source": [
        "f = open(\"all_words.txt\", \"w\")\n",
        "f.writelines((tokens_))\n",
        "f.close()\n",
        "f = open(\"all_words_confidence.txt\", \"w\")\n",
        "f.writelines((confidence_))\n",
        "f.close()\n",
        "f = open(\"all_slurs_words.txt\", \"w\")\n",
        "f.writelines(all_slurs)\n",
        "f.close()\n",
        "f = open(\"slur_words_confidence.txt\", \"w\")\n",
        "f.writelines(conf_slurs)\n",
        "f.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "36pK2VNbKI5-"
      },
      "source": [
        "conf_slurs_=[float(i) for i in conf_slurs]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QbqaEA7WK9qj",
        "outputId": "f4b2940c-59a7-4d43-c57a-709d08696eed"
      },
      "source": [
        "print(conf_slurs_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBr6r9lIK6Kg"
      },
      "source": [
        "confidence_=[float(i) for i in confidence]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "ORRpjkb89KXU",
        "outputId": "2e208119-3a40-4c59-e6cb-f1429adbb6c9"
      },
      "source": [
        "plt.rcParams.update({'font.size': 22})\n",
        "plt.figure(figsize =(9, 6))\n",
        "plt.xlim([min(conf_slurs)-0.1, max(conf_slurs)+0.1])\n",
        "plt.hist(conf_slurs, bins=20, alpha=0.5)\n",
        "plt.xlabel('Confidence score', )\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "ofeXkNV89T4h",
        "outputId": "3c078f23-9ead-474a-8256-edbbe87538d0"
      },
      "source": [
        "plt.figure(figsize =(9, 6))\n",
        "plt.xlim([min(conf_words)-0.1, max(conf_words)+0.1])\n",
        "plt.hist(conf_words, bins=20, alpha=0.5)\n",
        "plt.title(f'Plot of confidence scores for all words')\n",
        "plt.xlabel('Confidence score')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/MdscWVWrqmrVihUrhixHkiQtAoOFjyRbJdlm5j7wKOC8ofqTJEnTYcjDLtsDxyeZ6efDVfVfA/YnSZKmwGDho6q+B9x3qPYlSdJ08qO2kiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWpqovCR5N5DFyJJkjYOk858vD3JWUmeneQ2g1YkSZKWtInCR1U9BHgqsBNwdpIPJ9lv0MokSdKSNPE5H1X1beAI4CXAw4C3JbkoyROGKk6SJC09k57zcZ8kbwYuBB4BPLaq7tnff/OA9UmSpCVm+YTb/V/gPcDLq+qXMwur6sokRwxSmSRJWpImDR9/Dvyyqm4CSLIJsHlV/aKqPrCuHZMsA1YDV1TVAbeoWkmSNPUmPefjc8AWI4+37JdN4p/oDtdIkiRNHD42r6rrZx7097eca6ckd6KbNXnPhpUnSZKWmknDxw1J9pp5kOR+wC/Xsf2MtwAvBn67tg2SHJ5kdZLVa9asmbAcSZI0rSY95+O5wLFJrgQC3AE4aF07JDkAuKqqzk7y8LVtV1VHAkcCrFq1qiasR5IkTamJwkdVfTXJPYDd+kUXV9Wv59jtwcCBSfYHNgduneSDVXXohpcrSZKm3aQzHwB7Ayv7ffZKQlUdvbaNq+plwMsA+pmPFxo8JEnSROEjyQeAuwLnADf1iwtYa/iQJEkaZ9KZj1XAvapqg87JqKpTgFM2ZF9JkrS0TPppl/PoTjKVJEm6RSad+dgOuCDJWcCvZhZW1YGDVCVJkpasScPHK4csQpIkbTwm/ajtqUl2Ae5eVZ9LsiWwbNjSJEnSUjTROR9JngkcB7yrX3RH4JNDFSVJkpauSU84/Xu6i4ZdC1BV3wZuP1RRkiRp6Zo0fPyqqm6ceZBkOd11PiRJktbLpOHj1CQvB7ZIsh9wLPD/hitLkiQtVZOGj5cCa4BvAn8LnAgcMVRRkiRp6Zr00y6/Bd7d3yRJkjbYpN/tcgljzvGoqrvMe0WSJGlJW5/vdpmxOfBk4HbzX44kSVrqJjrno6p+MnK7oqreAvz5wLVJkqQlaNLDLnuNPNyEbiZk0lkTSZKkm00aIN44cv83wKXAU+a9GkmStORN+mmXfYYuRJIkbRwmPezy/HWtr6o3zU85kiRpqVufT7vsDZzQP34scBbw7SGKkiRJS9ek4eNOwF5VdR1AklcC/1lVhw5VmCRJWpomvbz69sCNI49v7JdJkiStl0lnPo4GzkpyfP/48cD7hylJkiQtZZN+2uW1ST4NPKRf9PSq+vpwZUmSpKVq0sMuAFsC11bVW4HLk9x5oJokSdISNlH4SPIK4CXAy/pFmwIfHKooSZK0dE068/EXwIHADQBVdSWwzVBFSZKkpWvS8HFjVRVQAEm2Gq4kSZK0lE0aPj6W5F3AbZM8E/gc8O7hypIkSUvVnJ92SRLgo8A9gGuB3YB/qaqTBq5NkiQtQXOGj6qqJCdW1b0BA4ckSbpFJj3s8rUkew9aiSRJ2ihMeoXTBwCHJrmU7hMvoZsUuc9QhUmSpKVpneEjyc5V9QPgzxrVI0mSlri5Zj4+Sfdttt9P8vGqemKLoiRJ0tI11zkfGbl/lyELkSRJG4e5wket5b4kSdIGmeuwy32TXEs3A7JFfx9+d8Lprde2Y5LNgdOAW/X9HFdVr5iHmiVJ0hRbZ/ioqmW3oO1fAY+oquuTbAp8Kcmnq+qMW9CmJEmacpN+1Ha99d8Fc33/cNP+5qEbSZI2cpNeZGyDJFmW5BzgKuCkqjpzyP4kSdLiN2j4qKqbqmoP4E7A/ZPsPnubJIcnWZ1k9Zo1a4YsR5IkLQKDho8ZVXUNcDLw6DHrjqyqVVW1asWKFS3KkSRJC2iw8JFkRZLb9ve3APYDLhqqP0mSNB0GO+EU2AF4f5JldCHnY1X1qQH7kyRJU2DIT7ucC+w5VPuSJGk6NTnnQ5IkaYbhQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktSU4UOSJDVl+JAkSU0ZPiRJUlPLh2o4yU7A0cD2QAFHVtVbh+qvlTef9K15b/N5++06721KkrRYDRY+gN8AL6iqryXZBjg7yUlVdcGAfUqSpEVusMMuVfXDqvpaf/864ELgjkP1J0mSpkOTcz6SrAT2BM4cs+7wJKuTrF6zZk2LciRJ0gIaPHwk2Rr4OPDcqrp29vqqOrKqVlXVqhUrVgxdjiRJWmCDho8km9IFjw9V1SeG7EuSJE2HwcJHkgD/DlxYVW8aqh9JkjRdhpz5eDDwNOARSc7pb/sP2J8kSZoCg33Utqq+BGSo9iVJ0nTyCqeSJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaGix8JHlvkquSnDdUH5IkafoMOfNxFPDoAduXJElTaLDwUVWnAT8dqn1JkjSdFvycjySHJ1mdZPWaNWsWuhxJkjSwBQ8fVXVkVa2qqlUrVqxY6HIkSdLAFjx8SJKkjYvhQ5IkNTXkR22PAU4HdktyeZJnDNWXJEmaHsuHariqDhmqbUmSNL087CJJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaMnxIkqSmDB+SJKkpw4ckSWrK8CFJkpoyfEiSpKYMH5IkqSnDhyRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKaWr7QBUiStFi8+aRvLXQJGwVnPiRJUlOGD0mS1JThQ5IkNWX4kCRJTRk+JElSU4YPSZLUlB+1lSRNJT8WO72c+ZAkSU058yFJGpyzFBpl+JAk/R6DgoZm+JCkRnxTlzqGD0nN+SYsbdwMH5LWyaAgab4NGj6SPBp4K7AMeE9V/e8h+5M2dgYFSdNgsPCRZBnwb8B+wOXAV5OcUFUXDNWnNBTf1CVp/gw583F/4DtV9T2AJB8BHgcYPjQog4IkLW5Dho87ApeNPL4ceMCA/WmEb8CSpMVqwU84TXI4cHj/8PokFy9kPRPYDrh6Pht8/nw2Nn3mfTw3co7n/HI8549jOY+ePz3jucu4hUOGjyuAnUYe36lf9nuq6kjgyAHrmFdJVlfVqoWuY6lwPOeX4zm/HM/541jOr2kfzyG/2+WrwN2T3DnJZsDBwAkD9idJkqbAYDMfVfWbJP8AfIbuo7bvrarzh+pPkiRNh0HP+aiqE4ETh+xjAUzNIaIp4XjOL8dzfjme88exnF9TPZ6pqoWuQZIkbUSGPOdDkiTpDxg+1iLJo5NcnOQ7SV46Zv2tkny0X39mkpXtq5weE4zn85NckOTcJJ9PMvbjWerMNZ4j2z0xSSWZ2rPihzbJWCZ5Sv/zeX6SD7eucZpM8H995yQnJ/l6//99/4WocxokeW+Sq5Kct5b1SfK2fqzPTbJX6xo3WFV5m3WjO0H2u8BdgM2AbwD3mrXNs4F39vcPBj660HUv1tuE47kPsGV//1mO5y0bz367bYDTgDOAVQtd92K8TfizeXfg68Af9Y9vv9B1L9bbhON5JPCs/v69gEsXuu7FegMeCuwFnLeW9fsDnwYCPBA4c6FrnvTmzMd4N18avqpuBGYuDT/qccD7+/vHAY9MkoY1TpM5x7OqTq6qX/QPz6C7LozGm+TnE+DVwOuB/25Z3JSZZCyfCfxbVf0MoKqualzjNJlkPAu4dX//NsCVDeubKlV1GvDTdWzyOODo6pwB3DbJDm2qu2UMH+ONuzT8Hde2TVX9Bvg5sG2T6qbPJOM56hl0aV7jzTme/fTrTlX1ny0Lm0KT/GzuCuya5MtJzui/rVvjTTKerwQOTXI53achn9OmtCVpfX+3LhoLfnl1aVSSQ4FVwMMWupZplWQT4E3AYQtcylKxnO7Qy8PpZuROS3LvqrpmQauaXocAR1XVG5M8CPhAkt2r6rcLXZjaceZjvEkuDX/zNkmW000f/qRJddNnokvtJ9kX+GfgwKr6VaPaptFc47kNsDtwSpJL6Y4Fn+BJp2NN8rN5OXBCVf26qi4BvkUXRvSHJhnPZwAfA6iq04HN6b6nROtvot+ti5HhY7xJLg1/AvDX/f0nAV+o/gwg/YE5xzPJnsC76IKHx9TXbZ3jWVU/r6rtqmplVa2kO4fmwKpavTDlLmqT/F//JN2sB0m2ozsM872WRU6RScbzB8AjAZLcky58rGla5dJxAvBX/adeHgj8vKp+uNBFTcLDLmPUWi4Nn+RVwOqqOgH4d7rpwu/QnRB08MJVvLhNOJ5vALYGju3P2/1BVR24YEUvYhOOpyYw4Vh+BnhUkguAm4AXVZWznGNMOJ4vAN6d5Hl0J58e5h9u4yU5hi74btefI/MKYFOAqnon3Tkz+wPfAX4BPH1hKl1/XuFUkiQ15WEXSZLUlOFDkiQ1ZfiQJElNGT4kSVJThg9JktSU4UNaQpLcIclHknw3ydlJTkyy6wa29ZD+W1zPSXLHJMetZbtTvICZpPVh+JCWiP6LDY8HTqmqu1bV/YCXAdtvYJNPBV5XVXtU1RVV9aT5qnUxSrJsoWuQNhaGD2np2Af4dX/xIQCq6htV9cX+CohvSHJekm8mOQggycP7mYvjklyU5EP9tn8DPAV4db9sZZLz+n226GdXLkxyPLDFTH9JHpXk9CRfS3Jskq375Zcm+dd++TeT3KNfvnWS9/XLzk3yxHW1MyrJPya5oN/vI3O0d0i/7Lwkrx9p4/okb0zyDeBBSQ5NclY/2/MuA4k0DMOHtHTsDpy9lnVPAPYA7gvsC7whv/vq7T2B5wL3Au4CPLiq3kN36eYXVdVTZ7X1LOAXVXVPuisu3g9uvvT4EcC+VbUXsBp4/sh+V/fL3wG8sF/2P+kuCX3vqroP8IUJ2pnxUmDPfr+/W0d7OwKvBx7Rj8HeSR7fb78VcGZV3Zfuu5kO6p//HnRXM5393CXNAy+vLm0c/hQ4pqpuAn6c5FRgb+Ba4KyquhwgyTnASuBL62jrocDbAKrq3CTn9ssfSBdgvtxfIn8z4PSR/T7R/3s2XRiCLgjd/NUEVfWzJAfM0c6Mc4EPJfkk3fevrK29h9IdilrTP8cP9c/hk3QB4+P95o+kC1Jf7fvdAvB7hqQBGD6kpeN8ui85XF+j3yB8Exv+eyHASVV1yBz9zNXHXO3M+HO6EPFY4J+T3Ht9iu39dx/IZvp9f1W9bAPakbQePOwiLR1fAG6V5PCZBUnuk+QhwBeBg5IsS7KC7k37rA3s5zTgL/v2dwfu0y8/A3hwkrv167aa4JM2JwF/P1LvH03STpJNgJ2q6mTgJcBt6L6YcFx7ZwEPS7Jdfw7HIcCpY2r5PPCkJLfv971dkl3mGgxJ68/wIS0R/TeD/gWwb/9R2/OB1wE/ovsUzLnAN+hCyour6kcb2NU7gK2TXAi8iv48k/6wxmHAMf2hmNOBe8zR1muAP+pPBP0GsM+E7SwDPpjkm8DXgbdV1TVrae+HdOeHnNw//7Or6j9mF1JVF9Cda/LZvt+TgB1mbyfplvNbbSVJUlPOfEiSpKYMH5IkqSnDh2XYxHoAAAAmSURBVCRJasrwIUmSmjJ8SJKkpgwfkiSpKcOHJElqyvAhSZKa+v+ZQFdZcEyNCwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "ksd2_QQBvwSk",
        "outputId": "accc94be-5410-4183-ae75-9628b4542fe0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize =(9, 6))\n",
        "plt.xlim([-0.1, 1.1])\n",
        "plt.hist(conf_words, bins=20, alpha=0.5, label='Confidence of All Words', color='yellow')\n",
        "plt.hist(conf_slurs, bins=20, alpha=0.5, label='Confidence of Inappropriate Words', color='black')\n",
        "plt.title(f'Plot of confidence scores')\n",
        "plt.xlabel('Confidence score')\n",
        "plt.ylabel('Frequency')\n",
        "plt.legend(loc='best')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDbx-TbXxOcy"
      },
      "source": [
        "conf_slurs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JftA4OQ-aA6"
      },
      "source": [
        "#Slur Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdTtwcsL-cK7"
      },
      "source": [
        "lex=[]\n",
        "with open('lexicon.txt','r') as file:\n",
        "    for line in file:\n",
        "        for word in line.split():\n",
        "          lex.append(word.lower())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xyek1_nM-dbj"
      },
      "source": [
        "#copy paste video ids here\n",
        "main_video_ids=[]\n",
        "loc='Extra/new_transcripts'\n",
        "for i in os.listdir(loc):\n",
        "    main_video_ids.append(i[11:-4])\n",
        "\n",
        "common_ids=[]\n",
        "for file in video_ids:\n",
        "  if(file not in main_video_ids):\n",
        "    common_ids.append(file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Z_pHn0vMN2f",
        "outputId": "cdaf5a1c-3101-4ab4-bada-c9ad2bdd899c"
      },
      "source": [
        "print(common_ids)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['0h6pGCHZe0I', 'EOdPpojgPhg', 'qr4c2bahvic', 'ls1DzZ0ijyM', 'k75gNyrDv90', 'XMBayu9OEpc', 'KqlyqU-kOu8', 'kRUYWuSNbCA', 'KuJ_FNPQwlM', 'kuRjLXW8OHg', 'KTyKRL9hd5I', 'ksOc6TQwTaE', 'kVCLLcusvwE', 'kSiA3rV-Ny4', 'KUDeop_vHMU', 'ktQl8qMBTcY', 'ksWu5lNASBM', 'kSZ9qIQtRSw', 'KVQcC0IqotA', 'KT7qI07QGvY', 'KV_TyCYSNYg', 'KrsGag9teO4', 'Kwr3lbEt0CI', 'L6RBX2n-L0I', 'KZ8IOyNcZ-Q', 'kzLcKSgPBhk', 'KyFYYKt0aVQ', 'L4nrcKRYzqg', 'lA3J4Z38m_0', 'LNXLEKuCGqQ', 'li_IUnBXtwg', 'LoZlQi7HGwk', 'KxXiAiLqYGQ', 'lp9Eqmt1jOA', 'L6ABKuIsP_c', 'LL58zpHF--A', 'l8qgLjUQxZA', 'lqqxw-OgIgM', 'LFbK5sqfMNE', 'lEii6jhLfxM', 'lcF1mO4FZwY', 'LphqmnejveQ', 'LPH3BNkgbIg', 'L_G3ActFaHQ', 'KxiR4Fy0n54', 'l6ONoOp89Hk', 'Lf3ixwMN4Vc', 'KyUMyCA8AWA', 'L4yJa_eP7iA', 'lBpEk8ES_4o', 'l4zTz0pxKZI', 'Kx40_u6oupI', 'lB17tzz7DTI', 'KYldqkxzrkQ', 'Luinf9qY18I', 'lSPuSLWb7eA', 'lXKwd_ZzpHE', 'lsbzvB5PwcA', 'lyWRjsclQtA', 'MeuBD-E1lwo', 'LwQX3G1GwmI', 'm5gx0Bp-eEA', 'LYq17fCeRyE', 'McgfYRTMvlM', 'M1WC2W4_otw', 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'rBV9l78e2V0', 'R9-TAiWRXiM', 'JfZt2W6CsQk', 'Z23PRO9UMa8', 'OBIJKZpH-xM', 'WSYDCgueh6I', 'bifacpvE8ws', '73TsZA6vkqk', 'CmwzNQC6Qtg', 'eblnVqvhc-A', '5TrQVsEbuOY', 'KE2EoAQOBVc', 'hltQg1y1juQ', 'x_puG6U5DjQ', 'kt7zPn-kQF8', 'xszi2PoILho', 'MZeqOM0VK7Y', '9EbT66dfnmM', 'FgM2VRn0Ryo', '5T3EPz5Lj24', 'A3-y_lD5qVk', '6ocRCc_2swQ', 'tpwn_Ul2jEQ', 'bMZDWKhhXvU', 'Dz_gZen6M0k', '3iKj4-PxfZs', 'i0TGJBjem-A', 'u0jitGEbOXg', 'f47uVZd81HQ', '2gi6y21noJo', '6srPzaT83oQ', 'LEAPfyXurg4', 'rhp2v7qkc84', 'tnF3waHFzMY', 'QFdNI8WS4Vk', 'lZuzkkz6vR0', 'z-_1oDfY46Y', 'u0GsTnFcIks', '52Xwef653-E', 'CLvihBtEiW4', 'KmGTVSISQT0', '8E_hzHLMBm8', 'VBAjl8b0b3Q', 'OXhnNrk3Rt8', 'lhhoidqDI4I', '-D-jQ4hJsnU', 'CbQA9HJ-rj0', 'pVyiV_BJMMM', 'eYAPz4c6fAA', 'cTKdrFoNpZg', 'LRGqwZ8Asfw', 'eDT-p5LVvts', 'kQDxEuVnSM4', 'r9rOtHfZCbY', '16gBvwp0TkM', '5RjxhAN-1IM', 'j2KKlMZbXAY', 'nUGj0RZEkFw', 'rq7gUcbi7NM', 'Jw4s0oCa8sE', 'z-VnYZRkLRA', '_pFBYL0EoQs', '_ofrmEoi4iw', 'eRLaKg0NMlc', 'i9pI4mZ9TZ0', 'qm_mzRrbqA0', '67CmHzQo4Eg', '1rrE1J3ez4M', '7BK1HN_cWco', 'bLon8Lb7g0I', 'yrwvYblSli0', 'E5_7TMGrOgg', 'WHrkvXjyxEw', 'O5i8Snxz1Ls', 'iJsJpmYstbI', 'rWHFby9SIjw', '9Kme313pbto', '7Dzwze2RmqI', 'EVfdpzFV47I', 'UF_WOIldXJY', 'GIXTc0lOvlI', '9YQoIn10QkI', '4L4M6eJbeW0', '5f8OT7Da66k', 'HmzNftnan3s', 'FiTl-sUNmHg', 'KmXQKCc0H6U', 'I0muXrXZAtk', 'q_upeT2Dxkc', 'm7ABLXMIPL8', 'Ep2IAOZ1ZIw', 'XSVlTKw6DCU', 'GIZ1KH7j914', 'K7RrWbVvImY', 'xVhr_RPyd2g', 'z5Ph6HiqfNY', '75qrgi_ExCg', 'XVk33hI8CWI', 'yXB8ktNsXhE', 'xEUUuvD6KIo', 'l9JD5dJs4Oc', 'qDHg31u6Z_4', 'lvLd6rgtnq4', 'LNXiNQydGRA', 'I5a9xvqulLs', 'pqXB20_Qtxw', '3WCAIv6jb8A', '6fpBLtprUQk', 'k53KmpZN_Og', 'rzrJ3WZpaik', 'apicb9yOVcI', '92IwzoGVDuw', 'nxKztm08Ojg', 'JYPuKTDJ-M8', 'vJXfhxLOJGw', 'O1kpKior-Uw', 'F7NE5sSmcXY', 'YBE-QEujYOQ', 'owmsijEA7Bw', 'Ym6tLaBjzo0', 'dwfZZMeGCOc', 'oKubKO8dSHg', 'CX7wYbhF6uU', 'S1G9C73ivvQ', 'TSNhazhGoiI']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bReXYBE1NCOp"
      },
      "source": [
        "common_ids=[]\n",
        "youtube=os.listdir('CSV/YouTube')\n",
        "amazon=os.listdir('CSV/Amazon')\n",
        "for i in youtube:\n",
        "  if(i in amazon):\n",
        "    common_ids.append(i)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gPH7kGKIEB3k"
      },
      "source": [
        "#Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFPzLP7EEEfQ"
      },
      "source": [
        "y_dict={}\n",
        "a_dict={}\n",
        "count_am=0\n",
        "count_yt=0\n",
        "for id in common_ids:\n",
        "  yt=str(pd.read_csv('CSV/YouTube/'+id)['Transcript'])\n",
        "  am=str(pd.read_csv('CSV/Amazon/'+id)['Transcript'])\n",
        "  #remove punctuation\n",
        "\n",
        "  yt=yt.split().lower()\n",
        "  am=am.split().lower()\n",
        "  for slur in lex:\n",
        "    if(slur=='you'):\n",
        "      continue\n",
        "    if(slur in yt):\n",
        "      y_dict=update_slurs(yt, y_dict, slur)\n",
        "      count_yt+=1\n",
        "    if(slur in am):\n",
        "      a_dict=update_slurs(am, a_dict, slur)\n",
        "      count_am+=1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mr7I2ZraPYpz",
        "outputId": "c42a1f6d-e7d3-4d43-ec71-9a7e3b5d9fb0"
      },
      "source": [
        "print(y_dict)\n",
        "print(a_dict)\n",
        "print(count_am)\n",
        "print(count_yt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'you': 4894, 'mad': 10, 'slime': 53, 'attack': 16, 'pee': 5, 'boom': 27, 'sex': 1, 'fight': 32, 'screw': 5, 'ass': 9, 'damn': 12, 'randy': 1, 'kill': 18, 'dick': 6, 'sexy': 3, 'jeez': 3, 'smack': 2, 'ho': 17, 'itch': 2, 'weapon': 7, 'bastard': 2, 'doodoo': 1, 'pissing': 1, 'suck': 8, 'sucker': 2, 'cigarette': 1, 'homicide': 1, 'porn': 3, 'aroused': 1, 'shag': 1, 'drunk': 4, 'turd': 2, 'babe': 6, 'stroke': 1, 'hell': 1, 'slave': 2, 'addict': 1, 'virgin': 2, 'crap': 34, 'whiskey': 2, 'liquor': 1, 'honk': 1, 'dong': 1, 'beaver': 3, 'loser': 2, 'vagina': 1, 'doom': 1, 'dope': 3, 'vulva': 1, 'poop': 4, 'pooping': 1, 'limey': 1, 'murder': 1, 'dumb': 3, 'beast': 1, 'amateur': 2, 'poo': 1, 'stupid': 3, 'naked': 1, 'sexual': 1, 'oral': 1, 'scum': 1, 'dink': 1, 'doo-doo': 1, 'whit': 1, 'scat': 1, 'murderer': 1, 'uk': 1, 'diddle': 1, 'killing': 2, 'marijuana': 1, 'pansies': 1, 'suicide': 1, 'bugger': 1, 'goddamn': 1, 'killed': 1, 'nipple': 1, 'torture': 1, 'sperm': 1, 'moles': 2, 'penis': 1, 'tits': 1, 'pocha': 1, 'pros': 1, 'drug': 2, 'racist': 1, 'killer': 1, 'booty': 1}\n",
            "{'you': 1237, 'ho': 8, 'killer': 1, 'ass': 4, 'fuck': 5, 'hell': 9, 'fucking': 4, 'bitch': 1, 'drug': 3, 'shit': 8, 'murder': 1, 'beast': 3, 'dumb': 5, 'boom': 7, 'randy': 1, 'booty': 1, 'jeez': 3, 'stupid': 1, 'fucks': 1, 'mad': 3, 'slime': 6, 'fucker': 1, 'crap': 1, 'tang': 1, 'fuckers': 1, 'racist': 1, 'babe': 1, 'dope': 1, 'squaw': 1, 'dick': 2, 'kill': 2, 'damn': 1, 'killing': 1, 'goddamn': 2, 'panties': 1, 'anal': 1, 'doom': 1, 'weapon': 1}\n",
            "1332\n",
            "5263\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6t9JDlQVac9n",
        "outputId": "79e1d6e7-dc2e-480f-ad06-058af8ff8fb8"
      },
      "source": [
        "print(\"YouTube slurs\", 5263-4894)\n",
        "print(\"Amazon slurs\", 1332-1237)\n",
        "print(y_dict)\n",
        "print(a_dict)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "YouTube slurs 369\n",
            "Amazon slurs 95\n",
            "{'mad': 10, 'slime': 53, 'attack': 16, 'pee': 5, 'boom': 27, 'sex': 1, 'fight': 32, 'screw': 5, 'ass': 9, 'damn': 12, 'randy': 1, 'kill': 18, 'dick': 6, 'sexy': 3, 'jeez': 3, 'smack': 2, 'ho': 17, 'itch': 2, 'weapon': 7, 'bastard': 2, 'doodoo': 1, 'pissing': 1, 'suck': 8, 'sucker': 2, 'cigarette': 1, 'homicide': 1, 'porn': 3, 'aroused': 1, 'shag': 1, 'drunk': 4, 'turd': 2, 'babe': 6, 'stroke': 1, 'hell': 1, 'slave': 2, 'addict': 1, 'virgin': 2, 'crap': 34, 'whiskey': 2, 'liquor': 1, 'honk': 1, 'dong': 1, 'beaver': 3, 'loser': 2, 'vagina': 1, 'doom': 1, 'dope': 3, 'vulva': 1, 'poop': 4, 'pooping': 1, 'limey': 1, 'murder': 1, 'dumb': 3, 'beast': 1, 'amateur': 2, 'poo': 1, 'stupid': 3, 'naked': 1, 'sexual': 1, 'oral': 1, 'scum': 1, 'dink': 1, 'doo-doo': 1, 'whit': 1, 'scat': 1, 'murderer': 1, 'uk': 1, 'diddle': 1, 'killing': 2, 'marijuana': 1, 'pansies': 1, 'suicide': 1, 'bugger': 1, 'goddamn': 1, 'killed': 1, 'nipple': 1, 'torture': 1, 'sperm': 1, 'moles': 2, 'penis': 1, 'tits': 1, 'pocha': 1, 'pros': 1, 'drug': 2, 'racist': 1, 'killer': 1, 'booty': 1}\n",
            "{'ho': 8, 'killer': 1, 'ass': 4, 'fuck': 5, 'hell': 9, 'fucking': 4, 'bitch': 1, 'drug': 3, 'shit': 8, 'murder': 1, 'beast': 3, 'dumb': 5, 'boom': 7, 'randy': 1, 'booty': 1, 'jeez': 3, 'stupid': 1, 'fucks': 1, 'mad': 3, 'slime': 6, 'fucker': 1, 'crap': 1, 'tang': 1, 'fuckers': 1, 'racist': 1, 'babe': 1, 'dope': 1, 'squaw': 1, 'dick': 2, 'kill': 2, 'damn': 1, 'killing': 1, 'goddamn': 2, 'panties': 1, 'anal': 1, 'doom': 1, 'weapon': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJhJ_jzEK-4z"
      },
      "source": [
        "def update_slurs(transcript, dictionary, word):\n",
        "  if(word in dictionary.keys()):\n",
        "    dictionary[word]+=1\n",
        "  else:\n",
        "    dictionary[word]=1\n",
        "  return dictionary"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VygswY92-Xbx"
      },
      "source": [
        "#Confidence for matching transcripts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfbeWrw93EoV"
      },
      "source": [
        "df=pd.read_csv(\"CSV/Matched Transcripts.csv\")\n",
        "scores=[]\n",
        "for i in range(len(df)):\n",
        "  scores.append(df['Match Score'][i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zEHVEDKy229s",
        "outputId": "13b47ad3-a041-4e96-86cd-b518f94b8f61"
      },
      "source": [
        "plt.xlim([0-0.05, 1+0.05])\n",
        "plt.hist(scores, bins=20, alpha=0.5)\n",
        "plt.title(f'Plot of confidence scores for matching transcripts')\n",
        "plt.xlabel('Confidence score')\n",
        "plt.ylabel('Frequency')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTU0lIs--ppY"
      },
      "source": [
        "#Slur Analysis (Old)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64PN6rGn8_DZ",
        "outputId": "927db468-7a9e-4687-ea5a-15a19e529229"
      },
      "source": [
        "count=0\n",
        "ratio=[]\n",
        "for f in os.listdir(\"Channels/New Trans/\"):\n",
        "      print(f)\n",
        "      df=pd.read_csv(\"Channels/New Trans/\"+f)\n",
        "      count=0\n",
        "      try:\n",
        "        for i in df['Transcript']:\n",
        "          sent_text = nltk.sent_tokenize(i)\n",
        "          for sentence in sent_text:\n",
        "            sent_words=sentence.split()\n",
        "            for word in lex:\n",
        "              if word in sent_words:\n",
        "                count+=1\n",
        "                break\n",
        "      except:\n",
        "        print(\"Error\")\n",
        "      print(\"Number of transcripts where swear words occur: \", count)\n",
        "      print(\"Total number of transcripts:\",len(df))\n",
        "      if(len(df)>0):\n",
        "        print(\"Ratio: \",count/len(df))\n",
        "        ratio.append(count/len(df))\n",
        "      else:\n",
        "        ratio.append(0)\n",
        "      count=0\n",
        "      print('\\n')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "UC7Gf2tZ8coTX2ckTPgn62iQ.csv\n",
            "Number of transcripts where swear words occur:  0\n",
            "Total number of transcripts: 0\n",
            "\n",
            "\n",
            "UC5PYHgAzJ1wLEidB58SK6Xw.csv\n",
            "Number of transcripts where swear words occur:  11\n",
            "Total number of transcripts: 84\n",
            "Ratio:  0.13095238095238096\n",
            "\n",
            "\n",
            "UCax-4TdDh3oetlyh-GoL7UA.csv\n",
            "Number of transcripts where swear words occur:  93\n",
            "Total number of transcripts: 2062\n",
            "Ratio:  0.04510184287099903\n",
            "\n",
            "\n",
            "UC8MR0wSTbzs5Yo7DgP04P.csv\n",
            "Number of transcripts where swear words occur:  27\n",
            "Total number of transcripts: 243\n",
            "Ratio:  0.1111111111111111\n",
            "\n",
            "\n",
            "UC68XdlxjeqSsjoAdR3nw9Vg.csv\n",
            "Number of transcripts where swear words occur:  2\n",
            "Total number of transcripts: 51\n",
            "Ratio:  0.0392156862745098\n",
            "\n",
            "\n",
            "UC5mMf2NE17_MiTL94ihRN2w.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  8\n",
            "Total number of transcripts: 41\n",
            "Ratio:  0.1951219512195122\n",
            "\n",
            "\n",
            "UCadtap8RYMNy8w4K9lXhzpQ.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  3\n",
            "Total number of transcripts: 216\n",
            "Ratio:  0.013888888888888888\n",
            "\n",
            "\n",
            "UC-5E0ZHSkN6hExW4rDCY4vw.csv\n",
            "Number of transcripts where swear words occur:  34\n",
            "Total number of transcripts: 114\n",
            "Ratio:  0.2982456140350877\n",
            "\n",
            "\n",
            "UC0bcaAsU6jN8O9TVuqGCSQQ.csv\n",
            "Number of transcripts where swear words occur:  5\n",
            "Total number of transcripts: 324\n",
            "Ratio:  0.015432098765432098\n",
            "\n",
            "\n",
            "UCFU1IubzLAHnoPA8uRt-SMA.csv\n",
            "Number of transcripts where swear words occur:  8\n",
            "Total number of transcripts: 276\n",
            "Ratio:  0.028985507246376812\n",
            "\n",
            "\n",
            "UCBnZ16ahKA2DZ_T5W0FPUXg.csv\n",
            "Number of transcripts where swear words occur:  29\n",
            "Total number of transcripts: 203\n",
            "Ratio:  0.14285714285714285\n",
            "\n",
            "\n",
            "UCDHU0rh47wAgRbI9iwVmq8g.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  0\n",
            "Total number of transcripts: 111\n",
            "Ratio:  0.0\n",
            "\n",
            "\n",
            "UCJcc5qvHowxRshW9P6YZwKA.csv\n",
            "Number of transcripts where swear words occur:  4\n",
            "Total number of transcripts: 65\n",
            "Ratio:  0.06153846153846154\n",
            "\n",
            "\n",
            "UChGJGhZ9SOOHvBB0Y4DOO_w.csv\n",
            "Number of transcripts where swear words occur:  292\n",
            "Total number of transcripts: 1931\n",
            "Ratio:  0.15121698601760747\n",
            "\n",
            "\n",
            "UCmKsULSe7oGtdGcv3bz-_bw.csv\n",
            "Number of transcripts where swear words occur:  9\n",
            "Total number of transcripts: 121\n",
            "Ratio:  0.0743801652892562\n",
            "\n",
            "\n",
            "UCJplp5SjeGSdVdwsfb9Q7lQ.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  31\n",
            "Total number of transcripts: 575\n",
            "Ratio:  0.05391304347826087\n",
            "\n",
            "\n",
            "UC_8PAD0Qmi6_gpe77S1Atgg.csv\n",
            "Number of transcripts where swear words occur:  170\n",
            "Total number of transcripts: 946\n",
            "Ratio:  0.17970401691331925\n",
            "\n",
            "\n",
            "UCoookXUzPciGrEZEXmh4Jjg.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  16\n",
            "Total number of transcripts: 2444\n",
            "Ratio:  0.006546644844517185\n",
            "\n",
            "\n",
            "UCKcQ7Jo2VAGHiPMfDwzeRUw.csv\n",
            "Number of transcripts where swear words occur:  3\n",
            "Total number of transcripts: 151\n",
            "Ratio:  0.019867549668874173\n",
            "\n",
            "\n",
            "UC_nVcTv8k1SETJ1QFrwz7tA.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  0\n",
            "Total number of transcripts: 43\n",
            "Ratio:  0.0\n",
            "\n",
            "\n",
            "UCzGoN1TCO2uiAQWyiNrGhuw.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  20\n",
            "Total number of transcripts: 231\n",
            "Ratio:  0.08658008658008658\n",
            "\n",
            "\n",
            "UCVHO-W63u8RrXa6BAHxfLtQ.csv\n",
            "Number of transcripts where swear words occur:  6\n",
            "Total number of transcripts: 122\n",
            "Ratio:  0.04918032786885246\n",
            "\n",
            "\n",
            "UCZA2NJWgiWtLQZPfMhhEZUQ.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  2\n",
            "Total number of transcripts: 1368\n",
            "Ratio:  0.0014619883040935672\n",
            "\n",
            "\n",
            "UCtBOJ9bEAoMOY9keGK6p2hQ.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  0\n",
            "Total number of transcripts: 276\n",
            "Ratio:  0.0\n",
            "\n",
            "\n",
            "UCTRNV3t2jxbkZgBjhyDv8UQ.csv\n",
            "Number of transcripts where swear words occur:  0\n",
            "Total number of transcripts: 1\n",
            "Ratio:  0.0\n",
            "\n",
            "\n",
            "UCrZ9CtlECK_Jl92di5fCfSg.csv\n",
            "Error\n",
            "Number of transcripts where swear words occur:  2\n",
            "Total number of transcripts: 238\n",
            "Ratio:  0.008403361344537815\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "lVg1A8-B-5uZ",
        "outputId": "f8a20cfa-e8d8-4a16-fa19-7ff014df81e3"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "c = np.arange(1,len(channels)+1,1)\n",
        "values = ratio\n",
        "\n",
        "fig = plt.figure(figsize = (20, 10))\n",
        "\n",
        "# creating the bar plot\n",
        "plt.bar(c, values, color ='maroon',\n",
        "        width = 0.4)\n",
        "plt.xticks(np.arange(0, len(channels)+1, 1))\n",
        "plt.yticks(np.arange(0, 1, 0.05))\n",
        "\n",
        "plt.xlabel(\"Channels\")\n",
        "plt.ylabel(\"Ratio of Swear Words\")\n",
        "plt.title(\"Ratio of Swear Words in Each Channel\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}