Text_Mining_with_R_Chapter_2_Sentiment_analysis_with_tidy_data.ipynb

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      "name": "Text Mining with R Chapter 2 Sentiment analysis with tidy data.ipynb",
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      "include_colab_link": true
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      "codemirror_mode": "r",
      "file_extension": ".r",
      "mimetype": "text/x-r-source",
      "name": "R",
      "pygments_lexer": "r",
      "version": "3.3.1"
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      "language": "R",
      "name": "ir"
    },
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/atlas-github/Sandbox/blob/master/Text_Mining_with_R_Chapter_2_Sentiment_analysis_with_tidy_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pw0ALD1K_vgX",
        "colab_type": "text"
      },
      "source": [
        "Source: [Text Mining with R Chapter 2 Sentiment analysis with tidy data](https://www.tidytextmining.com/sentiment.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Ou5Jt8YBZIV",
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          "height": 351
        },
        "outputId": "d713f7c6-d9de-4bd6-a8a0-1d7b37c4fd10"
      },
      "source": [
        "install.packages(\"textdata\")\n",
        "library(tidytext)\n",
        "library(textdata)\n",
        "\n",
        "my_lexicon = lexicon_afinn()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Do you want to download:\n",
            " Name: AFINN-111 \n",
            " URL: http:https://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010 \n",
            " License: Open Database License (ODbL) v1.0 \n",
            " Size: 78 KB (cleaned 59 KB) \n",
            " Download mechanism: https \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ERROR",
          "evalue": "ignored",
          "traceback": [
            "Error in menu(choices = c(\"Yes\", \"No\"), title = title): menu() cannot be used non-interactively\nTraceback:\n",
            "1. lexicon_afinn()",
            "2. load_dataset(data_name = \"afinn\", name = \"afinn_111.rds\", dir = dir, \n .     delete = delete, return_path = return_path, clean = clean)",
            "3. printer(data_name)",
            "4. menu(choices = c(\"Yes\", \"No\"), title = title)",
            "5. stop(\"menu() cannot be used non-interactively\")"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJVzbcZLKG-x",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d6d6f597-793c-445a-93cc-2267351ae178"
      },
      "source": [
        "get_sentiments(\"bing\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     word          sentiment\n",
              "1    2-faces       negative \n",
              "2    abnormal      negative \n",
              "3    abolish       negative \n",
              "4    abominable    negative \n",
              "5    abominably    negative \n",
              "6    abominate     negative \n",
              "7    abomination   negative \n",
              "8    abort         negative \n",
              "9    aborted       negative \n",
              "10   aborts        negative \n",
              "11   abound        positive \n",
              "12   abounds       positive \n",
              "13   abrade        negative \n",
              "14   abrasive      negative \n",
              "15   abrupt        negative \n",
              "16   abruptly      negative \n",
              "17   abscond       negative \n",
              "18   absence       negative \n",
              "19   absent-minded negative \n",
              "20   absentee      negative \n",
              "21   absurd        negative \n",
              "22   absurdity     negative \n",
              "23   absurdly      negative \n",
              "24   absurdness    negative \n",
              "25   abundance     positive \n",
              "26   abundant      positive \n",
              "27   abuse         negative \n",
              "28   abused        negative \n",
              "29   abuses        negative \n",
              "30   abusive       negative \n",
              "⋮    ⋮             ⋮        \n",
              "6757 wrestle       negative \n",
              "6758 wretch        negative \n",
              "6759 wretched      negative \n",
              "6760 wretchedly    negative \n",
              "6761 wretchedness  negative \n",
              "6762 wrinkle       negative \n",
              "6763 wrinkled      negative \n",
              "6764 wrinkles      negative \n",
              "6765 wrip          negative \n",
              "6766 wripped       negative \n",
              "6767 wripping      negative \n",
              "6768 writhe        negative \n",
              "6769 wrong         negative \n",
              "6770 wrongful      negative \n",
              "6771 wrongly       negative \n",
              "6772 wrought       negative \n",
              "6773 yawn          negative \n",
              "6774 yay           positive \n",
              "6775 youthful      positive \n",
              "6776 zap           negative \n",
              "6777 zapped        negative \n",
              "6778 zaps          negative \n",
              "6779 zeal          positive \n",
              "6780 zealot        negative \n",
              "6781 zealous       negative \n",
              "6782 zealously     negative \n",
              "6783 zenith        positive \n",
              "6784 zest          positive \n",
              "6785 zippy         positive \n",
              "6786 zombie        negative "
            ],
            "text/latex": "A tibble: 6786 × 2\n\\begin{tabular}{r|ll}\n word & sentiment\\\\\n <chr> & <chr>\\\\\n\\hline\n\t 2-faces       & negative\\\\\n\t abnormal      & negative\\\\\n\t abolish       & negative\\\\\n\t abominable    & negative\\\\\n\t abominably    & negative\\\\\n\t abominate     & negative\\\\\n\t abomination   & negative\\\\\n\t abort         & negative\\\\\n\t aborted       & negative\\\\\n\t aborts        & negative\\\\\n\t abound        & positive\\\\\n\t abounds       & positive\\\\\n\t abrade        & negative\\\\\n\t abrasive      & negative\\\\\n\t abrupt        & negative\\\\\n\t abruptly      & negative\\\\\n\t abscond       & negative\\\\\n\t absence       & negative\\\\\n\t absent-minded & negative\\\\\n\t absentee      & negative\\\\\n\t absurd        & negative\\\\\n\t absurdity     & negative\\\\\n\t absurdly      & negative\\\\\n\t absurdness    & negative\\\\\n\t abundance     & positive\\\\\n\t abundant      & positive\\\\\n\t abuse         & negative\\\\\n\t abused        & negative\\\\\n\t abuses        & negative\\\\\n\t abusive       & negative\\\\\n\t ⋮ & ⋮\\\\\n\t wrestle      & negative\\\\\n\t wretch       & negative\\\\\n\t wretched     & negative\\\\\n\t wretchedly   & negative\\\\\n\t wretchedness & negative\\\\\n\t wrinkle      & negative\\\\\n\t wrinkled     & negative\\\\\n\t wrinkles     & negative\\\\\n\t wrip         & negative\\\\\n\t wripped      & negative\\\\\n\t wripping     & negative\\\\\n\t writhe       & negative\\\\\n\t wrong        & negative\\\\\n\t wrongful     & negative\\\\\n\t wrongly      & negative\\\\\n\t wrought      & negative\\\\\n\t yawn         & negative\\\\\n\t yay          & positive\\\\\n\t youthful     & positive\\\\\n\t zap          & negative\\\\\n\t zapped       & negative\\\\\n\t zaps         & negative\\\\\n\t zeal         & positive\\\\\n\t zealot       & negative\\\\\n\t zealous      & negative\\\\\n\t zealously    & negative\\\\\n\t zenith       & positive\\\\\n\t zest         & positive\\\\\n\t zippy        & positive\\\\\n\t zombie       & negative\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6786 × 2\n\n| word &lt;chr&gt; | sentiment &lt;chr&gt; |\n|---|---|\n| 2-faces       | negative |\n| abnormal      | negative |\n| abolish       | negative |\n| abominable    | negative |\n| abominably    | negative |\n| abominate     | negative |\n| abomination   | negative |\n| abort         | negative |\n| aborted       | negative |\n| aborts        | negative |\n| abound        | positive |\n| abounds       | positive |\n| abrade        | negative |\n| abrasive      | negative |\n| abrupt        | negative |\n| abruptly      | negative |\n| abscond       | negative |\n| absence       | negative |\n| absent-minded | negative |\n| absentee      | negative |\n| absurd        | negative |\n| absurdity     | negative |\n| absurdly      | negative |\n| absurdness    | negative |\n| abundance     | positive |\n| abundant      | positive |\n| abuse         | negative |\n| abused        | negative |\n| abuses        | negative |\n| abusive       | negative |\n| ⋮ | ⋮ |\n| wrestle      | negative |\n| wretch       | negative |\n| wretched     | negative |\n| wretchedly   | negative |\n| wretchedness | negative |\n| wrinkle      | negative |\n| wrinkled     | negative |\n| wrinkles     | negative |\n| wrip         | negative |\n| wripped      | negative |\n| wripping     | negative |\n| writhe       | negative |\n| wrong        | negative |\n| wrongful     | negative |\n| wrongly      | negative |\n| wrought      | negative |\n| yawn         | negative |\n| yay          | positive |\n| youthful     | positive |\n| zap          | negative |\n| zapped       | negative |\n| zaps         | negative |\n| zeal         | positive |\n| zealot       | negative |\n| zealous      | negative |\n| zealously    | negative |\n| zenith       | positive |\n| zest         | positive |\n| zippy        | positive |\n| zombie       | negative |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 6786 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>word</th><th scope=col>sentiment</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>2-faces      </td><td>negative</td></tr>\n",
              "\t<tr><td>abnormal     </td><td>negative</td></tr>\n",
              "\t<tr><td>abolish      </td><td>negative</td></tr>\n",
              "\t<tr><td>abominable   </td><td>negative</td></tr>\n",
              "\t<tr><td>abominably   </td><td>negative</td></tr>\n",
              "\t<tr><td>abominate    </td><td>negative</td></tr>\n",
              "\t<tr><td>abomination  </td><td>negative</td></tr>\n",
              "\t<tr><td>abort        </td><td>negative</td></tr>\n",
              "\t<tr><td>aborted      </td><td>negative</td></tr>\n",
              "\t<tr><td>aborts       </td><td>negative</td></tr>\n",
              "\t<tr><td>abound       </td><td>positive</td></tr>\n",
              "\t<tr><td>abounds      </td><td>positive</td></tr>\n",
              "\t<tr><td>abrade       </td><td>negative</td></tr>\n",
              "\t<tr><td>abrasive     </td><td>negative</td></tr>\n",
              "\t<tr><td>abrupt       </td><td>negative</td></tr>\n",
              "\t<tr><td>abruptly     </td><td>negative</td></tr>\n",
              "\t<tr><td>abscond      </td><td>negative</td></tr>\n",
              "\t<tr><td>absence      </td><td>negative</td></tr>\n",
              "\t<tr><td>absent-minded</td><td>negative</td></tr>\n",
              "\t<tr><td>absentee     </td><td>negative</td></tr>\n",
              "\t<tr><td>absurd       </td><td>negative</td></tr>\n",
              "\t<tr><td>absurdity    </td><td>negative</td></tr>\n",
              "\t<tr><td>absurdly     </td><td>negative</td></tr>\n",
              "\t<tr><td>absurdness   </td><td>negative</td></tr>\n",
              "\t<tr><td>abundance    </td><td>positive</td></tr>\n",
              "\t<tr><td>abundant     </td><td>positive</td></tr>\n",
              "\t<tr><td>abuse        </td><td>negative</td></tr>\n",
              "\t<tr><td>abused       </td><td>negative</td></tr>\n",
              "\t<tr><td>abuses       </td><td>negative</td></tr>\n",
              "\t<tr><td>abusive      </td><td>negative</td></tr>\n",
              "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
              "\t<tr><td>wrestle     </td><td>negative</td></tr>\n",
              "\t<tr><td>wretch      </td><td>negative</td></tr>\n",
              "\t<tr><td>wretched    </td><td>negative</td></tr>\n",
              "\t<tr><td>wretchedly  </td><td>negative</td></tr>\n",
              "\t<tr><td>wretchedness</td><td>negative</td></tr>\n",
              "\t<tr><td>wrinkle     </td><td>negative</td></tr>\n",
              "\t<tr><td>wrinkled    </td><td>negative</td></tr>\n",
              "\t<tr><td>wrinkles    </td><td>negative</td></tr>\n",
              "\t<tr><td>wrip        </td><td>negative</td></tr>\n",
              "\t<tr><td>wripped     </td><td>negative</td></tr>\n",
              "\t<tr><td>wripping    </td><td>negative</td></tr>\n",
              "\t<tr><td>writhe      </td><td>negative</td></tr>\n",
              "\t<tr><td>wrong       </td><td>negative</td></tr>\n",
              "\t<tr><td>wrongful    </td><td>negative</td></tr>\n",
              "\t<tr><td>wrongly     </td><td>negative</td></tr>\n",
              "\t<tr><td>wrought     </td><td>negative</td></tr>\n",
              "\t<tr><td>yawn        </td><td>negative</td></tr>\n",
              "\t<tr><td>yay         </td><td>positive</td></tr>\n",
              "\t<tr><td>youthful    </td><td>positive</td></tr>\n",
              "\t<tr><td>zap         </td><td>negative</td></tr>\n",
              "\t<tr><td>zapped      </td><td>negative</td></tr>\n",
              "\t<tr><td>zaps        </td><td>negative</td></tr>\n",
              "\t<tr><td>zeal        </td><td>positive</td></tr>\n",
              "\t<tr><td>zealot      </td><td>negative</td></tr>\n",
              "\t<tr><td>zealous     </td><td>negative</td></tr>\n",
              "\t<tr><td>zealously   </td><td>negative</td></tr>\n",
              "\t<tr><td>zenith      </td><td>positive</td></tr>\n",
              "\t<tr><td>zest        </td><td>positive</td></tr>\n",
              "\t<tr><td>zippy       </td><td>positive</td></tr>\n",
              "\t<tr><td>zombie      </td><td>negative</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJkfAl-jKYww",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "outputId": "a6a03dbd-580d-4761-f337-30f7c62a5e68"
      },
      "source": [
        "get_sentiments(\"nrc\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Do you want to download:\n",
            " Name: NRC Word-Emotion Association Lexicon \n",
            " URL: http:https://saifmohammad.com/WebPages/lexicons.html \n",
            " License: License required for commercial use. Please contact Saif M. Mohammad (saif.mohammad@nrc-cnrc.gc.ca). \n",
            " Size: 22.8 MB (cleaned 424 KB) \n",
            " Download mechanism: http \n",
            " Citation info:\n",
            "\n",
            "This dataset was published in Saif M. Mohammad and Peter Turney. (2013), ``Crowdsourcing a Word-Emotion Association Lexicon.'' Computational Intelligence, 29(3): 436-465.\n",
            "\n",
            "article{mohammad13,\n",
            "author = {Mohammad, Saif M. and Turney, Peter D.},\n",
            "title = {Crowdsourcing a Word-Emotion Association Lexicon},\n",
            "journal = {Computational Intelligence},\n",
            "volume = {29},\n",
            "number = {3},\n",
            "pages = {436-465},\n",
            "doi = {10.1111/j.1467-8640.2012.00460.x},\n",
            "url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.2012.00460.x},\n",
            "eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8640.2012.00460.x},\n",
            "year = {2013}\n",
            "}\n",
            "If you use this lexicon, then please cite it. \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ERROR",
          "evalue": "ignored",
          "traceback": [
            "Error in menu(choices = c(\"Yes\", \"No\"), title = title): menu() cannot be used non-interactively\nTraceback:\n",
            "1. get_sentiments(\"nrc\")",
            "2. textdata::lexicon_nrc()",
            "3. load_dataset(data_name = \"nrc\", name = \"NRCWordEmotion.rds\", \n .     dir = dir, delete = delete, return_path = return_path, clean = clean)",
            "4. printer(data_name)",
            "5. menu(choices = c(\"Yes\", \"No\"), title = title)",
            "6. stop(\"menu() cannot be used non-interactively\")"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wzJjzrlK2LH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fefdea4d-38b3-4eef-8afd-7a6faa800a1d"
      },
      "source": [
        "library(janeaustenr)\n",
        "library(dplyr)\n",
        "library(stringr)\n",
        "\n",
        "tidy_books <- austen_books() %>%\n",
        "  group_by(book) %>%\n",
        "  mutate(linenumber = row_number(),\n",
        "         chapter = cumsum(str_detect(text, regex(\"^chapter [\\\\divxlc]\", \n",
        "                                                 ignore_case = TRUE)))) %>%\n",
        "  ungroup() %>%\n",
        "  unnest_tokens(word, text)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Attaching package: ‘dplyr’\n",
            "\n",
            "The following objects are masked from ‘package:stats’:\n",
            "\n",
            "    filter, lag\n",
            "\n",
            "The following objects are masked from ‘package:base’:\n",
            "\n",
            "    intersect, setdiff, setequal, union\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ME-g5IoQNHVp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "47704d58-15ce-4fd8-e651-792bcea8b562"
      },
      "source": [
        "nrc_joy <- get_sentiments(\"nrc\") %>% \n",
        "  filter(sentiment == \"joy\")\n",
        "\n",
        "tidy_books %>%\n",
        "  filter(book == \"Emma\") %>%\n",
        "  inner_join(nrc_joy) %>%\n",
        "  count(word, sort = TRUE)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Do you want to download:\n",
            " Name: NRC Word-Emotion Association Lexicon \n",
            " URL: http:https://saifmohammad.com/WebPages/lexicons.html \n",
            " License: License required for commercial use. Please contact Saif M. Mohammad (saif.mohammad@nrc-cnrc.gc.ca). \n",
            " Size: 22.8 MB (cleaned 424 KB) \n",
            " Download mechanism: http \n",
            " Citation info:\n",
            "\n",
            "This dataset was published in Saif M. Mohammad and Peter Turney. (2013), ``Crowdsourcing a Word-Emotion Association Lexicon.'' Computational Intelligence, 29(3): 436-465.\n",
            "\n",
            "article{mohammad13,\n",
            "author = {Mohammad, Saif M. and Turney, Peter D.},\n",
            "title = {Crowdsourcing a Word-Emotion Association Lexicon},\n",
            "journal = {Computational Intelligence},\n",
            "volume = {29},\n",
            "number = {3},\n",
            "pages = {436-465},\n",
            "doi = {10.1111/j.1467-8640.2012.00460.x},\n",
            "url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.2012.00460.x},\n",
            "eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8640.2012.00460.x},\n",
            "year = {2013}\n",
            "}\n",
            "If you use this lexicon, then please cite it. \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ERROR",
          "evalue": "ignored",
          "traceback": [
            "Error in menu(choices = c(\"Yes\", \"No\"), title = title): menu() cannot be used non-interactively\nTraceback:\n",
            "1. get_sentiments(\"nrc\") %>% filter(sentiment == \"joy\")",
            "2. eval(lhs, parent, parent)",
            "3. eval(lhs, parent, parent)",
            "4. get_sentiments(\"nrc\")",
            "5. textdata::lexicon_nrc()",
            "6. load_dataset(data_name = \"nrc\", name = \"NRCWordEmotion.rds\", \n .     dir = dir, delete = delete, return_path = return_path, clean = clean)",
            "7. printer(data_name)",
            "8. menu(choices = c(\"Yes\", \"No\"), title = title)",
            "9. stop(\"menu() cannot be used non-interactively\")"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJTxsVjKNLjC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "590289df-7a37-48cc-d4ea-944d2e6c535c"
      },
      "source": [
        "library(tidyr)\n",
        "\n",
        "jane_austen_sentiment <- tidy_books %>%\n",
        "  inner_join(get_sentiments(\"bing\")) %>%\n",
        "  count(book, index = linenumber %/% 80, sentiment) %>%\n",
        "  spread(sentiment, n, fill = 0) %>%\n",
        "  mutate(sentiment = positive - negative)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Joining, by = \"word\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81M1QFw2NOVX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "outputId": "19ce74cb-ffc3-4985-e2bd-dce2008e6f0e"
      },
      "source": [
        "library(ggplot2)\n",
        "\n",
        "ggplot(jane_austen_sentiment, aes(index, sentiment, fill = book)) +\n",
        "  geom_col(show.legend = FALSE) +\n",
        "  facet_wrap(~book, ncol = 2, scales = \"free_x\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dIeUbbaWNxmSiNXcXbITFbOvipqLe2OqUo+thfKXYejqJlJ7CI/HYkjUrFL4o6Z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hUOif//yndEivvvrq6NGj7Xb7gAED+vfv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",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xU4E8lNQNQS2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0b5e14cd-fad7-4c3b-96ff-5c086b91b992"
      },
      "source": [
        "pride_prejudice <- tidy_books %>% \n",
        "  filter(book == \"Pride & Prejudice\")\n",
        "\n",
        "pride_prejudice"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       book              linenumber chapter word        \n",
              "1      Pride & Prejudice  1         0       pride       \n",
              "2      Pride & Prejudice  1         0       and         \n",
              "3      Pride & Prejudice  1         0       prejudice   \n",
              "4      Pride & Prejudice  3         0       by          \n",
              "5      Pride & Prejudice  3         0       jane        \n",
              "6      Pride & Prejudice  3         0       austen      \n",
              "7      Pride & Prejudice  7         1       chapter     \n",
              "8      Pride & Prejudice  7         1       1           \n",
              "9      Pride & Prejudice 10         1       it          \n",
              "10     Pride & Prejudice 10         1       is          \n",
              "11     Pride & Prejudice 10         1       a           \n",
              "12     Pride & Prejudice 10         1       truth       \n",
              "13     Pride & Prejudice 10         1       universally \n",
              "14     Pride & Prejudice 10         1       acknowledged\n",
              "15     Pride & Prejudice 10         1       that        \n",
              "16     Pride & Prejudice 10         1       a           \n",
              "17     Pride & Prejudice 10         1       single      \n",
              "18     Pride & Prejudice 10         1       man         \n",
              "19     Pride & Prejudice 10         1       in          \n",
              "20     Pride & Prejudice 10         1       possession  \n",
              "21     Pride & Prejudice 11         1       of          \n",
              "22     Pride & Prejudice 11         1       a           \n",
              "23     Pride & Prejudice 11         1       good        \n",
              "24     Pride & Prejudice 11         1       fortune     \n",
              "25     Pride & Prejudice 11         1       must        \n",
              "26     Pride & Prejudice 11         1       be          \n",
              "27     Pride & Prejudice 11         1       in          \n",
              "28     Pride & Prejudice 11         1       want        \n",
              "29     Pride & Prejudice 11         1       of          \n",
              "30     Pride & Prejudice 11         1       a           \n",
              "⋮      ⋮                 ⋮          ⋮       ⋮           \n",
              "122175 Pride & Prejudice 13028      61      elizabeth   \n",
              "122176 Pride & Prejudice 13028      61      really      \n",
              "122177 Pride & Prejudice 13028      61      loved       \n",
              "122178 Pride & Prejudice 13028      61      them        \n",
              "122179 Pride & Prejudice 13028      61      and         \n",
              "122180 Pride & Prejudice 13028      61      they        \n",
              "122181 Pride & Prejudice 13028      61      were        \n",
              "122182 Pride & Prejudice 13028      61      both        \n",
              "122183 Pride & Prejudice 13028      61      ever        \n",
              "122184 Pride & Prejudice 13029      61      sensible    \n",
              "122185 Pride & Prejudice 13029      61      of          \n",
              "122186 Pride & Prejudice 13029      61      the         \n",
              "122187 Pride & Prejudice 13029      61      warmest     \n",
              "122188 Pride & Prejudice 13029      61      gratitude   \n",
              "122189 Pride & Prejudice 13029      61      towards     \n",
              "122190 Pride & Prejudice 13029      61      the         \n",
              "122191 Pride & Prejudice 13029      61      persons     \n",
              "122192 Pride & Prejudice 13029      61      who         \n",
              "122193 Pride & Prejudice 13029      61      by          \n",
              "122194 Pride & Prejudice 13029      61      bringing    \n",
              "122195 Pride & Prejudice 13030      61      her         \n",
              "122196 Pride & Prejudice 13030      61      into        \n",
              "122197 Pride & Prejudice 13030      61      derbyshire  \n",
              "122198 Pride & Prejudice 13030      61      had         \n",
              "122199 Pride & Prejudice 13030      61      been        \n",
              "122200 Pride & Prejudice 13030      61      the         \n",
              "122201 Pride & Prejudice 13030      61      means       \n",
              "122202 Pride & Prejudice 13030      61      of          \n",
              "122203 Pride & Prejudice 13030      61      uniting     \n",
              "122204 Pride & Prejudice 13030      61      them        "
            ],
            "text/latex": "A tibble: 122204 × 4\n\\begin{tabular}{r|llll}\n book & linenumber & chapter & word\\\\\n <fct> & <int> & <int> & <chr>\\\\\n\\hline\n\t Pride \\& Prejudice &  1 & 0 & pride       \\\\\n\t Pride \\& Prejudice &  1 & 0 & and         \\\\\n\t Pride \\& Prejudice &  1 & 0 & prejudice   \\\\\n\t Pride \\& Prejudice &  3 & 0 & by          \\\\\n\t Pride \\& Prejudice &  3 & 0 & jane        \\\\\n\t Pride \\& Prejudice &  3 & 0 & austen      \\\\\n\t Pride \\& Prejudice &  7 & 1 & chapter     \\\\\n\t Pride \\& Prejudice &  7 & 1 & 1           \\\\\n\t Pride \\& Prejudice & 10 & 1 & it          \\\\\n\t Pride \\& Prejudice & 10 & 1 & is          \\\\\n\t Pride \\& Prejudice & 10 & 1 & a           \\\\\n\t Pride \\& Prejudice & 10 & 1 & truth       \\\\\n\t Pride \\& Prejudice & 10 & 1 & universally \\\\\n\t Pride \\& Prejudice & 10 & 1 & acknowledged\\\\\n\t Pride \\& Prejudice & 10 & 1 & that        \\\\\n\t Pride \\& Prejudice & 10 & 1 & a           \\\\\n\t Pride \\& Prejudice & 10 & 1 & single      \\\\\n\t Pride \\& Prejudice & 10 & 1 & man         \\\\\n\t Pride \\& Prejudice & 10 & 1 & in          \\\\\n\t Pride \\& Prejudice & 10 & 1 & possession  \\\\\n\t Pride \\& Prejudice & 11 & 1 & of          \\\\\n\t Pride \\& Prejudice & 11 & 1 & a           \\\\\n\t Pride \\& Prejudice & 11 & 1 & good        \\\\\n\t Pride \\& Prejudice & 11 & 1 & fortune     \\\\\n\t Pride \\& Prejudice & 11 & 1 & must        \\\\\n\t Pride \\& Prejudice & 11 & 1 & be          \\\\\n\t Pride \\& Prejudice & 11 & 1 & in          \\\\\n\t Pride \\& Prejudice & 11 & 1 & want        \\\\\n\t Pride \\& Prejudice & 11 & 1 & of          \\\\\n\t Pride \\& Prejudice & 11 & 1 & a           \\\\\n\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t Pride \\& Prejudice & 13028 & 61 & elizabeth \\\\\n\t Pride \\& Prejudice & 13028 & 61 & really    \\\\\n\t Pride \\& Prejudice & 13028 & 61 & loved     \\\\\n\t Pride \\& Prejudice & 13028 & 61 & them      \\\\\n\t Pride \\& Prejudice & 13028 & 61 & and       \\\\\n\t Pride \\& Prejudice & 13028 & 61 & they      \\\\\n\t Pride \\& Prejudice & 13028 & 61 & were      \\\\\n\t Pride \\& Prejudice & 13028 & 61 & both      \\\\\n\t Pride \\& Prejudice & 13028 & 61 & ever      \\\\\n\t Pride \\& Prejudice & 13029 & 61 & sensible  \\\\\n\t Pride \\& Prejudice & 13029 & 61 & of        \\\\\n\t Pride \\& Prejudice & 13029 & 61 & the       \\\\\n\t Pride \\& Prejudice & 13029 & 61 & warmest   \\\\\n\t Pride \\& Prejudice & 13029 & 61 & gratitude \\\\\n\t Pride \\& Prejudice & 13029 & 61 & towards   \\\\\n\t Pride \\& Prejudice & 13029 & 61 & the       \\\\\n\t Pride \\& Prejudice & 13029 & 61 & persons   \\\\\n\t Pride \\& Prejudice & 13029 & 61 & who       \\\\\n\t Pride \\& Prejudice & 13029 & 61 & by        \\\\\n\t Pride \\& Prejudice & 13029 & 61 & bringing  \\\\\n\t Pride \\& Prejudice & 13030 & 61 & her       \\\\\n\t Pride \\& Prejudice & 13030 & 61 & into      \\\\\n\t Pride \\& Prejudice & 13030 & 61 & derbyshire\\\\\n\t Pride \\& Prejudice & 13030 & 61 & had       \\\\\n\t Pride \\& Prejudice & 13030 & 61 & been      \\\\\n\t Pride \\& Prejudice & 13030 & 61 & the       \\\\\n\t Pride \\& Prejudice & 13030 & 61 & means     \\\\\n\t Pride \\& Prejudice & 13030 & 61 & of        \\\\\n\t Pride \\& Prejudice & 13030 & 61 & uniting   \\\\\n\t Pride \\& Prejudice & 13030 & 61 & them      \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 122204 × 4\n\n| book &lt;fct&gt; | linenumber &lt;int&gt; | chapter &lt;int&gt; | word &lt;chr&gt; |\n|---|---|---|---|\n| Pride &amp; Prejudice |  1 | 0 | pride        |\n| Pride &amp; Prejudice |  1 | 0 | and          |\n| Pride &amp; Prejudice |  1 | 0 | prejudice    |\n| Pride &amp; Prejudice |  3 | 0 | by           |\n| Pride &amp; Prejudice |  3 | 0 | jane         |\n| Pride &amp; Prejudice |  3 | 0 | austen       |\n| Pride &amp; Prejudice |  7 | 1 | chapter      |\n| Pride &amp; Prejudice |  7 | 1 | 1            |\n| Pride &amp; Prejudice | 10 | 1 | it           |\n| Pride &amp; Prejudice | 10 | 1 | is           |\n| Pride &amp; Prejudice | 10 | 1 | a            |\n| Pride &amp; Prejudice | 10 | 1 | truth        |\n| Pride &amp; Prejudice | 10 | 1 | universally  |\n| Pride &amp; Prejudice | 10 | 1 | acknowledged |\n| Pride &amp; Prejudice | 10 | 1 | that         |\n| Pride &amp; Prejudice | 10 | 1 | a            |\n| Pride &amp; Prejudice | 10 | 1 | single       |\n| Pride &amp; Prejudice | 10 | 1 | man          |\n| Pride &amp; Prejudice | 10 | 1 | in           |\n| Pride &amp; Prejudice | 10 | 1 | possession   |\n| Pride &amp; Prejudice | 11 | 1 | of           |\n| Pride &amp; Prejudice | 11 | 1 | a            |\n| Pride &amp; Prejudice | 11 | 1 | good         |\n| Pride &amp; Prejudice | 11 | 1 | fortune      |\n| Pride &amp; Prejudice | 11 | 1 | must         |\n| Pride &amp; Prejudice | 11 | 1 | be           |\n| Pride &amp; Prejudice | 11 | 1 | in           |\n| Pride &amp; Prejudice | 11 | 1 | want         |\n| Pride &amp; Prejudice | 11 | 1 | of           |\n| Pride &amp; Prejudice | 11 | 1 | a            |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| Pride &amp; Prejudice | 13028 | 61 | elizabeth  |\n| Pride &amp; Prejudice | 13028 | 61 | really     |\n| Pride &amp; Prejudice | 13028 | 61 | loved      |\n| Pride &amp; Prejudice | 13028 | 61 | them       |\n| Pride &amp; Prejudice | 13028 | 61 | and        |\n| Pride &amp; Prejudice | 13028 | 61 | they       |\n| Pride &amp; Prejudice | 13028 | 61 | were       |\n| Pride &amp; Prejudice | 13028 | 61 | both       |\n| Pride &amp; Prejudice | 13028 | 61 | ever       |\n| Pride &amp; Prejudice | 13029 | 61 | sensible   |\n| Pride &amp; Prejudice | 13029 | 61 | of         |\n| Pride &amp; Prejudice | 13029 | 61 | the        |\n| Pride &amp; Prejudice | 13029 | 61 | warmest    |\n| Pride &amp; Prejudice | 13029 | 61 | gratitude  |\n| Pride &amp; Prejudice | 13029 | 61 | towards    |\n| Pride &amp; Prejudice | 13029 | 61 | the        |\n| Pride &amp; Prejudice | 13029 | 61 | persons    |\n| Pride &amp; Prejudice | 13029 | 61 | who        |\n| Pride &amp; Prejudice | 13029 | 61 | by         |\n| Pride &amp; Prejudice | 13029 | 61 | bringing   |\n| Pride &amp; Prejudice | 13030 | 61 | her        |\n| Pride &amp; Prejudice | 13030 | 61 | into       |\n| Pride &amp; Prejudice | 13030 | 61 | derbyshire |\n| Pride &amp; Prejudice | 13030 | 61 | had        |\n| Pride &amp; Prejudice | 13030 | 61 | been       |\n| Pride &amp; Prejudice | 13030 | 61 | the        |\n| Pride &amp; Prejudice | 13030 | 61 | means      |\n| Pride &amp; Prejudice | 13030 | 61 | of         |\n| Pride &amp; Prejudice | 13030 | 61 | uniting    |\n| Pride &amp; Prejudice | 13030 | 61 | them       |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 122204 × 4</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>book</th><th scope=col>linenumber</th><th scope=col>chapter</th><th scope=col>word</th></tr>\n",
              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 1</td><td>0</td><td><span style=white-space:pre-wrap>pride       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 1</td><td>0</td><td><span style=white-space:pre-wrap>and         </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 1</td><td>0</td><td><span style=white-space:pre-wrap>prejudice   </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 3</td><td>0</td><td><span style=white-space:pre-wrap>by          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 3</td><td>0</td><td><span style=white-space:pre-wrap>jane        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 3</td><td>0</td><td><span style=white-space:pre-wrap>austen      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 7</td><td>1</td><td><span style=white-space:pre-wrap>chapter     </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td> 7</td><td>1</td><td><span style=white-space:pre-wrap>1           </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>it          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>is          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>a           </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>truth       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td>universally </td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td>acknowledged</td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>that        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>a           </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>single      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>man         </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>in          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>10</td><td>1</td><td><span style=white-space:pre-wrap>possession  </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>of          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>a           </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>good        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>fortune     </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>must        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>be          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>in          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>want        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>of          </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>11</td><td>1</td><td><span style=white-space:pre-wrap>a           </span></td></tr>\n",
              "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td>elizabeth </td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>really    </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>loved     </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>them      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>and       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>they      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>were      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>both      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13028</td><td>61</td><td><span style=white-space:pre-wrap>ever      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>sensible  </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>of        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>the       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>warmest   </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td>gratitude </td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>towards   </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>the       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>persons   </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>who       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>by        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13029</td><td>61</td><td><span style=white-space:pre-wrap>bringing  </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>her       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>into      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td>derbyshire</td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>had       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>been      </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>the       </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>means     </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>of        </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>uniting   </span></td></tr>\n",
              "\t<tr><td>Pride &amp; Prejudice</td><td>13030</td><td>61</td><td><span style=white-space:pre-wrap>them      </span></td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbnIVLJMNTHS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "outputId": "844ddead-f035-4915-f32e-881a5e7b8575"
      },
      "source": [
        "afinn <- pride_prejudice %>% \n",
        "  inner_join(get_sentiments(\"afinn\")) %>% \n",
        "  group_by(index = linenumber %/% 80) %>% \n",
        "  summarise(sentiment = sum(value)) %>% \n",
        "  mutate(method = \"AFINN\")\n",
        "\n",
        "bing_and_nrc <- bind_rows(pride_prejudice %>% \n",
        "                            inner_join(get_sentiments(\"bing\")) %>%\n",
        "                            mutate(method = \"Bing et al.\"),\n",
        "                          pride_prejudice %>% \n",
        "                            inner_join(get_sentiments(\"nrc\") %>% \n",
        "                                         filter(sentiment %in% c(\"positive\", \n",
        "                                                                 \"negative\"))) %>%\n",
        "                            mutate(method = \"NRC\")) %>%\n",
        "  count(method, index = linenumber %/% 80, sentiment) %>%\n",
        "  spread(sentiment, n, fill = 0) %>%\n",
        "  mutate(sentiment = positive - negative)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Do you want to download:\n",
            " Name: AFINN-111 \n",
            " URL: http:https://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010 \n",
            " License: Open Database License (ODbL) v1.0 \n",
            " Size: 78 KB (cleaned 59 KB) \n",
            " Download mechanism: https \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ERROR",
          "evalue": "ignored",
          "traceback": [
            "Error in menu(choices = c(\"Yes\", \"No\"), title = title): menu() cannot be used non-interactively\nTraceback:\n",
            "1. pride_prejudice %>% inner_join(get_sentiments(\"afinn\")) %>% group_by(index = linenumber%/%80) %>% \n .     summarise(sentiment = sum(value)) %>% mutate(method = \"AFINN\")",
            "2. withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))",
            "3. eval(quote(`_fseq`(`_lhs`)), env, env)",
            "4. eval(quote(`_fseq`(`_lhs`)), env, env)",
            "5. `_fseq`(`_lhs`)",
            "6. freduce(value, `_function_list`)",
            "7. function_list[[i]](value)",
            "8. inner_join(., get_sentiments(\"afinn\"))",
            "9. inner_join.tbl_df(., get_sentiments(\"afinn\"))",
            "10. check_valid_names(tbl_vars(y))",
            "11. tbl_vars(y)",
            "12. new_sel_vars(tbl_vars_dispatch(x), group_vars(x))",
            "13. structure(vars, groups = group_vars, class = c(\"dplyr_sel_vars\", \n  .     \"character\"))",
            "14. tbl_vars_dispatch(x)",
            "15. get_sentiments(\"afinn\")",
            "16. textdata::lexicon_afinn()",
            "17. load_dataset(data_name = \"afinn\", name = \"afinn_111.rds\", dir = dir, \n  .     delete = delete, return_path = return_path, clean = clean)",
            "18. printer(data_name)",
            "19. menu(choices = c(\"Yes\", \"No\"), title = title)",
            "20. stop(\"menu() cannot be used non-interactively\")"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SphlKqogNVZZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "f0ef4fd6-e840-4709-96fa-4f09f3f43cf1"
      },
      "source": [
        "bind_rows(afinn, \n",
        "          bing_and_nrc) %>%\n",
        "  ggplot(aes(index, sentiment, fill = method)) +\n",
        "  geom_col(show.legend = FALSE) +\n",
        "  facet_wrap(~method, ncol = 1, scales = \"free_y\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ERROR",
          "evalue": "ignored",
          "traceback": [
            "Error in dots_values(...): object 'afinn' not found\nTraceback:\n",
            "1. bind_rows(afinn, bing_and_nrc) %>% ggplot(aes(index, sentiment, \n .     fill = method))",
            "2. eval(lhs, parent, parent)",
            "3. eval(lhs, parent, parent)",
            "4. bind_rows(afinn, bing_and_nrc)",
            "5. flatten_bindable(dots_values(...))",
            "6. dots_values(...)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2F8TJrJzNXhC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "outputId": "a27eebee-b979-4f03-db8a-87743edc77b3"
      },
      "source": [
        "get_sentiments(\"nrc\") %>% \n",
        "     filter(sentiment %in% c(\"positive\", \n",
        "                             \"negative\")) %>% \n",
        "  count(sentiment)\n",
        "\n",
        "get_sentiments(\"bing\") %>% \n",
        "  count(sentiment)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Do you want to download:\n",
            " Name: NRC Word-Emotion Association Lexicon \n",
            " URL: http:https://saifmohammad.com/WebPages/lexicons.html \n",
            " License: License required for commercial use. Please contact Saif M. Mohammad (saif.mohammad@nrc-cnrc.gc.ca). \n",
            " Size: 22.8 MB (cleaned 424 KB) \n",
            " Download mechanism: http \n",
            " Citation info:\n",
            "\n",
            "This dataset was published in Saif M. Mohammad and Peter Turney. (2013), ``Crowdsourcing a Word-Emotion Association Lexicon.'' Computational Intelligence, 29(3): 436-465.\n",
            "\n",
            "article{mohammad13,\n",
            "author = {Mohammad, Saif M. and Turney, Peter D.},\n",
            "title = {Crowdsourcing a Word-Emotion Association Lexicon},\n",
            "journal = {Computational Intelligence},\n",
            "volume = {29},\n",
            "number = {3},\n",
            "pages = {436-465},\n",
            "doi = {10.1111/j.1467-8640.2012.00460.x},\n",
            "url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8640.2012.00460.x},\n",
            "eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-8640.2012.00460.x},\n",
            "year = {2013}\n",
            "}\n",
            "If you use this lexicon, then please cite it. \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ERROR",
          "evalue": "ignored",
          "traceback": [
            "Error in menu(choices = c(\"Yes\", \"No\"), title = title): menu() cannot be used non-interactively\nTraceback:\n",
            "1. get_sentiments(\"nrc\") %>% filter(sentiment %in% c(\"positive\", \n .     \"negative\")) %>% count(sentiment)",
            "2. eval(lhs, parent, parent)",
            "3. eval(lhs, parent, parent)",
            "4. get_sentiments(\"nrc\")",
            "5. textdata::lexicon_nrc()",
            "6. load_dataset(data_name = \"nrc\", name = \"NRCWordEmotion.rds\", \n .     dir = dir, delete = delete, return_path = return_path, clean = clean)",
            "7. printer(data_name)",
            "8. menu(choices = c(\"Yes\", \"No\"), title = title)",
            "9. stop(\"menu() cannot be used non-interactively\")"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GcpQ8YlPNZJc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5dde5c43-7c39-484c-edf2-a6cf2518e076"
      },
      "source": [
        "bing_word_counts <- tidy_books %>%\n",
        "  inner_join(get_sentiments(\"bing\")) %>%\n",
        "  count(word, sentiment, sort = TRUE) %>%\n",
        "  ungroup()\n",
        "\n",
        "bing_word_counts"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Joining, by = \"word\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     word            sentiment n   \n",
              "1    miss            negative  1855\n",
              "2    well            positive  1523\n",
              "3    good            positive  1380\n",
              "4    great           positive   981\n",
              "5    like            positive   725\n",
              "6    better          positive   639\n",
              "7    enough          positive   613\n",
              "8    happy           positive   534\n",
              "9    love            positive   495\n",
              "10   pleasure        positive   462\n",
              "11   poor            negative   424\n",
              "12   happiness       positive   369\n",
              "13   right           positive   329\n",
              "14   best            positive   323\n",
              "15   comfort         positive   292\n",
              "16   doubt           negative   281\n",
              "17   affection       positive   272\n",
              "18   perfectly       positive   271\n",
              "19   glad            positive   263\n",
              "20   agreeable       positive   242\n",
              "21   pretty          positive   242\n",
              "22   object          negative   233\n",
              "23   ready           positive   226\n",
              "24   sorry           negative   219\n",
              "25   fine            positive   216\n",
              "26   impossible      negative   215\n",
              "27   regard          positive   214\n",
              "28   wonder          positive   206\n",
              "29   kindness        positive   200\n",
              "30   afraid          negative   198\n",
              "⋮    ⋮               ⋮         ⋮   \n",
              "2556 unfulfilled     negative  1   \n",
              "2557 ungovernable    negative  1   \n",
              "2558 unproductive    negative  1   \n",
              "2559 unquestionable  positive  1   \n",
              "2560 unselfish       positive  1   \n",
              "2561 unsettle        negative  1   \n",
              "2562 unsophisticated negative  1   \n",
              "2563 unsuccessful    negative  1   \n",
              "2564 unyielding      negative  1   \n",
              "2565 upbraid         negative  1   \n",
              "2566 useable         positive  1   \n",
              "2567 vehement        negative  1   \n",
              "2568 vehemently      negative  1   \n",
              "2569 vexing          negative  1   \n",
              "2570 villainous      negative  1   \n",
              "2571 villains        negative  1   \n",
              "2572 virulence       negative  1   \n",
              "2573 volatility      negative  1   \n",
              "2574 vulnerable      negative  1   \n",
              "2575 warped          negative  1   \n",
              "2576 wary            negative  1   \n",
              "2577 wasteful        negative  1   \n",
              "2578 weep            negative  1   \n",
              "2579 whine           negative  1   \n",
              "2580 winner          positive  1   \n",
              "2581 winning         positive  1   \n",
              "2582 wins            positive  1   \n",
              "2583 worries         negative  1   \n",
              "2584 wounds          negative  1   \n",
              "2585 wreck           negative  1   "
            ],
            "text/latex": "A tibble: 2585 × 3\n\\begin{tabular}{r|lll}\n word & sentiment & n\\\\\n <chr> & <chr> & <int>\\\\\n\\hline\n\t miss       & negative & 1855\\\\\n\t well       & positive & 1523\\\\\n\t good       & positive & 1380\\\\\n\t great      & positive &  981\\\\\n\t like       & positive &  725\\\\\n\t better     & positive &  639\\\\\n\t enough     & positive &  613\\\\\n\t happy      & positive &  534\\\\\n\t love       & positive &  495\\\\\n\t pleasure   & positive &  462\\\\\n\t poor       & negative &  424\\\\\n\t happiness  & positive &  369\\\\\n\t right      & positive &  329\\\\\n\t best       & positive &  323\\\\\n\t comfort    & positive &  292\\\\\n\t doubt      & negative &  281\\\\\n\t affection  & positive &  272\\\\\n\t perfectly  & positive &  271\\\\\n\t glad       & positive &  263\\\\\n\t agreeable  & positive &  242\\\\\n\t pretty     & positive &  242\\\\\n\t object     & negative &  233\\\\\n\t ready      & positive &  226\\\\\n\t sorry      & negative &  219\\\\\n\t fine       & positive &  216\\\\\n\t impossible & negative &  215\\\\\n\t regard     & positive &  214\\\\\n\t wonder     & positive &  206\\\\\n\t kindness   & positive &  200\\\\\n\t afraid     & negative &  198\\\\\n\t ⋮ & ⋮ & ⋮\\\\\n\t unfulfilled     & negative & 1\\\\\n\t ungovernable    & negative & 1\\\\\n\t unproductive    & negative & 1\\\\\n\t unquestionable  & positive & 1\\\\\n\t unselfish       & positive & 1\\\\\n\t unsettle        & negative & 1\\\\\n\t unsophisticated & negative & 1\\\\\n\t unsuccessful    & negative & 1\\\\\n\t unyielding      & negative & 1\\\\\n\t upbraid         & negative & 1\\\\\n\t useable         & positive & 1\\\\\n\t vehement        & negative & 1\\\\\n\t vehemently      & negative & 1\\\\\n\t vexing          & negative & 1\\\\\n\t villainous      & negative & 1\\\\\n\t villains        & negative & 1\\\\\n\t virulence       & negative & 1\\\\\n\t volatility      & negative & 1\\\\\n\t vulnerable      & negative & 1\\\\\n\t warped          & negative & 1\\\\\n\t wary            & negative & 1\\\\\n\t wasteful        & negative & 1\\\\\n\t weep            & negative & 1\\\\\n\t whine           & negative & 1\\\\\n\t winner          & positive & 1\\\\\n\t winning         & positive & 1\\\\\n\t wins            & positive & 1\\\\\n\t worries         & negative & 1\\\\\n\t wounds          & negative & 1\\\\\n\t wreck           & negative & 1\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 2585 × 3\n\n| word &lt;chr&gt; | sentiment &lt;chr&gt; | n &lt;int&gt; |\n|---|---|---|\n| miss       | negative | 1855 |\n| well       | positive | 1523 |\n| good       | positive | 1380 |\n| great      | positive |  981 |\n| like       | positive |  725 |\n| better     | positive |  639 |\n| enough     | positive |  613 |\n| happy      | positive |  534 |\n| love       | positive |  495 |\n| pleasure   | positive |  462 |\n| poor       | negative |  424 |\n| happiness  | positive |  369 |\n| right      | positive |  329 |\n| best       | positive |  323 |\n| comfort    | positive |  292 |\n| doubt      | negative |  281 |\n| affection  | positive |  272 |\n| perfectly  | positive |  271 |\n| glad       | positive |  263 |\n| agreeable  | positive |  242 |\n| pretty     | positive |  242 |\n| object     | negative |  233 |\n| ready      | positive |  226 |\n| sorry      | negative |  219 |\n| fine       | positive |  216 |\n| impossible | negative |  215 |\n| regard     | positive |  214 |\n| wonder     | positive |  206 |\n| kindness   | positive |  200 |\n| afraid     | negative |  198 |\n| ⋮ | ⋮ | ⋮ |\n| unfulfilled     | negative | 1 |\n| ungovernable    | negative | 1 |\n| unproductive    | negative | 1 |\n| unquestionable  | positive | 1 |\n| unselfish       | positive | 1 |\n| unsettle        | negative | 1 |\n| unsophisticated | negative | 1 |\n| unsuccessful    | negative | 1 |\n| unyielding      | negative | 1 |\n| upbraid         | negative | 1 |\n| useable         | positive | 1 |\n| vehement        | negative | 1 |\n| vehemently      | negative | 1 |\n| vexing          | negative | 1 |\n| villainous      | negative | 1 |\n| villains        | negative | 1 |\n| virulence       | negative | 1 |\n| volatility      | negative | 1 |\n| vulnerable      | negative | 1 |\n| warped          | negative | 1 |\n| wary            | negative | 1 |\n| wasteful        | negative | 1 |\n| weep            | negative | 1 |\n| whine           | negative | 1 |\n| winner          | positive | 1 |\n| winning         | positive | 1 |\n| wins            | positive | 1 |\n| worries         | negative | 1 |\n| wounds          | negative | 1 |\n| wreck           | negative | 1 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 2585 × 3</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>word</th><th scope=col>sentiment</th><th scope=col>n</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>miss      </td><td>negative</td><td>1855</td></tr>\n",
              "\t<tr><td>well      </td><td>positive</td><td>1523</td></tr>\n",
              "\t<tr><td>good      </td><td>positive</td><td>1380</td></tr>\n",
              "\t<tr><td>great     </td><td>positive</td><td> 981</td></tr>\n",
              "\t<tr><td>like      </td><td>positive</td><td> 725</td></tr>\n",
              "\t<tr><td>better    </td><td>positive</td><td> 639</td></tr>\n",
              "\t<tr><td>enough    </td><td>positive</td><td> 613</td></tr>\n",
              "\t<tr><td>happy     </td><td>positive</td><td> 534</td></tr>\n",
              "\t<tr><td>love      </td><td>positive</td><td> 495</td></tr>\n",
              "\t<tr><td>pleasure  </td><td>positive</td><td> 462</td></tr>\n",
              "\t<tr><td>poor      </td><td>negative</td><td> 424</td></tr>\n",
              "\t<tr><td>happiness </td><td>positive</td><td> 369</td></tr>\n",
              "\t<tr><td>right     </td><td>positive</td><td> 329</td></tr>\n",
              "\t<tr><td>best      </td><td>positive</td><td> 323</td></tr>\n",
              "\t<tr><td>comfort   </td><td>positive</td><td> 292</td></tr>\n",
              "\t<tr><td>doubt     </td><td>negative</td><td> 281</td></tr>\n",
              "\t<tr><td>affection </td><td>positive</td><td> 272</td></tr>\n",
              "\t<tr><td>perfectly </td><td>positive</td><td> 271</td></tr>\n",
              "\t<tr><td>glad      </td><td>positive</td><td> 263</td></tr>\n",
              "\t<tr><td>agreeable </td><td>positive</td><td> 242</td></tr>\n",
              "\t<tr><td>pretty    </td><td>positive</td><td> 242</td></tr>\n",
              "\t<tr><td>object    </td><td>negative</td><td> 233</td></tr>\n",
              "\t<tr><td>ready     </td><td>positive</td><td> 226</td></tr>\n",
              "\t<tr><td>sorry     </td><td>negative</td><td> 219</td></tr>\n",
              "\t<tr><td>fine      </td><td>positive</td><td> 216</td></tr>\n",
              "\t<tr><td>impossible</td><td>negative</td><td> 215</td></tr>\n",
              "\t<tr><td>regard    </td><td>positive</td><td> 214</td></tr>\n",
              "\t<tr><td>wonder    </td><td>positive</td><td> 206</td></tr>\n",
              "\t<tr><td>kindness  </td><td>positive</td><td> 200</td></tr>\n",
              "\t<tr><td>afraid    </td><td>negative</td><td> 198</td></tr>\n",
              "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
              "\t<tr><td>unfulfilled    </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>ungovernable   </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>unproductive   </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>unquestionable </td><td>positive</td><td>1</td></tr>\n",
              "\t<tr><td>unselfish      </td><td>positive</td><td>1</td></tr>\n",
              "\t<tr><td>unsettle       </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>unsophisticated</td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>unsuccessful   </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>unyielding     </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>upbraid        </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>useable        </td><td>positive</td><td>1</td></tr>\n",
              "\t<tr><td>vehement       </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>vehemently     </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>vexing         </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>villainous     </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>villains       </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>virulence      </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>volatility     </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>vulnerable     </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>warped         </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>wary           </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>wasteful       </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>weep           </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>whine          </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>winner         </td><td>positive</td><td>1</td></tr>\n",
              "\t<tr><td>winning        </td><td>positive</td><td>1</td></tr>\n",
              "\t<tr><td>wins           </td><td>positive</td><td>1</td></tr>\n",
              "\t<tr><td>worries        </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>wounds         </td><td>negative</td><td>1</td></tr>\n",
              "\t<tr><td>wreck          </td><td>negative</td><td>1</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hXD33e__NbEn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "outputId": "9b7f0568-6b96-460d-b43b-57c5fddb220e"
      },
      "source": [
        "bing_word_counts %>%\n",
        "  group_by(sentiment) %>%\n",
        "  top_n(10) %>%\n",
        "  ungroup() %>%\n",
        "  mutate(word = reorder(word, n)) %>%\n",
        "  ggplot(aes(word, n, fill = sentiment)) +\n",
        "  geom_col(show.legend = FALSE) +\n",
        "  facet_wrap(~sentiment, scales = \"free_y\") +\n",
        "  labs(y = \"Contribution to sentiment\",\n",
        "       x = NULL) +\n",
        "  coord_flip()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Selecting by n\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EiagAQkMwgkTUACkhkEkiYgAckMAkkTkIBkBoGkCUhAMoNA0gQkIJlBIGkCEpDM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\nSUVORK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwo-cyiENcta",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "31213b5b-cdf6-4c3c-c8c7-a739511955cb"
      },
      "source": [
        "custom_stop_words <- bind_rows(tibble(word = c(\"miss\"), \n",
        "                                          lexicon = c(\"custom\")), \n",
        "                               stop_words)\n",
        "\n",
        "custom_stop_words"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     word        lexicon\n",
              "1    miss        custom \n",
              "2    a           SMART  \n",
              "3    a's         SMART  \n",
              "4    able        SMART  \n",
              "5    about       SMART  \n",
              "6    above       SMART  \n",
              "7    according   SMART  \n",
              "8    accordingly SMART  \n",
              "9    across      SMART  \n",
              "10   actually    SMART  \n",
              "11   after       SMART  \n",
              "12   afterwards  SMART  \n",
              "13   again       SMART  \n",
              "14   against     SMART  \n",
              "15   ain't       SMART  \n",
              "16   all         SMART  \n",
              "17   allow       SMART  \n",
              "18   allows      SMART  \n",
              "19   almost      SMART  \n",
              "20   alone       SMART  \n",
              "21   along       SMART  \n",
              "22   already     SMART  \n",
              "23   also        SMART  \n",
              "24   although    SMART  \n",
              "25   always      SMART  \n",
              "26   am          SMART  \n",
              "27   among       SMART  \n",
              "28   amongst     SMART  \n",
              "29   an          SMART  \n",
              "30   and         SMART  \n",
              "⋮    ⋮           ⋮      \n",
              "1121 went        onix   \n",
              "1122 were        onix   \n",
              "1123 what        onix   \n",
              "1124 when        onix   \n",
              "1125 where       onix   \n",
              "1126 whether     onix   \n",
              "1127 which       onix   \n",
              "1128 while       onix   \n",
              "1129 who         onix   \n",
              "1130 whole       onix   \n",
              "1131 whose       onix   \n",
              "1132 why         onix   \n",
              "1133 will        onix   \n",
              "1134 with        onix   \n",
              "1135 within      onix   \n",
              "1136 without     onix   \n",
              "1137 work        onix   \n",
              "1138 worked      onix   \n",
              "1139 working     onix   \n",
              "1140 works       onix   \n",
              "1141 would       onix   \n",
              "1142 year        onix   \n",
              "1143 years       onix   \n",
              "1144 yet         onix   \n",
              "1145 you         onix   \n",
              "1146 young       onix   \n",
              "1147 younger     onix   \n",
              "1148 youngest    onix   \n",
              "1149 your        onix   \n",
              "1150 yours       onix   "
            ],
            "text/latex": "A tibble: 1150 × 2\n\\begin{tabular}{r|ll}\n word & lexicon\\\\\n <chr> & <chr>\\\\\n\\hline\n\t miss        & custom\\\\\n\t a           & SMART \\\\\n\t a's         & SMART \\\\\n\t able        & SMART \\\\\n\t about       & SMART \\\\\n\t above       & SMART \\\\\n\t according   & SMART \\\\\n\t accordingly & SMART \\\\\n\t across      & SMART \\\\\n\t actually    & SMART \\\\\n\t after       & SMART \\\\\n\t afterwards  & SMART \\\\\n\t again       & SMART \\\\\n\t against     & SMART \\\\\n\t ain't       & SMART \\\\\n\t all         & SMART \\\\\n\t allow       & SMART \\\\\n\t allows      & SMART \\\\\n\t almost      & SMART \\\\\n\t alone       & SMART \\\\\n\t along       & SMART \\\\\n\t already     & SMART \\\\\n\t also        & SMART \\\\\n\t although    & SMART \\\\\n\t always      & SMART \\\\\n\t am          & SMART \\\\\n\t among       & SMART \\\\\n\t amongst     & SMART \\\\\n\t an          & SMART \\\\\n\t and         & SMART \\\\\n\t ⋮ & ⋮\\\\\n\t went     & onix\\\\\n\t were     & onix\\\\\n\t what     & onix\\\\\n\t when     & onix\\\\\n\t where    & onix\\\\\n\t whether  & onix\\\\\n\t which    & onix\\\\\n\t while    & onix\\\\\n\t who      & onix\\\\\n\t whole    & onix\\\\\n\t whose    & onix\\\\\n\t why      & onix\\\\\n\t will     & onix\\\\\n\t with     & onix\\\\\n\t within   & onix\\\\\n\t without  & onix\\\\\n\t work     & onix\\\\\n\t worked   & onix\\\\\n\t working  & onix\\\\\n\t works    & onix\\\\\n\t would    & onix\\\\\n\t year     & onix\\\\\n\t years    & onix\\\\\n\t yet      & onix\\\\\n\t you      & onix\\\\\n\t young    & onix\\\\\n\t younger  & onix\\\\\n\t youngest & onix\\\\\n\t your     & onix\\\\\n\t yours    & onix\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 1150 × 2\n\n| word &lt;chr&gt; | lexicon &lt;chr&gt; |\n|---|---|\n| miss        | custom |\n| a           | SMART  |\n| a's         | SMART  |\n| able        | SMART  |\n| about       | SMART  |\n| above       | SMART  |\n| according   | SMART  |\n| accordingly | SMART  |\n| across      | SMART  |\n| actually    | SMART  |\n| after       | SMART  |\n| afterwards  | SMART  |\n| again       | SMART  |\n| against     | SMART  |\n| ain't       | SMART  |\n| all         | SMART  |\n| allow       | SMART  |\n| allows      | SMART  |\n| almost      | SMART  |\n| alone       | SMART  |\n| along       | SMART  |\n| already     | SMART  |\n| also        | SMART  |\n| although    | SMART  |\n| always      | SMART  |\n| am          | SMART  |\n| among       | SMART  |\n| amongst     | SMART  |\n| an          | SMART  |\n| and         | SMART  |\n| ⋮ | ⋮ |\n| went     | onix |\n| were     | onix |\n| what     | onix |\n| when     | onix |\n| where    | onix |\n| whether  | onix |\n| which    | onix |\n| while    | onix |\n| who      | onix |\n| whole    | onix |\n| whose    | onix |\n| why      | onix |\n| will     | onix |\n| with     | onix |\n| within   | onix |\n| without  | onix |\n| work     | onix |\n| worked   | onix |\n| working  | onix |\n| works    | onix |\n| would    | onix |\n| year     | onix |\n| years    | onix |\n| yet      | onix |\n| you      | onix |\n| young    | onix |\n| younger  | onix |\n| youngest | onix |\n| your     | onix |\n| yours    | onix |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 1150 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>word</th><th scope=col>lexicon</th></tr>\n",
              "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>miss       </td><td>custom</td></tr>\n",
              "\t<tr><td>a          </td><td>SMART </td></tr>\n",
              "\t<tr><td>a's        </td><td>SMART </td></tr>\n",
              "\t<tr><td>able       </td><td>SMART </td></tr>\n",
              "\t<tr><td>about      </td><td>SMART </td></tr>\n",
              "\t<tr><td>above      </td><td>SMART </td></tr>\n",
              "\t<tr><td>according  </td><td>SMART </td></tr>\n",
              "\t<tr><td>accordingly</td><td>SMART </td></tr>\n",
              "\t<tr><td>across     </td><td>SMART </td></tr>\n",
              "\t<tr><td>actually   </td><td>SMART </td></tr>\n",
              "\t<tr><td>after      </td><td>SMART </td></tr>\n",
              "\t<tr><td>afterwards </td><td>SMART </td></tr>\n",
              "\t<tr><td>again      </td><td>SMART </td></tr>\n",
              "\t<tr><td>against    </td><td>SMART </td></tr>\n",
              "\t<tr><td>ain't      </td><td>SMART </td></tr>\n",
              "\t<tr><td>all        </td><td>SMART </td></tr>\n",
              "\t<tr><td>allow      </td><td>SMART </td></tr>\n",
              "\t<tr><td>allows     </td><td>SMART </td></tr>\n",
              "\t<tr><td>almost     </td><td>SMART </td></tr>\n",
              "\t<tr><td>alone      </td><td>SMART </td></tr>\n",
              "\t<tr><td>along      </td><td>SMART </td></tr>\n",
              "\t<tr><td>already    </td><td>SMART </td></tr>\n",
              "\t<tr><td>also       </td><td>SMART </td></tr>\n",
              "\t<tr><td>although   </td><td>SMART </td></tr>\n",
              "\t<tr><td>always     </td><td>SMART </td></tr>\n",
              "\t<tr><td>am         </td><td>SMART </td></tr>\n",
              "\t<tr><td>among      </td><td>SMART </td></tr>\n",
              "\t<tr><td>amongst    </td><td>SMART </td></tr>\n",
              "\t<tr><td>an         </td><td>SMART </td></tr>\n",
              "\t<tr><td>and        </td><td>SMART </td></tr>\n",
              "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
              "\t<tr><td>went    </td><td>onix</td></tr>\n",
              "\t<tr><td>were    </td><td>onix</td></tr>\n",
              "\t<tr><td>what    </td><td>onix</td></tr>\n",
              "\t<tr><td>when    </td><td>onix</td></tr>\n",
              "\t<tr><td>where   </td><td>onix</td></tr>\n",
              "\t<tr><td>whether </td><td>onix</td></tr>\n",
              "\t<tr><td>which   </td><td>onix</td></tr>\n",
              "\t<tr><td>while   </td><td>onix</td></tr>\n",
              "\t<tr><td>who     </td><td>onix</td></tr>\n",
              "\t<tr><td>whole   </td><td>onix</td></tr>\n",
              "\t<tr><td>whose   </td><td>onix</td></tr>\n",
              "\t<tr><td>why     </td><td>onix</td></tr>\n",
              "\t<tr><td>will    </td><td>onix</td></tr>\n",
              "\t<tr><td>with    </td><td>onix</td></tr>\n",
              "\t<tr><td>within  </td><td>onix</td></tr>\n",
              "\t<tr><td>without </td><td>onix</td></tr>\n",
              "\t<tr><td>work    </td><td>onix</td></tr>\n",
              "\t<tr><td>worked  </td><td>onix</td></tr>\n",
              "\t<tr><td>working </td><td>onix</td></tr>\n",
              "\t<tr><td>works   </td><td>onix</td></tr>\n",
              "\t<tr><td>would   </td><td>onix</td></tr>\n",
              "\t<tr><td>year    </td><td>onix</td></tr>\n",
              "\t<tr><td>years   </td><td>onix</td></tr>\n",
              "\t<tr><td>yet     </td><td>onix</td></tr>\n",
              "\t<tr><td>you     </td><td>onix</td></tr>\n",
              "\t<tr><td>young   </td><td>onix</td></tr>\n",
              "\t<tr><td>younger </td><td>onix</td></tr>\n",
              "\t<tr><td>youngest</td><td>onix</td></tr>\n",
              "\t<tr><td>your    </td><td>onix</td></tr>\n",
              "\t<tr><td>yours   </td><td>onix</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4YhB5BqbNenn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 925
        },
        "outputId": "cbcf1178-8f29-4680-ca7a-5771f854372f"
      },
      "source": [
        "install.packages(\"wordcloud\")\n",
        "library(wordcloud)\n",
        "\n",
        "tidy_books %>%\n",
        "  anti_join(stop_words) %>%\n",
        "  count(word) %>%\n",
        "  with(wordcloud(word, n, max.words = 100))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Installing package into ‘/usr/local/lib/R/site-library’\n",
            "(as ‘lib’ is unspecified)\n",
            "Loading required package: RColorBrewer\n",
            "Joining, by = \"word\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p96bG7+qzG8SO/P3m5HYQZHfhFpD/hmuAQZjEkHeHNVu8QY6PsMoec9IOyN8ZY5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C+k+iLL/t7JMLQDoA0nRTUh2DTIN0qiBk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\nSOD/AUUhYcChowrdAAAAAElFTkSuQmCC",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjGThF-fNgC_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "outputId": "e5ec2903-5cb2-47fe-b427-4e2906538652"
      },
      "source": [
        "library(reshape2)\n",
        "\n",
        "tidy_books %>%\n",
        "  inner_join(get_sentiments(\"bing\")) %>%\n",
        "  count(word, sentiment, sort = TRUE) %>%\n",
        "  acast(word ~ sentiment, value.var = \"n\", fill = 0) %>%\n",
        "  comparison.cloud(colors = c(\"gray20\", \"gray80\"),\n",
        "                   max.words = 100)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Attaching package: ‘reshape2’\n",
            "\n",
            "The following object is masked from ‘package:tidyr’:\n",
            "\n",
            "    smiths\n",
            "\n",
            "Joining, by = \"word\"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BDr+KwE5yqEXUiSz/67dTliUNGm//yT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P82R4hqaaIZEuiQT3B+/aZzMMN4MNcdWU\nLoc5P/kJ3KxMBvDBUx0Isdux/xlkx0VauecdkfhO540yj8TLP+IHtU9SJLVm7jJVaql1a5Ui\n9BL+v87X3WJKsgZtsmqiDPxLlZo501jjlaqaZuXg+rwJEe2+0oDnhVy6od91Am8/dBI9suor\nGnsiXdp5GmEVrSOS/SNq4TW1LedQJTNbpLy9Js9SCBvYj90k+bBkg7sOYoVF4vcH74pwIB2X\nWdkYWdZg+k9pu9lMRph04CPmOvBUlknE/vxsuUiGe94RiV/WzvMThUec+OFJirRjlfgyxart\nDlL9sEq2oIYwd4sp5KmaCLZIwUtbhI18DAhtODQVUHMniE0BwiKWbha6Lh/2VNy8jBueSjUi\nUtFdb5FxRbLv7ZBQHpKWE0xeZA0TF0lyVCg4aYPM7bYPH6NO1K7TbwuLVIHX0bt8DqXj4izn\n/vQbj62TJ3wvTP2ASM75UItUQZEeI5Osokg6HUK3f8yamWyLjZy9Ygo5GWesooNITtkAKvSI\n2EsjyF4FBJgDORsB6u7S3Wasz/dlSXudNAkMiIiUdpdbNFyR5oF7PVrOBrNVcslFsvxIPjZq\nGhdE94GPVRcIUWtLGhUJOp3hu0IcSse1B4+tk4YbNkYaCwdEcs5Tdt4dIykjFOkRshI6Fu3o\ntA8iMTtgZ5JCyysOiuSWDWT70uHxfKN6IFD6IjCFbcugQapXjgq1OSKS5uaBvHRFCt7r4YvU\ncUVqtmzmtEfE2mA8adkN465XlgkoGxYJphFCd4U5mI4riplX0/2GNOuparVwuS+Se74iXriz\ndh2KIj1CBiBQWadT6OK0CeS+Xggrrzgokn+pAZNerIpDDT8Yx617eX85VxJJdSfKOmdEst+k\nSIZ2104m/ryHareTfS8EwRpKwtoVaeGLFLorzOF0XEgMnqJIM7IwuxnZMqW6Mddh+jvP98V0\nij2RtJbhX9owZ3BpNr81m+qh5yFZEtooNSJDxuvaiRBh5Ilkzx+kXM2aZ0SyO10q6/lxkQrO\nWGrH2xn+sgQirW3VK2w41nI3jPFFCtwVYS8dFxKXpygSrUtyY6OqdJYSMwPYY6/Pw+fcYlek\ntpj2yia6mB+wqropilL+YNhBLbyzUUSGqjvZsOb7E02JM+AvcZEK7kx09oxIfHXHmCje9LcG\nz19XUsqiCr+np5AcnZMMaGqlyYK9BeT63mgBkYJ3hdlLx4XE5UmKdA0s0O3kngoTdx/lld6w\n9mQYO5mCWf9QsifH+RLvtcBFuuBz4vwp7mmRKmL5oibCD7IfyFbh0WpZFIbQmKkX7bwyJ0Jr\nUSFqtVXXoXHaySRdr8iXAZGCd0WJpOMK44fOOa+eUCzdOVCkeKh1Y50+HLjpkiUyzNMtND5K\nicqQth/ITkTe1bIkkmPqrNI6F2klkArIp+pnRLqcQYjQkHohQn2ISirBpIF1oYlKZUMvJGlI\ne1lZUDIdaHFWBQXCBCXZFylwV4Chz7Ff0QudQ5GioEjxWGRFpXo6ee82RUgqr7P/QH2LyrDW\nIIBIJaTM+1MXhEiZFMn1+eNa6LTJuQwpnJtsuM1pL8HHLpikNRXm23oFtdUtaJfcG7twQ3q6\nXDH5ctisrt1Jvr77BYqUHDxoVcp2+F/pPRl40KpSdFNGdtOiHaVq9xf7GVFKdyw+J3BHIkWx\nWMu2FFY70mFdzkv2EpI/OoUbUqWGBuvKYUvZrTI9/3aPHBTpXtEip3ZbvX2R5o1SuenG2kED\nrI7sQLo1tcgGGiC7cAMBT80iKxhAE9qoHn/LJwKKdB/YDdr2TFmGnKqSty5Sl/VNWffT2WGT\nx8wxkQx42uqKZBduYNLxMscKuoKqqkrp9Ps+AVCk+4ApkSL7U2823Lm9w9y6SHYXrWvvjM5j\n5pSISE7hBvqezRLkJ378+yzHAkW6F4wEIqYzEnmug6B9bCmcTKsQM9eRBmGRnMINpP3WuzBG\nkkfUquMYCUW6HyzKqiCopdndfoocn9+e2yEaZkFN95vSMCSSU9gn/bRa5VtPzLOaVnvES19j\ngiLF5cCDyhPPLu87hYNJgbr6xWDQ1DonHiUhh0GR4pLZ2+v1UNFD4bBIhEQeJR1nQ4ytHea0\n3V8f+ARBkQJMNbF0maU70pcGdJ2XpDrrsswzotKAuAWx5pVdqqI+sIseKIdFukoPbUtRpAAo\nko8ltcyBnGMjgsLatPSGuYVsDkrLWsESP3FC3bKluLSm4haK7hdSNydRq6mJKeiabfKi2oet\nIbZlhe+lYZ/f5AW1c1ikKA1NS40grENTL5kwg2I6bziFvEXq6XKJN0z2FU8ZFMlnArkW8iAS\nq4Yzkf15HiuU7ti/5ToXyS2bMYngr/d1RTIKYvboSf0Gz2Rk/WJOq9JofQkxsVl1yio4MyaX\nmrOShXM+q81XZSWOSFPFpIsiNZQe3WnDHUTqZTpOIRepYpnpFhPJueL6H/zhgyL5wHJz2gSR\nFvZWlQyLjjKyAl04Zo1bZpWEQte8vkg90ju4EmMMSy1uIpKSg1WKMIWez9M1PFjdCO7yJEj9\nD+c3UGxKcURaiR2YxhvCQr+LAk9VUWk4hVwk9j1dZplIzhXX/+APHxTJpweB2FykJas99lNJ\nuoSM+mVbJLeMlbZT6u7aIrUPbQfOaF1BpH7B/U8ApQlLneCZ7oXCXoCtKWbMpqrLMrQnCl+c\nC8XZWF27WUlmnUQ7eCELsUK0WncKuUhbeNrERHKuiPXBHykoks8Yem4FR6QF1BLDsBchpWyR\n3DJIsW+pgzMiadVLVbQDo3tpSSrBXLlebYkd2BIzFSikHV3QmhbfKrMFIuV41qzFyU3QawX3\nPwEUvtiPz7oRa8i30GCDIUtNTTcbpWWfH4IPNB83J/5Q2DrBC65IdiEXaem0SBjegCIFMMVL\nayg7IkE2/V2xTKfi2mikWbWTO4Zb1k2t6UKa86Lj6HLNNJuQS+iSNNfzjMIuTqUK063RIhsj\nUNgWLhcDqcKGTvrOBJGGPAFRQ+Jh5HKrrohZ9onUi7K4oO2UqDrSjfh/srwhaPGVgSDKlIxX\nAB3z1AyZIiuBlUeCI9KY/3aZOCL1KxZdCztDHlKrOXREcgq5SDVqpi9gjGRfcbOv/2GDIgUY\naVK15Yq0zotyhdXOqqR2Z1KNNoWSW2Y1FEHrUl50HB2WnltMEEuCyzYwdkmLdo5TM1BoSRCo\n2itbtOh07SwVEjyo9ty6Il1auwxro7RUbW40he5mJNeokc3vLP6fAfdFr/Br2Y8wBIg53Rp0\nCXMmMEaawBVDyLkC5/nQyRDjiGRUVE3v81k7rWI4IjmFTKQNhDeUTGfWTqucXq71yEGRAsB6\n8nr5/HXx0HnSx0zG7aPB4CfNs5eASH7h0o229kSiF8y3qZOaVYHCMWtUNBUaTehadVmnLQe9\nOviPJTdhBRMPLlL48nNltBmr7Do9tVjmtCLdCo3tKJsuGvb5tDZdFFXc7ihZUCQfS74wF/LV\nMuefQOd1NQe5vOxRSwa2fIYyEMkvnBG3T+SJtBM6tOqM3ZUKtafatDLkZIGcW7DDrScSbcA+\nE3YuYy6K1WTtZQtWrmcEfVQoQbIJMb/piyX7/DorKJ0nufP4bYIiBZilRfXi/GUxsVsk1gYt\nSZuPWtZBkfzClTep4IlEyxlLcoxWoA0yyCXVajDlZuvXD4gEWh3cR9yEvpZ+pb1f26c2NHIz\nNOQObpX0xEGRbg1dgiVGQtUZBNGVFRTJL7RkUKeXMWkR2hUu0py03eRevEWCgQ2INCc9rp8R\nEInmKkvhUJROJj1ftYQrbfKUFQrDo3FC7rZhKNIBUKRbQ1eLy1UJZszaAmt9LqBX5osUKGyT\ni8VAZh23ijhbO8+RMkLDeRvF3sxizkUyRWgxzQ0NijSQ6gdHPNuSLKYPpSk/waadkuvBxYVr\noZNP1ayNcClP2cfua3ot26XzrK4l1gd+FKBIt4Ze6mmCxicSeilBzEDak4BIfiG91AS1wYoW\nqlBzRLr00gorSn0901inD0SiTbG3nhdU1nqpi639H9akCVfU5SQzlaRH3tGGXFAz1d+yfyC3\ntzCnE6FrKAO6kaOZw580KFKQLenk0/kt3ZGuMoZEU3rV8ArpMqfBIxz75HluEutjpb02Rqm3\nFCG7cUSCzW35Y9ypIvbs/1BaPZhDOYJJphY5H4oxKQmpy5Hubdq3gUlv2ChsDSLxvVy07gCe\nUtcax9/l6YEiBdnB9irlMhvb1yxqyF1q5mpeoaleUiPTtk/G4PoiGYuS6K0ZVOqnLgVMJdYC\n9ZlxXqS2Jlahodl5wVB8huGiALtJuNuGZbp2SFAlzk99KqBIQXYQpzaU2V/vGaVjiMQcKl7h\nCFIZ9HX7ZAyuL1JXSPs/4pxIu0VOi5nv9LxImZ7zVt7k5Qb6mPWKIxLf71LtjvUj9z9dUKQg\nO6g1E4G5snZiWKeCV9jjf4YV++Qt4BkTVuecSJdC9kAoeU8XlLplkX5OTa/qKaXndu22JUXM\nnZzJ24ZmADekSQ2t74i0YWOkERsjyWNq1u44wcT9AkUKsoPGZiDbrvAWiY0G3MKJs23Qcxbp\nWqyYNSu9S0nOtNLyhPZFyxEpUzbMxtEx1bBIh4IQnI/bkG5aqVmOSLSrqrVCl2c8aWDGkwAo\nUpAdbLJaKNuuwGo1M9vwC9kxbXcfhEgLiC9iFV1gH7mWgudQG1skHsBuHQ1b1xc0NVqnAiWY\nJz8eKFKQHfvzq+a3jiuLnK7XTa8QZu204jamSEZVtue0dyVRbgzYH/RQ9Db1XngX2tNzS0ck\n7zpgXZDFzJS1iylRYp/A/dfi25OzBkToprLUqKtiyp23rgrZC/YxYV68kQcdVrZIg8Obx7po\ndCu76e1sUKR4oEhBeIBzrMKzFJXpeiw1WVsmj1Y1HTYADEZvU++Fd6GilucTTbO4SN5pTrqw\nMxuSuSFDa1csUfdfTyQxNTVoLr+1+l4gw7qTYxYJk4hII+Fkh0wzO2VqBfeBQpHigSIFSVCk\nNTRblTS7G8LgUiBSMHrbD+N2L3RDGKYgkn/a/ggG9M4WS2fWzf3XF6kBheBQ1u4WwtJDWinu\ni7Ti62ePNqkNRZzRSuUav+9TB0UKkqBIu0ZaUUSVznmlb4JIwehtP4zbvdAOqtuSLojkn+bM\n8ooskxltCOkGPOZx/vVF6oKCHPtBbk9bUsh3tCcSzWU31qV0dOnQdEVpv3/sLHIUFOl2sNLa\neLOtqHTMm4AuiBSM3vZeeBf6Yd7shR/lDWxENobawOThtlcSIJzb/tcXqQ9LlgLT1lZTFZSa\neUCkbUmScseDe2a9Xu9Sus1v5pGCIt0Oc77IqKAyJeBxS9sRyYveDrxwLqQKhIM7LZIf5Q0M\nwZEhmfEOW1ek7r/gEKSb4yKtefN1ncYzQEtIibr8tDPUXQ8U6XaY8vaD9dg2fIyUdkTyore9\nF96FVIEHwCN2yMdIbpQ3sCAja1Ikg748s4xqmrr/0lSVWjWnRaKF1Moai+EowLFyauOyfdQ1\nzdAeLpO4OijS7bATy9t5uiyurYwyXddTjkh+9Lb7wr8Qwrznuu7M2rnXcS5kqbgri8O2JsjF\nNY9c5f/OdDUzVPu2SLuKLKYiw5t89WpzbhqlsI43ka/gaYEi3RIjTUxP16q0WedEpdUlViR6\n23vhXSi34DnSyn2O5F53IzKdq12fGljZdWj6G4kHinTbmDCgadxN3cwQIUNn9i4AOzLKqrAc\nY5UV9SHNQN+xGs30MpGMrqRiPoergyLdNnl1sh6IyaWCuBKsRbKUC2slD6lBSpaVa1BLa+7G\nwrIDI7L9dVWQ3LyPj2CvDop02+wqiqi3rxzgOYlEaBvkYLrwM0DXDnYBKDYh+pvSVoHOYKuA\n0XonLOhM3vtYg0KWjlCkq4Mi3VNykZQIVxApsJEgiMR3AagzkdgbtHP2VgGMYoM29rZ3amsX\nCm2c2ik/i+YAABgySURBVFkdOQyK9BwxSV+5oNuyLBZ3EH0gKC0mSC+j6gvqFW8KkBMsR4Tw\nSOUKIgU2EmQiLYQhpaWASM5wbahSbW9FkbqjKuv8XfcXfMKgSM8Ri+SWJk1XDaOSpSthbi3V\nsUmyBm2xyu0U01R1u5B7VHFapC3pp5Xslou0Kcr2JoJZUYF/O3aSfufQgNmEzJo56G8kyETq\ngTiaL9JMMJjEc2pJ3f0ZEDZwUsPR30g8UKS41Cveyy3ZbImXStFwY/FGm6N32FikC8ENBrzD\nei7wJsbkuxiRhVcMUQzTmSeSQQomLRa5SLBhYJZvImiu1R7fOHAmbtxDE64sVEPbNjGRJuLa\nqKdKnkhUq23H4hKSmu/njsz2mEiDJ70/yzVBkeISFon6KUk9kdLzo3fY8JwJzoqgCa0IufbG\nyQBBxm7xQLSv9UUas1GPACLxDQNhne6W/VupUZCIfxz70IQrL7NRkWAXgN5EangirbMCz0k3\nI/srzheKLGjyYq8cOQeKFIehppVgcQEk5Gp5LdJQV8sNSDnUT8u5LS0R7dK9wrsjhAUTZ0PR\nPVxfZsUFn0yzyNgtHu6JtIQlEjsmkrNhoGlPH1SoVRbysLGMc8g30eikY28kODgUwGAMu2Oc\ntLsGKFIMduKYriRWVdUONdIdR6SdMGaNQoVV9aplZVg3SZh7V7h3hOEiLaFlsDZ25Gm+bkJ3\nb02WbvEC2rfJMCDSAoLtQKSBv4mgZS8a4hsHuodXE8laqU96P6OkQZFiwLdILVXoCNYXdDOO\nSHwjzELFruqsZQKR3CvcO8JwkWg2vzUbmtXVlnStd0yS3phVzSum6dJ2oXSp1rIjvw2IDmfN\nFBNp7m4iCD84VbE3DtT67uHVRGoeGCEh1wdFikEXogBqFSchl+6I1E3ZpXyM1Cxxkdwr3DvC\n2CJtiqKUX/HdV9SGZZJeWkwtvWK6zYuwS2Vb5FspsXfPbY1siU828A0DS3QqrI1mOu9sHDhz\nDz2R5C44uCHRxPqjva0mWMl27zLkOqBIMeBtT7FCJ07fyhaJ5+0thEVyr3DvOI9JTqfQZiMw\nXcztuEiwYWCZbyKodGZShW8c2PEOPZGaAl8mu6dNam8SgZWgSMmAIsVgy0ZDSxgjKX1qXfQc\nkTbClM6kgEjS2L/CueM850W6TmgQ5S3SlvSLGSZhU9P0AUyGdOg0DZMhzgkoYSLx+NXcHUUD\nPhZQpDj0VbUAe2JCQq7Szp2166l6Ndi1q4k19wrvjrPcpkgG7BubbU8Vky5ZIyUsqCX16Uac\nOieghIkEj2y3wg3X1j51UKSbUbntLRluJhK7t1pbil3ezWPaUJjaTnedE45IpjShl/kEP/NT\nBEW6Nhbrxm3kJHcmCnC91EVBuEjsTWo1Oi3JqaEtUi+byYgd94QtEq2WaBq3DbsZKNL1maRU\nLdaGKtcgUZEYI9Z1Y9qMJdYUpaIizcW5hE9hbwaKdMeoE0o1NoC5LPCNzWqGvZEZiDSTb7Lf\ngy9Sv2LRjbCF50t91aIDue2KxEr4rF1K21tQgVwNFOkWgX1Q9jL+DsPTzbUm3aQVixa6hgxJ\n++v2RmZMpIUyvdkPd0UyKqqm99mgSGyYBTXduRS7jkishIvUOTPjgZwFRbpFDoqkh0NFx2na\na+Tnlrhxt5ExIIx1R6ZqvJC5JBhj3qCbgiLdFNYh03q0p1t0rBh0lpLTU7fQEck+gGc3qaJV\nIlooZZYpGqVxuz1L2RubTSAYaA0iKdI1p+uujpHCsLubgiLdEEPp0y3rhBVaBmtCDGlMx6Lh\nFNoiOQc70qaWNqDRxQv5kWLM862ms7GZ5oo0udCPZuhOlgsFw+5uDIp0Q3hIdqNKd0qxDqsn\n2NHOLbRFcg74DpqF9p5InWyWWnJmZm9slmm6Im1o4QbbotvU3DmELdn6sUDuAiqIvbuUS/4l\nN/1xTxkU6YbYG3yz2tiA+t/NBAttkZyDHVTk4sWeSGvSYs2SZPH+IGxs5om0U3kS7rMz4dGV\nuT5Bkejekt70Ah7O0sAlyLVBkW7I2ElwsFLqeVanIZB1ZTqFtkjOwTGRznJWpPTBWXJYWliu\n0UVOUy/cFmmoq5VazSD9lJzflYh6WRSU8gYWuVfqKNKNQJFuiCGPqFWfWum+xf66G9KQTtkY\nyS50xkj2gSuScNWHQzvS1WVn602++naW0dSGs06iVuUrczNQXgzuPbsTJnQp1kyVfahUzxYJ\nliJOxBqsRDTTFzzSIdWnKFISoEg3BTb4rlkt1hwtpDXM2qUmbqEza2cfuCJVxNbVfsCOp0h1\n1+desgFVl26ZsK5IoGZfsaghBNs6e9lhja807ORskYbQXmZrfCUiuxFFShAU6XnSkSPzB4Hh\njVuR9yr0Dp4r9VV79W0vzRpBNp7KtUMimdKYn/Lhyw4rNXulYdoWqZvmZfZzWhQpUVCk50k6\nuvNQIPHQCZHWbCgmOqtvNTvwVLoIiURrBZoNvTlvkfK1seq+KROJL0XM7YsETSWKdDNQpOSZ\npRV9CN07Pgm3I72iVh0V9TItiUrFLd6SrjLz1to1+V59ulzYcZHsGQKHHcTvsBbJXX07gaR0\nWRCJ1fyyI9JCmIuhx06wtHDBxkjKgFqtgS0S7J45FT2RpAkXyRKmdCOhSDcDRUocmHBYCitD\n7rMhDeQkbjM5aqz3NYNq6xdDrlRhzoY8PboRx+yasmVmayASHw2les777UiVWrmqt/p2oFp0\nJDe4AFvYL1OCpRxpLbKMsK+qxSqftdPKhjNr11X1WrXuilQTG/CJaFfLV6so0s1AkRJnBC2H\nYf/TS9kr8/gf/wFUW78YenVMJD7kyVwygdjrHmRW2dozBG6+0w3pp1XWVLmrb82imm73xC4I\nUIEEKzWxzu4kMUNcS1ec60BigSIlTi/F/+GjfaYN//vPWiNHJL8Y9owAkfiQ55Knb4UTTCR7\nNJS60k+d6OevgaWIa+lGMeXIEVCkxBlBzNzaGPOmJx0VKVTMEw+JrMVKg0gLLiETaXL1/f12\n0d1jDwJLEXHL8lsBRUock42CluIKYlWNdDsqUqgYhjcw5BnKLT6OsrINPkbiMwRxzHC5kG87\ndwRyGhQpeeZpWR/YoXNNKypSqBiGN7DW7rIr9vhQqGjwWTsYDZWdGNO2hPt+PQBQpPtOrD4b\nctegSPeckqDWYWyjdmBqj1JtQGcZXb+gawIBDp0N6cjPbQUgchQU6b6TGlILnkyRFazRmEum\nIY3oTh04Iu0ITmffB1Ck+w4sA4eYBWW8Eza0UbEXDzZLnkjYHt0HUKT7DojUzWYywpgWL6ky\npR1YPHiZ80TCJPj3ARTpvsNEmshrSuUxHWZmqrOcvVnmIqVRpHsCinTfYSL1dYt2pAEbK0F8\nD4yRturIFBZ0KaBI9wQU6b7DRDLzarrflGa0yuOKYNbukvXu9Hy9iCLdE1AkBEkAFAlBEgBF\nQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAUCUESAEVCkARAkRAkAVAkBEkAFAlB\nEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAUCUESAEVCkARAkRAkAVAkBEkA\nFAlBEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAUCUESAEVCkARAkRAkAVAk\nBEkAFAlBEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAUCUESAEVCkARAkRAk\nAVAkBEkAFAlBEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAUCUESAEVCkARA\nkRAkAVAkBEkAFAlBEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAUCUESAEVC\nkARAkRAkAVAkBEkAFAlBEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAUCUES\nAEVCkARAkRAkAVAkBEkAFAlBEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQESQAU\nCUESAEVCkARAkRAkAVAkBEkAFAlBEgBFQpAEQJEQJAFQJARJABQJQRIARUKQBECRECQBUCQE\nSQAUCUESAEVCkARAkRAkAVAkBEkAFAlBEgBFQpAEQJFi0LpbXvaM8ZPXO5sId/39PwRQpBjc\nai09D4r0AECRYnCrtfQ8KNIDAEWKwa3W0vOgSA8AFCkGt1pLz/NxL2P8lHf4D77t295z/Oxt\ncNff/0MARYrBrdbSK/O7nj37/c/3J9719/8QQJFi8Hyr7RmUZyjSPQRFisHzrbZn+HEU6T6C\nIsXg+VbbM/w/KNJ9BEWKwfOttmf4chTpPoIixeD5Vtsz/DYU6T6CIsUgfpWTf/L7vvPt3/1j\nHzt8tvjhD7737S9+z4/+l/rB09mff9/3vvj2d7/v5/UTP+E3nt1cpPpHfuTvvP07v/dDv9yM\nd/1df/8PARQpBqerGUyjPXtWbbWa73/1S57ZfNa7a9HLsu/44lc4Z5/9pq/8iejpj33rZz/z\n+Ly/KQROeY9cpWchvjx89mv4Oxeib1z+JCj/Ou+49IOv+0T3HT7ta34BRUoGFCkGp6tZkddJ\nqaW+NljLX/ivoYtqL/6WsAVf9OHg6dKbXxE+/Ypvrl5VpA/zwh+Ofrwf4cX/1j38h789/CZ/\n8n+gSEmAIsXgdDVr8gr5y+nPDtfQ3/yLgWuyX/Isyst/wD+d/sN7p5+9OndFkZqfAa++Ivrx\n/jco/WznoPbVez/nk38aRUoAFCkGZ+rZy6E+/vuvADleeOObvv71n2TX0N/ud8+yn2sXfebX\nvfMHvv9vfcUn20fvCld2Vt3/4re8/dv/+ht+l330ZXsi6S+88AI/+coXgDeFz/KJ8WevyIQ/\nXJo3de+0DxpfZb/17/zq7/rB9/zNL3klP/i4D7fOcNff/0MARYrBmXrG6+ObmUbfovHj0vd/\nPK+hf8m74i/bnrgDo/y3c/de9nPO8U/w06/7Jffyn7cbqH8SFQl4DxwEJxv8sx/jI7RAQwe8\nl7d+kn3wrbbOP96wD5U38ztelUWRbgyKFIMz9Yxr89Jnn/DPvZKPfAIv+mXn8EO8/n5h2r/l\ng9ykz3Uq9J+Hgy9p+Kfrf5HfsKdK66RIrS+Gl18a/nCvCfT3PsqvfW3RP/shXvJ/nPkF7/r7\nfwigSDE4U89+k91f+qFAEW8Hnn2jc/SH4OATxeA9b+IXfMg+eBW8/tfB00WYEniJtKfKaZF+\ngLdzSvCNfuMlgbbty3l7FJrX+05+y5Hpepe7/v4fAihSDE5XM0ekF4JFtd8JRZ9hH3zkWWCY\n4pD9ZL+lqHMXpND59/617/zhf1faU+W0SDneEH5v8H1ehJJPt+fif5VL9S9DP6fKP+iLp3/B\nu/7+HwIoUgxOVzNHpPeFyuzRyH/jr7+RD1PU8E182PRKrkqVV/CPHn3/2CK1eI/wjwTv5bMc\n32y//r/h9WdF3vxvQeEfPP0L3vX3/xBAkWJwuprZIr08PGL/aS7SB/jrL4CXfyJy0z/lF/wM\nfw1RP8/edPT944v0L+D1S37DP/mf+U9xZjH45/jWyJv/Ir8iYnmEu/7+HwIoUgxO1jJHpC8M\nl+V5/XwbvKzwiYW3R25S+QXfxV9/Ja//72i0DhNfpMbviHQi/0bgo5X55/iRyJvXP25vgLbH\nXX//DwEUKQYna5kj0tdGCj8VCt8Ir37hWWC878MjHb6ev/zX/Ipnn/t3lOhFnPgitd4KB/+L\nd67JxXqvfWC3Pf8h+u6fA6Xfc/IXvOvv/yGAIsXgZC1zRPqOSOEfhMLXwSt78vsj0bs+H0pf\nb7/+JtukZy999f/5E/sPda4g0q/wt/lV99zPwNHHO+/4oWfHefPJX/Cuv/+HAIoUg5O1zBHp\n70UK+TOd18Cr9/Gq+hvRu3hk3hfZr+t/3a/TL3v1W3423Mm7gkitL4Sjb3ePvhaO3OfCf/eE\nSF/XOsVdf/8PARQpBidrmSPSByKFfxwKvwBefQ+vqlr0ri+D0s93jz74e4P1+lXf+N+PqXJG\npO+HI3dmrsLn2N3xz7tPiPRnT/6Cd/39PwRQpBicrGWOSD8WKfyTUPg58Oo7eFVNR++C2Dwv\nmLTVqr7/j74sULNf/tXksCpnRErzuYP/bB/8KLz+ve6io3edEOn1J3/Bu/7+HwIoUgxOe3Si\nRXo1vHonr6p7E8zhFolL8IG//Cq/bn/KvzqoyhmRWm+Aw//Lfs1DVL1+Ho972O9ixuCuv/+H\nAIoUgzP1jIv09yOFfIz0Wnj1fbwC/1r0Lj5G+sPR0o++6/XusruP/49O2ZVE+mdw+Hv4yyy0\nTi/1zOHt07PwMql43PX3/xBAkWJwpp5xkb47UuhPyn2QV+DD085feeDtqj/1Z/gTn2ef5cw5\nXEmkGn+6y9fx/X145cew/iv+pmfXTBzgrr//hwCKFIMz9YyL9KZIIX9M9A3wyn6O9MHoXbzh\n+d8Pv+NH7fVLP2ofXUkkOxz2m+AVb/P8FbP/nb/n3z3zyxzirr//hwCKFIMz9YyL9MfDZQVe\na/nSvQIPpfu2yE3/g1/w3iNvqfHB0tfYB1cTiUcFvYo1ZuSlMNKq+Gc+9YS6J7nr7/8hgCLF\n4Ew94yL91vCzH/4o9NmP89ef5w2XAryfX/Cfjr0nX+36Gvv11USyHwX/W2eS4xsCJ14HBZ9x\n5pc5xF1//w8BFCkGZ+qZHf39M6Gyt0HRS1P8NQ94eykJ3/R6KPw01z4pOqn3j+G0E+tzRZHe\nBQVvcVZB/cfAiXccHKz9XOnMr4cixQFFisGZemaL9MZgUfMzociJW7CD3MJ9O/HlXnshvOV1\nr9oLLeATBU7Whn2Rfl/gyj2RFHjrP9D6dSj/vOB7Kjx9w5eHf9BHn338l70HF/bdGBQpBqer\nmSPSS4PRdB/gRW7E9x+Bg0/49eA9/HHPS/hcdBbq98f9aivEnw2MZ0Kq8NCFTwtcub/RGH8W\n/Gs8kCE8l/jn+KcKxX9X/1co+iunf8G7/v4fAihSDE5XM3ep+Wf6WYN+7VOg4BNTzqEd3f2H\nAsENf5uX/AX74E/x3lqo6/dTXI+fPaDKP+B3BpqQfZH4fPv3QQKwV4Rztv4ST9PyysAa2QKP\nr3j5r5z+Be/6+38IoEgxOF3NbJFYE/O7nenq1k9+Oq/tb/Wu+Cv8+LPdWAXyV/nxp8v24X/l\n3bxP/n9d71r6t/GSF5oHVPlZfutXleF147BIVZh6/2J4izdEPul3223nm50RWf3H/mde8C1n\nfsG7/v4fAihSDOKI9M9hsuz3fO07f+Ddf+MP2C3UF/jZekqvtos+55ve9UPvfstr7ayqn/Rv\n3NPfbp996QtvfOvb3vbWr/5C+3nsK90ghJAqeR5M9+zTvuxPveZTf+6wSK1veOYQTYzcfINd\n/vIvfdM7/va3fNVvtY++9HAqchTpKqBIMYgl0scCcXLAq4J9tewfexbldweSNLxp7+yzZ5/i\nZr2LqPJG/5JjIv2Cc/5/2jOk9lf3f9Abzk7b3fX3/xBAkWIQR6R/1vr1LwpWzz8RzgpUf8dv\nDtXel399aDXsj0Uycj979qf94LywKpIv7DGRWk725Le29vnHvyP8c37bD57fkuKuv/+HAIoU\ngzgi/VPWcfrAa92VEJ//Q3vVM/XO17iJ8l/yud8cjWGtvd8LVmX8vrcEcwpFVPnYH3Wu+qRf\nPHCW86J9wX879Gmr7/tSL2H/K//Y+4qHrolw19//QwBFisGZesZFsnMyZH/6vS++7bv/yZEd\nHoo/88Pvefu73/9Th3P21P6/n3jvd739O97zvg+fSyH8Kz/44tve9YFfirm70T6VX/iH3/W2\nF7//H31kb++Zw9z19/8QQJFicKaeBUR6nNz19/8QQJFicKaeoUgIihSHM/UMRUJQpDicqWco\nEoIixeFMPUOREBQpDmfqGYqEoEhxOFPPUCQERYrDmXqGIiEoUhzO1DMUCUGR4nCmnqFICIqU\nADwc9efu+lMgdwqKhCAJgCIhSAKgSAiSACgSgiQAioQgCYAiIUgCoEgIkgAoEoIkAIqEIAmA\nIiFIAqBICJIAKBKCJACKhCAJgCIhSAKgSAiSACgSgiQAioQgCYAiIUgCoEgIkgAoEoIkAIqE\nIAmAIiFIAqBICJIAKBKCJACKhCAJgCIhSAKgSAiSACgSgiQAioQgCYAiIUgCoEgIkgAoEoIk\nAIqEIAmAIiFIAqBICJIAKBKCJACKhCAJgCIhSAKgSAiSACgSgiQAioQgCYAiIUgC/P9GgSae\nh+2kfAAAAABJRU5ErkJggg==",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhTAbUY2Nh9E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "PandP_sentences <- tibble(text = prideprejudice) %>% \n",
        "  unnest_tokens(sentence, text, token = \"sentences\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1BvV48lNnUm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6d99931e-cc7a-43f2-943f-a1024ef519f0"
      },
      "source": [
        "PandP_sentences$sentence[2]"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] \"however little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters.\""
            ],
            "text/latex": "'however little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters.'",
            "text/markdown": "'however little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters.'",
            "text/html": [
              "'however little known the feelings or views of such a man may be on his first entering a neighbourhood, this truth is so well fixed in the minds of the surrounding families, that he is considered the rightful property of some one or other of their daughters.'"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EXJIJ83lNpWD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b0ba1ca0-0965-4559-b0ec-f9dbf0299c22"
      },
      "source": [
        "austen_chapters <- austen_books() %>%\n",
        "  group_by(book) %>%\n",
        "  unnest_tokens(chapter, text, token = \"regex\", \n",
        "                pattern = \"Chapter|CHAPTER [\\\\dIVXLC]\") %>%\n",
        "  ungroup()\n",
        "\n",
        "austen_chapters %>% \n",
        "  group_by(book) %>% \n",
        "  summarise(chapters = n())"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  book                chapters\n",
              "1 Sense & Sensibility 51      \n",
              "2 Pride & Prejudice   62      \n",
              "3 Mansfield Park      49      \n",
              "4 Emma                56      \n",
              "5 Northanger Abbey    32      \n",
              "6 Persuasion          25      "
            ],
            "text/latex": "A tibble: 6 × 2\n\\begin{tabular}{r|ll}\n book & chapters\\\\\n <fct> & <int>\\\\\n\\hline\n\t Sense \\& Sensibility & 51\\\\\n\t Pride \\& Prejudice   & 62\\\\\n\t Mansfield Park      & 49\\\\\n\t Emma                & 56\\\\\n\t Northanger Abbey    & 32\\\\\n\t Persuasion          & 25\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 2\n\n| book &lt;fct&gt; | chapters &lt;int&gt; |\n|---|---|\n| Sense &amp; Sensibility | 51 |\n| Pride &amp; Prejudice   | 62 |\n| Mansfield Park      | 49 |\n| Emma                | 56 |\n| Northanger Abbey    | 32 |\n| Persuasion          | 25 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 6 × 2</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>book</th><th scope=col>chapters</th></tr>\n",
              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>Sense &amp; Sensibility</td><td>51</td></tr>\n",
              "\t<tr><td><span style=white-space:pre-wrap>Pride &amp; Prejudice  </span></td><td>62</td></tr>\n",
              "\t<tr><td>Mansfield Park     </td><td>49</td></tr>\n",
              "\t<tr><td>Emma               </td><td>56</td></tr>\n",
              "\t<tr><td>Northanger Abbey   </td><td>32</td></tr>\n",
              "\t<tr><td>Persuasion         </td><td>25</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRtTD8-RNrkJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "c3eb5fce-44e5-47bf-e9ce-98b2fcde8c5b"
      },
      "source": [
        "bingnegative <- get_sentiments(\"bing\") %>% \n",
        "  filter(sentiment == \"negative\")\n",
        "\n",
        "wordcounts <- tidy_books %>%\n",
        "  group_by(book, chapter) %>%\n",
        "  summarize(words = n())\n",
        "\n",
        "tidy_books %>%\n",
        "  semi_join(bingnegative) %>%\n",
        "  group_by(book, chapter) %>%\n",
        "  summarize(negativewords = n()) %>%\n",
        "  left_join(wordcounts, by = c(\"book\", \"chapter\")) %>%\n",
        "  mutate(ratio = negativewords/words) %>%\n",
        "  filter(chapter != 0) %>%\n",
        "  top_n(1) %>%\n",
        "  ungroup()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Joining, by = \"word\"\n",
            "Selecting by ratio\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  book                chapter negativewords words ratio     \n",
              "1 Sense & Sensibility 43      161           3405  0.04728341\n",
              "2 Pride & Prejudice   34      111           2104  0.05275665\n",
              "3 Mansfield Park      46      173           3685  0.04694708\n",
              "4 Emma                15      151           3340  0.04520958\n",
              "5 Northanger Abbey    21      149           2982  0.04996647\n",
              "6 Persuasion           4       62           1807  0.03431101"
            ],
            "text/latex": "A tibble: 6 × 5\n\\begin{tabular}{r|lllll}\n book & chapter & negativewords & words & ratio\\\\\n <fct> & <int> & <int> & <int> & <dbl>\\\\\n\\hline\n\t Sense \\& Sensibility & 43 & 161 & 3405 & 0.04728341\\\\\n\t Pride \\& Prejudice   & 34 & 111 & 2104 & 0.05275665\\\\\n\t Mansfield Park      & 46 & 173 & 3685 & 0.04694708\\\\\n\t Emma                & 15 & 151 & 3340 & 0.04520958\\\\\n\t Northanger Abbey    & 21 & 149 & 2982 & 0.04996647\\\\\n\t Persuasion          &  4 &  62 & 1807 & 0.03431101\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA tibble: 6 × 5\n\n| book &lt;fct&gt; | chapter &lt;int&gt; | negativewords &lt;int&gt; | words &lt;int&gt; | ratio &lt;dbl&gt; |\n|---|---|---|---|---|\n| Sense &amp; Sensibility | 43 | 161 | 3405 | 0.04728341 |\n| Pride &amp; Prejudice   | 34 | 111 | 2104 | 0.05275665 |\n| Mansfield Park      | 46 | 173 | 3685 | 0.04694708 |\n| Emma                | 15 | 151 | 3340 | 0.04520958 |\n| Northanger Abbey    | 21 | 149 | 2982 | 0.04996647 |\n| Persuasion          |  4 |  62 | 1807 | 0.03431101 |\n\n",
            "text/html": [
              "<table>\n",
              "<caption>A tibble: 6 × 5</caption>\n",
              "<thead>\n",
              "\t<tr><th scope=col>book</th><th scope=col>chapter</th><th scope=col>negativewords</th><th scope=col>words</th><th scope=col>ratio</th></tr>\n",
              "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><td>Sense &amp; Sensibility</td><td>43</td><td>161</td><td>3405</td><td>0.04728341</td></tr>\n",
              "\t<tr><td><span style=white-space:pre-wrap>Pride &amp; Prejudice  </span></td><td>34</td><td>111</td><td>2104</td><td>0.05275665</td></tr>\n",
              "\t<tr><td>Mansfield Park     </td><td>46</td><td>173</td><td>3685</td><td>0.04694708</td></tr>\n",
              "\t<tr><td>Emma               </td><td>15</td><td>151</td><td>3340</td><td>0.04520958</td></tr>\n",
              "\t<tr><td>Northanger Abbey   </td><td>21</td><td>149</td><td>2982</td><td>0.04996647</td></tr>\n",
              "\t<tr><td>Persuasion         </td><td> 4</td><td> 62</td><td>1807</td><td>0.03431101</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wLiaPEcNto8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}