sentiment analysis.ipynb

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    "colab": {
      "name": "1A_experience_sentiment_analysis.ipynb",
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      "name": "python"
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      "cell_type": "markdown",
      "source": [
        "We started off with importing relevant libraries:"
      ],
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        "id": "P2PHfXjBxre5"
      }
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import nltk\n",
        "import re\n",
        "nltk.download('wordnet')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we grabbed relevant data. The questions we considered analyzing are all related to 1A experiences and advice. These include: \n",
        "\n",
        "*   Given the chance, would you apply to SYDE again?\n",
        "*   How does your current experience with SYDE compare to what you **expected** it to be when you applied?\n",
        "*   What is the biggest piece of advice/wisdom you would give to someone thinking about applying to SYDE?\n",
        "*   If you could describe 1A in one word, what would it be?\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "RHJWYOc9xNLx"
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    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"unordered.csv\")\n",
        "\n",
        "# In our dataset, the columns are named by survey question title\n",
        "\n",
        "rel_cols = ['Given the chance, would you apply to SYDE again?',\n",
        "           'How does your current experience with SYDE compare to what you expected it to be when you applied?',\n",
        "           'What is the biggest piece of advice/wisdom you would give to someone thinking about applying to SYDE?',\n",
        "           'If you could describe 1A in one word, what would it be?']\n",
        "\n",
        "df = data[rel_cols].dropna().rename(columns={'Given the chance, would you apply to SYDE again?' : 'apply_again',\n",
        "           'How does your current experience with SYDE compare to what you expected it to be when you applied?' : 'expected_experience',\n",
        "           'What is the biggest piece of advice/wisdom you would give to someone thinking about applying to SYDE?': 'advice',\n",
        "           'If you could describe 1A in one word, what would it be?':'describe1a'})\n",
        "\n",
        "df"
      ],
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      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   apply_again  expected_experience  \\\n",
              "0          Yes                  4.0   \n",
              "4          Yes                  3.0   \n",
              "5          Yes                  5.0   \n",
              "6          Yes                  4.0   \n",
              "7          Yes                  3.0   \n",
              "..         ...                  ...   \n",
              "80         Yes                  6.0   \n",
              "83         Yes                  3.0   \n",
              "84         Yes                  4.0   \n",
              "90         Yes                  3.0   \n",
              "91         Yes                  6.0   \n",
              "\n",
              "                                               advice     describe1a  \n",
              "0                                    It won't be easy           Hell  \n",
              "4   Try out many different things in high school, ...       dramatic  \n",
              "5                                         Be sociable         Hectic  \n",
              "6   It’s just a program, it won’t make that much o...        Chaotic  \n",
              "7   Any university degree is a marathon, not a spr...     An Ordeal.  \n",
              "..                                                ...            ...  \n",
              "80  If you want the nicest and best co-op communit...          based  \n",
              "83  Grades don't matter as much, so don't stress t...            New  \n",
              "84              It is absolutely what you make of it!  Life-changing  \n",
              "90            Try hard but you still have to have fun            Fun  \n",
              "91  Do you want to have the best balance between y...  Collaborative  \n",
              "\n",
              "[62 rows x 4 columns]"
            ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "To get a better idea of the data we are working with, we generated a rough bar graph."
      ],
      "metadata": {
        "id": "AzHC9GFuyMVt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "apply_df = df['apply_again']\n",
        "apply_df\n",
        "\n",
        "df['apply_again'].value_counts(normalize= True).plot.bar()"
      ],
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          "output_type": "execute_result",
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              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe1c5dc3410>"
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          "execution_count": 4
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are focusing on the questions where students described their 1A experience and where they gave advice to future students. Both questions have 62 responses to work with."
      ],
      "metadata": {
        "id": "MRhF8XhGyau6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df[['describe1a', 'advice']]\n",
        "describe_df = df.groupby('describe1a')['describe1a'].count()\n",
        "df['describe1a'].count()\n",
        "data.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0KCmp7yxTXf",
        "outputId": "098768cd-159a-4e15-9efd-9d66fbe86178"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "describe1a    62\n",
              "advice        62\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Taking a closer look at the types of words we are working with, it is clear that some will be recognizable - like 'TsTsians'. For reference, these are fictional aliens created by our Design professor for our term design projects. "
      ],
      "metadata": {
        "id": "AO4Yj8BHytEp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "describe_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgZovuMYxVOf",
        "outputId": "e65be0c4-96a8-4fb9-a8e7-1f85d2e4b7ca"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "describe1a\n",
              "0/10                                     1\n",
              "Adjustment                               1\n",
              "Adventurous                              1\n",
              "Alcohol                                  1\n",
              "Amazing                                  1\n",
              "An Ordeal.                               1\n",
              "Balanced                                 1\n",
              "Busy                                     1\n",
              "Change                                   1\n",
              "Chaotic                                  1\n",
              "Collaborative                            1\n",
              "Exciting                                 1\n",
              "Exhaustion                               1\n",
              "Eye-opening                              1\n",
              "F U N K Y                                1\n",
              "Fast                                     1\n",
              "Freedom                                  1\n",
              "Fun                                      2\n",
              "Fun socially, depressing academically    1\n",
              "Fun with some difficult times            1\n",
              "Hectic                                   1\n",
              "Hell                                     1\n",
              "Intense                                  2\n",
              "Joyful                                   1\n",
              "Learning Curve                           1\n",
              "Life-changing                            1\n",
              "Lit                                      1\n",
              "Mid                                      1\n",
              "Movie                                    1\n",
              "New                                      3\n",
              "Ok.                                      1\n",
              "Online                                   1\n",
              "Pedantic                                 1\n",
              "People                                   1\n",
              "PoggyWoggy                               1\n",
              "Rollercoaster                            1\n",
              "Rush                                     1\n",
              "Scrambled                                1\n",
              "Shit                                     1\n",
              "TSTSIAN                                  1\n",
              "TsTs                                     1\n",
              "TsTsians                                 2\n",
              "Tstsians                                 1\n",
              "Unexpected                               1\n",
              "based                                    1\n",
              "chaotic and lonely                       1\n",
              "death                                    1\n",
              "decent                                   1\n",
              "dramatic                                 1\n",
              "family                                   1\n",
              "fast                                     1\n",
              "hectic                                   1\n",
              "marathon                                 1\n",
              "new                                      1\n",
              "ok                                       1\n",
              "pain                                     1\n",
              "shit                                     1\n",
              "Name: describe1a, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It follows that the next step of the analysis includes cleaning up the data. "
      ],
      "metadata": {
        "id": "2Q2WS9DTzBDU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list for the describe 1A data\n",
        "described= df['describe1a'].values.tolist()\n",
        "\n",
        "# Removing capital letters \n",
        "for word in range(len(described)):\n",
        "    described[word] = described[word].lower()\n",
        "\n",
        "clean_described = []\n",
        "for w in range(0, len(described)):\n",
        "\n",
        "    # Removing spaces\n",
        "    clean_described.append(re.sub(\" \", \"\", described[w]))\n",
        "\n",
        "    # Removing periods\n",
        "    clean_described[w] = re.sub(\"-\",\"\", clean_described[w])\n",
        "\n",
        "clean_described"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YVPsZexmxWmx",
        "outputId": "57337863-b398-4a2b-852c-9fd9f49f7ee4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hell',\n",
              " 'dramatic',\n",
              " 'hectic',\n",
              " 'chaotic',\n",
              " 'anordeal.',\n",
              " 'ok.',\n",
              " 'marathon',\n",
              " 'new',\n",
              " 'chaoticandlonely',\n",
              " 'adventurous',\n",
              " 'family',\n",
              " 'funsocially,depressingacademically',\n",
              " 'ok',\n",
              " 'joyful',\n",
              " 'amazing',\n",
              " 'rush',\n",
              " 'shit',\n",
              " 'poggywoggy',\n",
              " 'rollercoaster',\n",
              " 'fast',\n",
              " '0/10',\n",
              " 'pedantic',\n",
              " 'tstsians',\n",
              " 'change',\n",
              " 'scrambled',\n",
              " 'fast',\n",
              " 'decent',\n",
              " 'intense',\n",
              " 'exhaustion',\n",
              " 'shit',\n",
              " 'busy',\n",
              " 'hectic',\n",
              " 'tstsians',\n",
              " 'eyeopening',\n",
              " 'tstsians',\n",
              " 'mid',\n",
              " 'unexpected',\n",
              " 'adjustment',\n",
              " 'exciting',\n",
              " 'death',\n",
              " 'learningcurve',\n",
              " 'freedom',\n",
              " 'tsts',\n",
              " 'movie',\n",
              " 'lit',\n",
              " 'intense',\n",
              " 'fun',\n",
              " 'new',\n",
              " 'balanced',\n",
              " 'alcohol',\n",
              " 'people',\n",
              " 'new',\n",
              " 'funwithsomedifficulttimes',\n",
              " 'funky',\n",
              " 'online',\n",
              " 'pain',\n",
              " 'tstsian',\n",
              " 'based',\n",
              " 'new',\n",
              " 'lifechanging',\n",
              " 'fun',\n",
              " 'collaborative']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We than lemmatized the data, a process that removes 'uneccessary' characters from a word - bringing it back to its root form. For instance, 'run', 'runs', and 'running' would be reduced to 'run'. \n",
        "\n",
        "Following this step, the tokezined words were stripped of spaces and combined as strings for the next step of analysis."
      ],
      "metadata": {
        "id": "jxHEo2gkzfxP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.tag import pos_tag\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "\n",
        "# lemmatization \n",
        "\n",
        "def lemmatize_words(tokens):\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemmatized_sentence = []\n",
        "    for word in clean_described:\n",
        "        lemmatized_sentence.append(lemmatizer.lemmatize(word))\n",
        "    return lemmatized_sentence\n",
        "    \n",
        "lemmatize_words(clean_described)\n",
        "\n",
        "# converting the Tokenized list to string to apply sentiment.polarity() via TextBlob\n",
        "\n",
        "text=''\n",
        "for i in clean_described:\n",
        "    text += i\n",
        "print(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SjsppCNBxaKJ",
        "outputId": "5b35fa8a-6cc0-4ad9-a416-a6c2b980970b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "helldramatichecticchaoticanordeal.ok.marathonnewchaoticandlonelyadventurousfamilyfunsocially,depressingacademicallyokjoyfulamazingrushshitpoggywoggyrollercoasterfast0/10pedantictstsianschangescrambledfastdecentintenseexhaustionshitbusyhectictstsianseyeopeningtstsiansmidunexpectedadjustmentexcitingdeathlearningcurvefreedomtstsmovielitintensefunnewbalancedalcoholpeoplenewfunwithsomedifficulttimesfunkyonlinepaintstsianbasednewlifechangingfuncollaborative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "At this point, we are bringing in the Afinn and Vader scales as a method to measure sentiment. Use '!pip install afinn' and '!pip install vaderSentiment' to install the libraries. \n",
        "\n",
        "As a side note, Afinn is more focused on individual words than a full sentence. "
      ],
      "metadata": {
        "id": "2OQN-5cX0xZl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install afinn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYm1EjnZ_zw5",
        "outputId": "103a46a7-0c1d-4504-bafe-d6b83a1b0de2"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting afinn\n",
            "  Downloading afinn-0.1.tar.gz (52 kB)\n",
            "\u001b[?25l\r\u001b[K     |██████▎                         | 10 kB 18.2 MB/s eta 0:00:01\r\u001b[K     |████████████▌                   | 20 kB 11.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████▊             | 30 kB 9.4 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 40 kB 8.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 51 kB 5.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 52 kB 858 kB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: afinn\n",
            "  Building wheel for afinn (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for afinn: filename=afinn-0.1-py3-none-any.whl size=53447 sha256=0af7c5ea672d3dbb1ae49d20c09e019180f614711734f0353839ab134737812b\n",
            "  Stored in directory: /root/.cache/pip/wheels/9d/16/3a/9f0953027434eab5dadf3f33ab3298fa95afa8292fcf7aba75\n",
            "Successfully built afinn\n",
            "Installing collected packages: afinn\n",
            "Successfully installed afinn-0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install vaderSentiment"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kY3xx76E_3rQ",
        "outputId": "9c0247a4-97f1-4ca4-8be5-24d2f45b6950"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting vaderSentiment\n",
            "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
            "\u001b[?25l\r\u001b[K     |██▋                             | 10 kB 23.4 MB/s eta 0:00:01\r\u001b[K     |█████▏                          | 20 kB 15.5 MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 30 kB 12.0 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 40 kB 10.0 MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 51 kB 5.0 MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 61 kB 5.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████▏             | 71 kB 6.0 MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 81 kB 4.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▍        | 92 kB 4.9 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 102 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 112 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 122 kB 5.4 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 125 kB 5.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vaderSentiment) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2021.10.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vaderSentiment) (3.0.4)\n",
            "Installing collected packages: vaderSentiment\n",
            "Successfully installed vaderSentiment-3.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating the afinn score for the describe 1A question:"
      ],
      "metadata": {
        "id": "yZLQ0YZ4AFpC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import afinn\n",
        "from afinn import Afinn\n",
        "afinn = Afinn()\n",
        "\n",
        "df['afinn_score'] = df['describe1a'].apply(afinn.score)\n",
        "df['afinn_score'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z5i-GrSkSyFL",
        "outputId": "ebed4620-24fc-4ee1-e5c3-a87d0ff8353c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    62.000000\n",
              "mean      0.129032\n",
              "std       1.644503\n",
              "min      -4.000000\n",
              "25%       0.000000\n",
              "50%       0.000000\n",
              "75%       0.000000\n",
              "max       4.000000\n",
              "Name: afinn_score, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing the data and creating a histogram:"
      ],
      "metadata": {
        "id": "liW0p_0d9Q4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_display = ['describe1a','afinn_score']\n",
        "values = df.sort_values(by='afinn_score')[columns_to_display]\n",
        "values\n",
        "\n",
        "values.hist()\n",
        "plt.title('Frequency vs Afinn Score - Describe 1A')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "29GVWVrcvLrC",
        "outputId": "52591875-5398-4188-861c-24386c7d3750"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Frequency vs Afinn Score - Describe 1A')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, calculating the afinn score of the advice question and putting it into graph form:"
      ],
      "metadata": {
        "id": "ACLxEO-S9vv3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['afinn_score'] = df['advice'].apply(afinn.score)\n",
        "df['afinn_score'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuPnxlNmvPxz",
        "outputId": "d045eedc-f980-4930-f813-7d073c5841f9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    62.000000\n",
              "mean      1.919355\n",
              "std       3.535758\n",
              "min      -3.000000\n",
              "25%       0.000000\n",
              "50%       1.000000\n",
              "75%       3.000000\n",
              "max      16.000000\n",
              "Name: afinn_score, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_display = ['advice', 'afinn_score']\n",
        "values2 = df.sort_values(by='afinn_score')[columns_to_display]\n",
        "values2\n",
        "\n",
        "values2.hist()\n",
        "plt.title('Frequency vs Afinn Score - Advice')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "4cAlJqcjvSHR",
        "outputId": "0dcceef0-aaf7-4f04-e7ad-f3ff7813f552"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Frequency vs Afinn Score - Advice')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unlike Afinn, which returns a single value, Vader analysis returns a dictionary."
      ],
      "metadata": {
        "id": "IcM5_uKJ-Zlk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "# summary of polarity scores\n",
        "\n",
        "analyzer = SentimentIntensityAnalyzer()\n",
        "analyzer.polarity_scores(df['advice'])\n",
        "\n",
        "sentiment = df['advice'].apply(analyzer.polarity_scores)\n",
        "sentiment_df = pd.DataFrame(sentiment.tolist())\n",
        "sentiment_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u4ZbobBmvXvv",
        "outputId": "11007c4a-ac9c-4ae1-85df-c7726177c63e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     neg    neu    pos  compound\n",
              "0  0.445  0.555  0.000   -0.3412\n",
              "1  0.000  1.000  0.000    0.0000\n",
              "2  0.000  0.256  0.744    0.4404\n",
              "3  0.000  0.818  0.182    0.5824\n",
              "4  0.000  0.769  0.231    0.5859"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3361f7b-9834-42b9-a332-0bb964ad1627\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>neg</th>\n",
              "      <th>neu</th>\n",
              "      <th>pos</th>\n",
              "      <th>compound</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.445</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.3412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.256</td>\n",
              "      <td>0.744</td>\n",
              "      <td>0.4404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.818</td>\n",
              "      <td>0.182</td>\n",
              "      <td>0.5824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.769</td>\n",
              "      <td>0.231</td>\n",
              "      <td>0.5859</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3361f7b-9834-42b9-a332-0bb964ad1627')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  "
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using vader sentiment to analyze the describe 1A data:"
      ],
      "metadata": {
        "id": "x2DkUTE9-q7N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "added_df_sentiment = pd.concat([df,sentiment_df], axis =1)\n",
        "added_df_sentiment.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
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        "id": "iHgwyCtmvavW",
        "outputId": "646c41e9-74f3-4131-cb51-623902f1a79b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  apply_again  expected_experience  \\\n",
              "0         Yes                  4.0   \n",
              "1         NaN                  NaN   \n",
              "2         NaN                  NaN   \n",
              "3         NaN                  NaN   \n",
              "4         Yes                  3.0   \n",
              "\n",
              "                                              advice describe1a  afinn_score  \\\n",
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              "1                                                NaN        NaN          NaN   \n",
              "2                                                NaN        NaN          NaN   \n",
              "3                                                NaN        NaN          NaN   \n",
              "4  Try out many different things in high school, ...   dramatic          0.0   \n",
              "\n",
              "     neg    neu    pos  compound  \n",
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              "1  0.000  1.000  0.000    0.0000  \n",
              "2  0.000  0.256  0.744    0.4404  \n",
              "3  0.000  0.818  0.182    0.5824  \n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# For ease of testing, the whole process was put into one function so we can try it multiple times\n",
        "\n",
        "def vaderize(df, column):\n",
        "\n",
        "    analyzer = SentimentIntensityAnalyzer()    \n",
        "    sentiment = df[column].apply(analyzer.polarity_scores)\n",
        "    \n",
        "    sdf = pd.DataFrame(sentiment.tolist()).add_prefix('vader_')\n",
        "    \n",
        "    # Merge dataframes\n",
        "    df_combined = pd.concat([df,sdf], axis=1)\n",
        "    return df_combined\n",
        "  \n",
        "df_vaderized = vaderize(df, 'describe1a')\n",
        "df_vaderized.head(50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "JMU7dqw8veXb",
        "outputId": "4949607b-0230-4c0e-b852-c04987ec5280"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   apply_again  expected_experience  \\\n",
              "0          Yes                  4.0   \n",
              "1          NaN                  NaN   \n",
              "2          NaN                  NaN   \n",
              "3          NaN                  NaN   \n",
              "4          Yes                  3.0   \n",
              "5          Yes                  5.0   \n",
              "6          Yes                  4.0   \n",
              "7          Yes                  3.0   \n",
              "8          NaN                  NaN   \n",
              "9          Yes                  4.0   \n",
              "10         NaN                  NaN   \n",
              "11         Yes                  2.0   \n",
              "12         Yes                  2.0   \n",
              "13         NaN                  NaN   \n",
              "14          No                  2.0   \n",
              "15         Yes                  6.0   \n",
              "16         Yes                  2.0   \n",
              "17         NaN                  NaN   \n",
              "18         Yes                  3.0   \n",
              "19         Yes                  5.0   \n",
              "20          No                  3.0   \n",
              "21         NaN                  NaN   \n",
              "22         Yes                  5.0   \n",
              "23         Yes                  5.0   \n",
              "24         Yes                  6.0   \n",
              "25         Yes                  7.0   \n",
              "26         Yes                  6.0   \n",
              "27         Yes                  5.0   \n",
              "28          No                  1.0   \n",
              "29         NaN                  NaN   \n",
              "30         Yes                  4.0   \n",
              "31         Yes                  5.0   \n",
              "32         Yes                  4.0   \n",
              "33         Yes                  6.0   \n",
              "34         Yes                  6.0   \n",
              "35         Yes                  4.0   \n",
              "36          No                  4.0   \n",
              "37         Yes                  6.0   \n",
              "38         Yes                  2.0   \n",
              "39         Yes                  5.0   \n",
              "40         Yes                  5.0   \n",
              "41         NaN                  NaN   \n",
              "42         Yes                  5.0   \n",
              "43         Yes                  5.0   \n",
              "44         Yes                  3.0   \n",
              "45         Yes                  4.0   \n",
              "46         Yes                  2.0   \n",
              "47         Yes                  5.0   \n",
              "48         Yes                  5.0   \n",
              "49          No                  2.0   \n",
              "\n",
              "                                               advice  \\\n",
              "0                                    It won't be easy   \n",
              "1                                                 NaN   \n",
              "2                                                 NaN   \n",
              "3                                                 NaN   \n",
              "4   Try out many different things in high school, ...   \n",
              "5                                         Be sociable   \n",
              "6   It’s just a program, it won’t make that much o...   \n",
              "7   Any university degree is a marathon, not a spr...   \n",
              "8                                                 NaN   \n",
              "9                                               Code    \n",
              "10                                                NaN   \n",
              "11  get cracked before coming here lmao, everyones...   \n",
              "12             Prepare for co-op before school starts   \n",
              "13                                                NaN   \n",
              "14  check if you actually like the courses in the ...   \n",
              "15  SYDE is best for people who aren't sure betwee...   \n",
              "16  THE PEOPLE ARE AMAZING AND SYDE IS SO FAMILY O...   \n",
              "17                                                NaN   \n",
              "18                               Look at the courses.   \n",
              "19                               keep mental in check   \n",
              "20                    Get familiar with C++ and CAD.    \n",
              "21                                                NaN   \n",
              "22                                              APPLY   \n",
              "23             Don't stress too much, try to enjoy 1A   \n",
              "24                            dont trust jason foster   \n",
              "25                           The TsTsians are coming    \n",
              "26  If you want a strong cohort and community, thi...   \n",
              "27  SYDE is a really unique program. If you get in...   \n",
              "28                                             Don't    \n",
              "29                                                NaN   \n",
              "30                Be disciplined so you can have fun.   \n",
              "31  Don’t stress too much, they give you more work...   \n",
              "32                                             Do it.   \n",
              "33  Don't let your grades define you. You're never...   \n",
              "34  Don't set unrealistically high expectations fo...   \n",
              "35            Interact with people and enjoy yourself   \n",
              "36  Make friends in the program and try to hang ou...   \n",
              "37  It's very versatile - you can go into whatever...   \n",
              "38  dont worry about your drop in grades, just foc...   \n",
              "39  Rather than comparing yourself to your peers, ...   \n",
              "40               stop worrying so much and enjoy life   \n",
              "41                                                NaN   \n",
              "42  get them syde projects done, join an extracurr...   \n",
              "43  Make sure it aligns with your interests! Check...   \n",
              "44  Things like \"SYDE is tight, fun, chill, wide r...   \n",
              "45  If you have no idea what to apply to, apply to...   \n",
              "46  The 'close knit' community reason to apply to ...   \n",
              "47  Adjust your expectations - it's not like the m...   \n",
              "48  Try your best but don't base your worth on uni...   \n",
              "49                                           get good   \n",
              "\n",
              "                               describe1a  afinn_score  vader_neg  vader_neu  \\\n",
              "0                                    Hell          4.0      1.000      0.000   \n",
              "1                                     NaN          NaN      0.000      1.000   \n",
              "2                                     NaN          NaN      0.000      1.000   \n",
              "3                                     NaN          NaN      1.000      0.000   \n",
              "4                                dramatic          0.0      0.000      1.000   \n",
              "5                                  Hectic          0.0      0.000      1.000   \n",
              "6                                 Chaotic          0.0      0.000      1.000   \n",
              "7                              An Ordeal.          2.0      0.000      1.000   \n",
              "8                                     NaN          NaN      0.851      0.149   \n",
              "9                                     Ok.          0.0      0.000      0.000   \n",
              "10                                    NaN          NaN      0.000      1.000   \n",
              "11                               marathon          7.0      0.329      0.253   \n",
              "12                                    new          0.0      0.000      0.000   \n",
              "13                                    NaN          NaN      0.000      0.000   \n",
              "14                     chaotic and lonely          4.0      0.000      0.000   \n",
              "15                            Adventurous          3.0      0.000      1.000   \n",
              "16                                 family          4.0      1.000      0.000   \n",
              "17                                    NaN          NaN      0.000      1.000   \n",
              "18  Fun socially, depressing academically          0.0      0.000      1.000   \n",
              "19                                     ok          0.0      0.000      1.000   \n",
              "20                                 Joyful          0.0      0.000      1.000   \n",
              "21                                    NaN          NaN      0.000      1.000   \n",
              "22                                Amazing          0.0      0.000      1.000   \n",
              "23                                   Rush          1.0      0.000      1.000   \n",
              "24                                   Shit          1.0      0.000      1.000   \n",
              "25                             PoggyWoggy          0.0      0.000      1.000   \n",
              "26                          Rollercoaster          3.0      0.000      1.000   \n",
              "27                                   Fast          1.0      0.000      0.000   \n",
              "28                                   0/10          0.0      1.000      0.000   \n",
              "29                                    NaN          NaN      1.000      0.000   \n",
              "30                              Pedantic           4.0      0.000      1.000   \n",
              "31                               Tstsians          1.0      0.000      1.000   \n",
              "32                                 Change          0.0      0.000      1.000   \n",
              "33                              Scrambled          8.0      0.000      1.000   \n",
              "34                                   fast          3.0      0.000      1.000   \n",
              "35                                 decent          2.0      0.000      1.000   \n",
              "36                                Intense          0.0      0.000      1.000   \n",
              "37                             Exhaustion          4.0      0.000      1.000   \n",
              "38                                   shit         -3.0      0.000      0.000   \n",
              "39                                   Busy         -2.0      1.000      0.000   \n",
              "40                                 hectic         -2.0      0.000      1.000   \n",
              "41                                    NaN          NaN      0.000      0.000   \n",
              "42                               TsTsians          1.0      0.000      1.000   \n",
              "43                            Eye-opening          1.0      0.000      1.000   \n",
              "44                               TsTsians         16.0      0.000      1.000   \n",
              "45                                    Mid         -1.0      0.000      0.000   \n",
              "46                             Unexpected          2.0      0.000      0.000   \n",
              "47                             Adjustment          2.0      0.000      1.000   \n",
              "48                               Exciting          9.0      0.000      1.000   \n",
              "49                                 death           3.0      0.000      1.000   \n",
              "\n",
              "    vader_pos  vader_compound  \n",
              "0       0.000         -0.6808  \n",
              "1       0.000          0.0000  \n",
              "2       0.000          0.0000  \n",
              "3       0.000         -0.4939  \n",
              "4       0.000          0.0000  \n",
              "5       0.000          0.0000  \n",
              "6       0.000          0.0000  \n",
              "7       0.000          0.0000  \n",
              "8       0.000         -0.6908  \n",
              "9       1.000          0.3400  \n",
              "10      0.000          0.0000  \n",
              "11      0.418          0.1779  \n",
              "12      1.000          0.2960  \n",
              "13      1.000          0.5994  \n",
              "14      1.000          0.5859  \n",
              "15      0.000          0.0000  \n",
              "16      0.000         -0.5574  \n",
              "17      0.000          0.0000  \n",
              "18      0.000          0.0000  \n",
              "19      0.000          0.0000  \n",
              "20      0.000          0.0000  \n",
              "21      0.000          0.0000  \n",
              "22      0.000          0.0000  \n",
              "23      0.000          0.0000  \n",
              "24      0.000          0.0000  \n",
              "25      0.000          0.0000  \n",
              "26      0.000          0.0000  \n",
              "27      1.000          0.0772  \n",
              "28      0.000         -0.3612  \n",
              "29      0.000         -0.5574  \n",
              "30      0.000          0.0000  \n",
              "31      0.000          0.0000  \n",
              "32      0.000          0.0000  \n",
              "33      0.000          0.0000  \n",
              "34      0.000          0.0000  \n",
              "35      0.000          0.0000  \n",
              "36      0.000          0.0000  \n",
              "37      0.000          0.0000  \n",
              "38      1.000          0.4939  \n",
              "39      0.000         -0.5994  \n",
              "40      0.000          0.0000  \n",
              "41      1.000          0.6369  \n",
              "42      0.000          0.0000  \n",
              "43      0.000          0.0000  \n",
              "44      0.000          0.0000  \n",
              "45      1.000          0.0772  \n",
              "46      1.000          0.5106  \n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>apply_again</th>\n",
              "      <th>expected_experience</th>\n",
              "      <th>advice</th>\n",
              "      <th>describe1a</th>\n",
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              "      <th>vader_compound</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>It won't be easy</td>\n",
              "      <td>Hell</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.000</td>\n",
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              "      <td>0.000</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.4939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Try out many different things in high school, ...</td>\n",
              "      <td>dramatic</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Be sociable</td>\n",
              "      <td>Hectic</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>It’s just a program, it won’t make that much o...</td>\n",
              "      <td>Chaotic</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Any university degree is a marathon, not a spr...</td>\n",
              "      <td>An Ordeal.</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.851</td>\n",
              "      <td>0.149</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.6908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Code</td>\n",
              "      <td>Ok.</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.3400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>get cracked before coming here lmao, everyones...</td>\n",
              "      <td>marathon</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.329</td>\n",
              "      <td>0.253</td>\n",
              "      <td>0.418</td>\n",
              "      <td>0.1779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Prepare for co-op before school starts</td>\n",
              "      <td>new</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.2960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.5994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>No</td>\n",
              "      <td>2.0</td>\n",
              "      <td>check if you actually like the courses in the ...</td>\n",
              "      <td>chaotic and lonely</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.5859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Yes</td>\n",
              "      <td>6.0</td>\n",
              "      <td>SYDE is best for people who aren't sure betwee...</td>\n",
              "      <td>Adventurous</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>THE PEOPLE ARE AMAZING AND SYDE IS SO FAMILY O...</td>\n",
              "      <td>family</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.5574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Look at the courses.</td>\n",
              "      <td>Fun socially, depressing academically</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>keep mental in check</td>\n",
              "      <td>ok</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>No</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Get familiar with C++ and CAD.</td>\n",
              "      <td>Joyful</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>APPLY</td>\n",
              "      <td>Amazing</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Don't stress too much, try to enjoy 1A</td>\n",
              "      <td>Rush</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Yes</td>\n",
              "      <td>6.0</td>\n",
              "      <td>dont trust jason foster</td>\n",
              "      <td>Shit</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Yes</td>\n",
              "      <td>7.0</td>\n",
              "      <td>The TsTsians are coming</td>\n",
              "      <td>PoggyWoggy</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Yes</td>\n",
              "      <td>6.0</td>\n",
              "      <td>If you want a strong cohort and community, thi...</td>\n",
              "      <td>Rollercoaster</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SYDE is a really unique program. If you get in...</td>\n",
              "      <td>Fast</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>No</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Don't</td>\n",
              "      <td>0/10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.3612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.5574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Be disciplined so you can have fun.</td>\n",
              "      <td>Pedantic</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Don’t stress too much, they give you more work...</td>\n",
              "      <td>Tstsians</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Do it.</td>\n",
              "      <td>Change</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Yes</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Don't let your grades define you. You're never...</td>\n",
              "      <td>Scrambled</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Yes</td>\n",
              "      <td>6.0</td>\n",
              "      <td>Don't set unrealistically high expectations fo...</td>\n",
              "      <td>fast</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Interact with people and enjoy yourself</td>\n",
              "      <td>decent</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>No</td>\n",
              "      <td>4.0</td>\n",
              "      <td>Make friends in the program and try to hang ou...</td>\n",
              "      <td>Intense</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Yes</td>\n",
              "      <td>6.0</td>\n",
              "      <td>It's very versatile - you can go into whatever...</td>\n",
              "      <td>Exhaustion</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>dont worry about your drop in grades, just foc...</td>\n",
              "      <td>shit</td>\n",
              "      <td>-3.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.4939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Rather than comparing yourself to your peers, ...</td>\n",
              "      <td>Busy</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>-0.5994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>stop worrying so much and enjoy life</td>\n",
              "      <td>hectic</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.6369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>get them syde projects done, join an extracurr...</td>\n",
              "      <td>TsTsians</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Make sure it aligns with your interests! Check...</td>\n",
              "      <td>Eye-opening</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Things like \"SYDE is tight, fun, chill, wide r...</td>\n",
              "      <td>TsTsians</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>If you have no idea what to apply to, apply to...</td>\n",
              "      <td>Mid</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.0772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>The 'close knit' community reason to apply to ...</td>\n",
              "      <td>Unexpected</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.5106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Adjust your expectations - it's not like the m...</td>\n",
              "      <td>Adjustment</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Try your best but don't base your worth on uni...</td>\n",
              "      <td>Exciting</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>No</td>\n",
              "      <td>2.0</td>\n",
              "      <td>get good</td>\n",
              "      <td>death</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8c959af-61ab-4bdb-be34-e10a61bcd6d3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a8c959af-61ab-4bdb-be34-e10a61bcd6d3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a8c959af-61ab-4bdb-be34-e10a61bcd6d3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting vader analysis \n",
        "\n",
        "df_vaderized['vader_compound'].plot(kind='hist')\n",
        "plt.title('Vader Sentiment Analysis - Describe 1A')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xlabel('Vader Score')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "M0gLsHZbvhr6",
        "outputId": "6d9881ce-4de9-4c0b-c780-7abd5381c143"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Vader score')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_vaderized.plot.scatter(x='vader_pos', y='vader_neg')\n",
        "plt.title('Vader Sentiment Analysis - Describe 1A')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "S9xkY57ivjv7",
        "outputId": "01b95321-42f0-4ca5-9949-e9d7e42850c4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Vader Sentiment Analysis')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}