Amazon_greenwashing_analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_TDAUT_houh"
      },
      "source": [
        "# Analysis of perceived harmfullness\n",
        "\n",
        "This notebook loads amazon web-scraped data and analyzes the correlation between perceived harmfulness of products and 'greenwashing' keywords from the products descriptions."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WybeiRH6houo"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XATtDSYyhoup"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qtc-iDgghour"
      },
      "source": [
        "## Data preparation\n",
        "\n",
        "Load the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fDd52Ebehour"
      },
      "outputs": [],
      "source": [
        "# products = pd.read_csv('products1.csv.gz') # out keywords\n",
        "# products = pd.read_csv('products2.csv.gz') # extended keywords\n",
        "products = pd.read_csv('products.csv.gz') # both combined (duplicates removed), no website"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TP6jHFC9hous",
        "outputId": "fcf910b6-4f02-4bdb-8a95-c0a6a7d60511"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['bleach+tablets', 'Kitchen+cleaner', 'Dishwasher+detergent',\n",
              "       'Window+spray', 'Fabric+softener', 'Laundry+sanitizer',\n",
              "       'Moisture+absorber', 'Disinfectant+spray', 'Kettle+descaler',\n",
              "       'Spot+remover', 'Glass+cleaner', 'Stain+eliminator',\n",
              "       'Machine+descaler', 'Dishwasher+pods', 'Odor+lifter',\n",
              "       'Bleach+pods', 'Window+cleaner', 'Detergent+tablets',\n",
              "       'Bathroom+cleaner', 'Soap+pods', 'Washing+powder', 'Stain+remover',\n",
              "       'Laundry+sheets', 'Disinfectant+wipes', 'Laundry+powder',\n",
              "       'Flashlight', 'Mouthwash', 'Contact+Lens+Solutions',\n",
              "       'Prescription+Medications', 'Fabric+Softener', 'Razor', 'Lotion',\n",
              "       'Deodorant', 'Sponges', 'Extension+Cords', 'Eye+Drops',\n",
              "       'Hydrogen+Peroxide', 'Allergy+Medications', 'Shampoo',\n",
              "       'Rubbing+Alcohol', 'Cough+Syrup', 'Antibiotic+Ointment',\n",
              "       'Light+Bulbs', 'Conditioner', 'Laundry+Detergent',\n",
              "       'Insect+Repellent', 'Vitamins', 'Paper+Towels', 'Bar+Soap',\n",
              "       'Toothpaste', 'Cotton+Swabs', 'Toothbrush', 'Face+Wash',\n",
              "       'Thermometer', 'First+Aid+Kit', 'Moisturizer',\n",
              "       'Disinfecting+Wipes', 'Dish+Soap', 'Toilet+Paper',\n",
              "       'Hand+Sanitizer', 'Facial+Tissues', 'Cleaning+Spray',\n",
              "       'Reading+Glasses', 'Hot+Cold+Packs', 'Nasal+Spray', 'Body+Wash',\n",
              "       'Sunscreen', 'Pain+Relievers', 'Acne+Treatment', 'Bandages',\n",
              "       'Shaving+Cream', 'Trash+Bags', 'Batteries', 'Floss'], dtype=object)"
            ]
          },
          "execution_count": 267,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "products[\"keyword\"].unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLHDkmWQhouu"
      },
      "source": [
        "Drop the whole website, which takes up a lot of memory\n",
        "\n",
        "This has already been done for the `products.csv.gz` dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QOq70z8-houv"
      },
      "outputs": [],
      "source": [
        "# products = products.copy().drop(\"webpage\", axis = 1) "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1Cs91gMhouw"
      },
      "source": [
        "Find missing values. There are some missing descriptions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kg5Fq1bIhouw",
        "outputId": "ab3c667e-7b20-440e-f2b8-3dc04740374c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "keyword                False\n",
              "name                   False\n",
              "url                    False\n",
              "product_description     True\n",
              "dtype: bool"
            ]
          },
          "execution_count": 269,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "products.isna().any()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-MuUBRThoux"
      },
      "source": [
        "There are 129 of them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1wSyiXf6houy",
        "outputId": "e951fe89-8cc1-4230-eecc-f9baf37ba279"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>keyword</th>\n",
              "      <th>name</th>\n",
              "      <th>url</th>\n",
              "      <th>product_description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>770</th>\n",
              "      <td>Bleach+pods</td>\n",
              "      <td>Clorox Bleach Packs, Bleach Laundry Pods, Zero...</td>\n",
              "      <td>https://amazon.com/Clorox-Zero-Splash-Bleach-P...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>771</th>\n",
              "      <td>Bleach+pods</td>\n",
              "      <td>Grab Green Bleach Alternative Pods, 60 Count, ...</td>\n",
              "      <td>https://amazon.com/Grab-Green-Alternative-Unsc...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>772</th>\n",
              "      <td>Bleach+pods</td>\n",
              "      <td>GuardH Bleach Tablets - 40 count. Bleach for l...</td>\n",
              "      <td>https://amazon.com/GuardH-Bleach-Tablets-Multi...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>773</th>\n",
              "      <td>Bleach+pods</td>\n",
              "      <td>Country Save Laundry Detergent Powder Natural-...</td>\n",
              "      <td>https://amazon.com/Country-Save-Detergent-Natu...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>774</th>\n",
              "      <td>Bleach+pods</td>\n",
              "      <td>Dropps Laundry &amp; Household Oxi Booster Pods: U...</td>\n",
              "      <td>https://amazon.com/Dropps-Laundry-Household-Bo...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4090</th>\n",
              "      <td>Shaving+Cream</td>\n",
              "      <td>Musgo Real Shaving Cream - Classic Scent 3.4 O...</td>\n",
              "      <td>https://amazon.com/Musgo-Real-Shaving-Cream-Cl...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4092</th>\n",
              "      <td>Shaving+Cream</td>\n",
              "      <td>The Art of Shaving Unscented Shaving Cream for...</td>\n",
              "      <td>https://amazon.com/Art-Shaving-Cream-Unscented...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4095</th>\n",
              "      <td>Shaving+Cream</td>\n",
              "      <td>Proraso Shaving Soap</td>\n",
              "      <td>https://amazon.com/Proraso-Shaving-Soap-Moistu...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4099</th>\n",
              "      <td>Shaving+Cream</td>\n",
              "      <td>Proraso Shaving Cream for Men</td>\n",
              "      <td>https://amazon.com/Proraso-Shaving-Cream-Sensi...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4104</th>\n",
              "      <td>Shaving+Cream</td>\n",
              "      <td>The Art of Shaving Shaving Cream for Men - Sha...</td>\n",
              "      <td>https://amazon.com/Art-Shaving-Bourbon-Cream-F...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>301 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            keyword                                               name  \\\n",
              "770     Bleach+pods  Clorox Bleach Packs, Bleach Laundry Pods, Zero...   \n",
              "771     Bleach+pods  Grab Green Bleach Alternative Pods, 60 Count, ...   \n",
              "772     Bleach+pods  GuardH Bleach Tablets - 40 count. Bleach for l...   \n",
              "773     Bleach+pods  Country Save Laundry Detergent Powder Natural-...   \n",
              "774     Bleach+pods  Dropps Laundry & Household Oxi Booster Pods: U...   \n",
              "...             ...                                                ...   \n",
              "4090  Shaving+Cream  Musgo Real Shaving Cream - Classic Scent 3.4 O...   \n",
              "4092  Shaving+Cream  The Art of Shaving Unscented Shaving Cream for...   \n",
              "4095  Shaving+Cream                              Proraso Shaving Soap    \n",
              "4099  Shaving+Cream                     Proraso Shaving Cream for Men    \n",
              "4104  Shaving+Cream  The Art of Shaving Shaving Cream for Men - Sha...   \n",
              "\n",
              "                                                    url product_description  \n",
              "770   https://amazon.com/Clorox-Zero-Splash-Bleach-P...                 NaN  \n",
              "771   https://amazon.com/Grab-Green-Alternative-Unsc...                 NaN  \n",
              "772   https://amazon.com/GuardH-Bleach-Tablets-Multi...                 NaN  \n",
              "773   https://amazon.com/Country-Save-Detergent-Natu...                 NaN  \n",
              "774   https://amazon.com/Dropps-Laundry-Household-Bo...                 NaN  \n",
              "...                                                 ...                 ...  \n",
              "4090  https://amazon.com/Musgo-Real-Shaving-Cream-Cl...                 NaN  \n",
              "4092  https://amazon.com/Art-Shaving-Cream-Unscented...                 NaN  \n",
              "4095  https://amazon.com/Proraso-Shaving-Soap-Moistu...                 NaN  \n",
              "4099  https://amazon.com/Proraso-Shaving-Cream-Sensi...                 NaN  \n",
              "4104  https://amazon.com/Art-Shaving-Bourbon-Cream-F...                 NaN  \n",
              "\n",
              "[301 rows x 4 columns]"
            ]
          },
          "execution_count": 270,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "products[products.isna().any(axis=1)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SddY5AQuhouy"
      },
      "source": [
        "Drop them for now"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o5O9L2Jdhouy"
      },
      "outputs": [],
      "source": [
        "products = products[-products.isna().any(axis=1)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RrLlWNDehouz"
      },
      "source": [
        "## Ecological keywords in product descriptions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a38G-wLBhouz"
      },
      "source": [
        "Convert all to small case, strip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1rnzuf87hou0"
      },
      "outputs": [],
      "source": [
        "products[\"product_description\"] = products[\"product_description\"].copy().apply(lambda desc: desc.strip().lower())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-eRF9kYhou0"
      },
      "source": [
        "Have a look at one description"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UpshnKAdhou0",
        "outputId": "cfef9383-82fe-46ac-f805-7a6b52becfce"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'about this item    seventh generation free & clear dishwasher detergent powder powers through stuck-on food    this powerful formula leaves dishes spot-free, streak-free and sparkling clean    0% fragrances, dyes or chlorine bleach    not tested on animals    works well even in hard water conditions'"
            ]
          },
          "execution_count": 273,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "products[\"product_description\"][128]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZ_cF4p-hou1"
      },
      "source": [
        "List of relevant green keywords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bHTWvGPihou1"
      },
      "outputs": [],
      "source": [
        "green_keywords = [\n",
        "    'eco friendly', 'earth friendly', 'environmentally friendly',\n",
        "    'eco', 'bio',  'not tested on animals'\n",
        "    'reusable', 'green',\n",
        "    'free & gentle', 'free & clear', 'free and gentle', 'free and clear',\n",
        "    'safe', 'sensitive, gentle', 'probiotic', 'botanical', 'natural',\n",
        "    'plant based', 'plant-based', 'hypoallergenic', 'recycled', 'vegan', 'organic',\n",
        "    'alternative', 'compostable', 'bamboo', 'renewable', 'biodegradable',\n",
        "    'sustainable', 'eco', 'responsible', 'responsibility', 'certified',\n",
        "    'durable', 'no chemicals', 'non gmo', 'non-gmo', 'washable'\n",
        "]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xxX1YCLkhou2"
      },
      "source": [
        "Count keywords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0eauOFfDhou2"
      },
      "outputs": [],
      "source": [
        "def count_keywords(item, keywords):\n",
        "    counts = [item.count(keyword) for keyword in keywords]\n",
        "    return sum(counts)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GO7u_HHBhou3"
      },
      "outputs": [],
      "source": [
        "import collections\n",
        "\n",
        "products[\"desc_green_count\"] = products[\"product_description\"].apply(lambda desc: count_keywords(desc, green_keywords))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4v43YBhhou3"
      },
      "source": [
        "Calculate the mean by a search keyword"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kxZ8Cl1thou3"
      },
      "outputs": [],
      "source": [
        "mean_green_desc = products.groupby(\"keyword\")[\"desc_green_count\"].mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JeOBt6lehou3"
      },
      "source": [
        "Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OCL5Br5Ohou4",
        "outputId": "55b5c6cd-f3ee-4792-d4f8-44dbe3447866"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "mean_green_desc_plot = pd.DataFrame(mean_green_desc.sort_values(ascending = False)).reset_index()\n",
        "\n",
        "by_school=sns.barplot(x ='keyword',y ='desc_green_count',data = mean_green_desc_plot,color=\"green\",ci=None)\n",
        "\n",
        "for item in by_school.get_xticklabels():\n",
        "    item.set_rotation(90)\n",
        "    item.set_size(7)\n",
        "\n",
        "plt.savefig('Plot.png', dpi=300, bbox_inches='tight')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBYWx_XThou4"
      },
      "source": [
        "## Word emberrings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9V7U87thou4"
      },
      "source": [
        "### Prepare data\n",
        "\n",
        "Split two-word keywords in case there is more than one"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ShUzpciJhou5"
      },
      "outputs": [],
      "source": [
        "products[\"keyword1\"] = products[\"keyword\"].apply(lambda word: word.split('+', 1)[0].lower())\n",
        "products[\"keyword2\"] = products[\"keyword\"].apply(lambda word: word.split('+', 1)[0].lower())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rk504jldhou5"
      },
      "source": [
        "### Download model\n",
        "\n",
        "Prepare a list of unique keywords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cuw896Xwhou5"
      },
      "outputs": [],
      "source": [
        "keywords = products[\"keyword1\"].append(products[\"keyword2\"]).unique()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wnxV7kl-hou6"
      },
      "source": [
        "Prepare"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wE-z-Npphou6"
      },
      "outputs": [],
      "source": [
        "import gensim\n",
        "# import json\n",
        "import pandas as pd\n",
        "import gensim.downloader as api\n",
        "import matplotlib.pyplot as plt\n",
        "from numpy.linalg import norm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8-oVb2kmhou6"
      },
      "source": [
        "Load the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fkP87SQPhou6"
      },
      "outputs": [],
      "source": [
        "## download a pre-trained model and load it into ram\n",
        "model = api.load(\"glove-wiki-gigaword-100\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UzMA7z2-hou7"
      },
      "outputs": [],
      "source": [
        "def cosine_similarity(vector1,vector2):\n",
        "    result = np.dot(vector1, vector2)/(norm(vector1)*norm(vector2))\n",
        "    return result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-VJ0isb0hou7",
        "outputId": "934e4aed-1deb-4a37-ed93-7feaa3e1c86a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([], shape=(100, 0), dtype=float64)"
            ]
          },
          "execution_count": 284,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.empty((100,0))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKDBCRP4hou7"
      },
      "source": [
        "### Create a vector between two opposites\n",
        "\n",
        "Generate lists of embeddings for groups of positive and negative words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ivKp7s4Yhou8"
      },
      "outputs": [],
      "source": [
        "## let's define an axis\n",
        "w1 = \"ecological\"\n",
        "w2 = \"harmful\"\n",
        "axis = model.get_vector(w2) - model.get_vector(w1)\n",
        "\n",
        "positive_words = [\"ecology\", \"environment\", \"sustainable\", \"renewable\", \"nature\"]\n",
        "negative_words = [\"pollution\", \"emission\", \"chemical\", \"deforestation\", \"smog\"]\n",
        "\n",
        "positive_words_embeddings = []\n",
        "\n",
        "for word in positive_words:\n",
        "    positive_words_embeddings.append(model.get_vector(word))\n",
        "    # np.concatenate((, model.get_vector(word)))\n",
        "\n",
        "negative_words_embeddings = []\n",
        "\n",
        "for word in negative_words:\n",
        "    negative_words_embeddings.append(model.get_vector(word))\n",
        "\n",
        "positive_words_embeddings = np.array(positive_words_embeddings)\n",
        "negative_words_embeddings = np.array(negative_words_embeddings)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xv-YAH4ihou8"
      },
      "source": [
        "Create a vector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G5rkJZ22hou8"
      },
      "outputs": [],
      "source": [
        "axis = negative_words_embeddings.mean(axis=0) - positive_words_embeddings.mean(axis=0)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzoXl2E9hou8"
      },
      "source": [
        "### Check position in vector space\n",
        "\n",
        "List 10 words closest to the two extremes of the axes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6az1gY_thou9",
        "outputId": "1af0467d-571f-48ee-962c-b87a88d092c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('environment', 0.8834255933761597),\n",
              " ('sustainable', 0.848753809928894),\n",
              " ('development', 0.7633209824562073),\n",
              " ('ecological', 0.7618227601051331),\n",
              " ('environmental', 0.7481111288070679),\n",
              " ('ecology', 0.747917115688324),\n",
              " ('conservation', 0.7468903064727783),\n",
              " ('nature', 0.730949342250824),\n",
              " ('natural', 0.7257879972457886),\n",
              " ('biodiversity', 0.725212574005127)]"
            ]
          },
          "execution_count": 287,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.most_similar(positive=[positive_words_embeddings.mean(axis=0)], topn=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kKZJcPDBhou9",
        "outputId": "5b4b7b1e-8f15-412d-efd5-06f2815ce1f7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[('pollution', 0.9005914926528931),\n",
              " ('emissions', 0.8254444599151611),\n",
              " ('dioxide', 0.7731346487998962),\n",
              " ('smog', 0.7709972858428955),\n",
              " ('carbon', 0.7695528268814087),\n",
              " ('emission', 0.746194064617157),\n",
              " ('greenhouse', 0.7460324168205261),\n",
              " ('pollutants', 0.7371047139167786),\n",
              " ('gases', 0.722122311592102),\n",
              " ('ozone', 0.6999489068984985)]"
            ]
          },
          "execution_count": 288,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.most_similar(positive=[negative_words_embeddings.mean(axis=0)], topn=10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ih4xjtqkhou9"
      },
      "source": [
        "### Project keywords\n",
        "\n",
        "For each word, get its position relative to the vector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "90ZA0Wibhou-"
      },
      "outputs": [],
      "source": [
        "## choose some words to project on it\n",
        "words = keywords\n",
        "projections = []\n",
        "for word in words:\n",
        "    wordvector = model.get_vector(word)\n",
        "    projections.append(cosine_similarity(wordvector,axis))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbkBRa7Xhou-"
      },
      "source": [
        "Plot the result, add labels for a few words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ANeG5ZVfhou-",
        "outputId": "d0b9c354-fcb1-4122-facf-c1bcfe59db33"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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VK7Bv7oEz6abcMRsfH48GDRoJ2/N+XlSvXh3t27sVGDsAoKen9/9pagifKXm9eRMHICewzQ1u4+PjUafOP+MqN7ADlOdYwbzeFDnHgJz73ydOHAN7+2bYvHkXGjZsBKlUivPnf8fp0yeL3E8b8n6ZDOTcTyyTyWBhYYknT2KE7arMR1XbWb16dchkCQXyOH/+dzRrVvZLsImocuLlylQpREdfQ+PGTdC7dx8hwH39Ohb3798v8E1s/nt/jh+PgEQigYtLe5XLa9vWGTdu/KkU6CYkxGPixDE4f/730jeEqBJwdm4HADh2LEJp+/HjEcjOzoaTU9sC+5w6dRyenj3w5k0c9PT00Lq1EyZNCoSZWXXhSem5gUKu3PvrY2NjhW1ZWVm4ceNvtescF/caU6dOQsOGjRAYOKPINslkMjRv3lL4l5CQgNDQtUoBCOme1q2dYGRkhFOnjitt/+OP34T/Ozu3w6NHD9G0qYMwPhwdW2DHjq347bfTAAAXl3b4669oYUznOnr0V9SqVQt2dvXRvr0rABRS1lnh/w0aNEStWrUKzLFnz57ir7+iC51juR4/foTExET07dsPjRs3Ec7O5n72qXN2Mu+tPWK4eDESWVlZwt+nT59Adna20pOQAdXmo6rtdHJywcWLkcjMzBTyv3PnFiZPHofbt2+K2j4iqjx4JpcqhRYtWiE8PAybN29E69Zt8OzZE2zatAGZmRlIS1O+t+jmzb/xww9z0LPnR7hx42+sXx8CT89/o379BiqX98UXX+HXX3/BhAn+GDhwEPT1DRAeHgZr6zro2fNjsZtHVKE0btwEH3/sibCwtUhPf4e2bV1w9+4dbNiwDu3adUDHjv8qsE+bNs7IzpZj6tRJGDDAF6ampjhx4ihSU1Pw/vs59yLmfkF17NgRtGrVBvXq2aJNGyfs3bsTdnYNUKNGDezevQMZGekwNlb9kuHMzExMnToRqakp+OabGbh//x7k8n8O9K2trWFv3xS9en2MRYvm4uXL52jevCViYh4hJCQYdevWU2t9oMqnWrVq8PUditDQNTA2NkH79q44f/4PpcDT13cYRowYhClTxuPzz/8LQ0MjHDiwD2fPnsbcuQsBAF9++TUiIg4jIGAkBg/2Q40a5jhy5BCioi5h6tSZkEqlsLOrjz59PkdoaDCysrLg4OCIiIjDuH//rlCWVCrF8OH+mD9/NmbNmo5evT5BYmLO05Vr1DDHl19+XWRbGjRoBFNTU2zatB56evrQ19fHqVMn8MsvBwCgwGdicXLnZFTURTRs2BitWrVWo1cLevMmDjNmTMF///slnj59gpCQILi6dkSHDm4IDw8T0qkyH9PS0lRqp6/vEIwYMRiTJwegb9/+SE9PR2hoMFq0aAU3t07488/rZWoTEVVODHKpUvD2HoTERBn27NmOjRtDYWNTB716fQKpVIrNmzcgOTkZ1avnXP40aNAw3Lp1A1OmjIOZmRm++mqg8DRNVdnY1MGaNWFYvfpHzJs3GwYGBmjXrj2+/34BatSooYkmElUogYHfws6uPn755SC2bNkIKytreHnlPJ20sLM/tWvXxrJlqxAaugY//DAH7969Q5Mm9pg7d5FwFuf997sjIuIw5s2bBU/PzzBpUiCmTZuF5csXY+HCOf//25V94OTUFj//vF/lusbFvcbNmzcAAJMnBxR4fdCgYRgyZDimTv0OW7ZsxP79exEbG4yaNWuhe/cP4ec3ssBZZtI93t6DYGJigl27tmP37u1o3doJ/v7jsGTJDwBynqC/evX/sG5dMObM+Q4KhQJNmthjwYIlwm/g1qpVG2vWrMfatUFYvnwxsrIy0bSpA374YanS7+ROnBiIWrVqY9++XUhKSkTHjv/CwIGDERq6RkjzySefolq1ati8eSOmTZsEU1NTdOzoDj+/0Ur3CudnZmaGBQuWIjh4Jb799htUq2aKZs0cERS0DpMmBSA6+io8PN4rcv+8TE3N8OWXX+PgwX04d+4P/PzzUaUH1Knr88/7IjU1BdOmTYaBgQF69vwYo0aNLfCTYwBKnI+qttPBoTlWrgxBSEgQZs4MhKmpGdzdO2PkyLEwMDAodVuIqHKTKEp51312thzx8aklptPXl8LS0hQJCanIyuLvDVLFxHFKlQHHKVUGHKdUGXCcUmXAcVpQzZqm0NMr+VYLnT2TK5crcOeJDLLUdFiYGsGhvgWkUonaaSqCylLP0hKzfRU1LzHzlMsVuBWTgFuPEwAJ0Ly+JZo3zHnyZ3mNE3XaJVYfFJZH3u01TAwBCZD0NqNM/ZGVJcfJK08RK0uDtYUJurWzg75++T/OQNV+VOqTaoaAAkhKy+mTprbmuPcsEQnJ6Uh+mwEzUwPUNDMWtsenvENKaiaqVzOEZfWyj6nS1Dl/usLaI3ubXup6ijXPNbH2FNf/mpxHRb0GALceJ+DWkwRAATRvaInmDSyLneu3HidorI5i51FUm/O+F2YmBkhJyxTmSt402l57S7vuFrY2qtOG/OXmrheqtr+4NamwMSj2OqRKmzT9Hqp6jFrYZ70mj2V19XhTV9slFl3tH50McqNux2Lb8btISE4XtllWN8JXPZqhvaO1ymkqgspSz9ISs30VNS8x84y6HYuNv95C6rt/HuxxCI9hZCCFvp5Uabu2xok67RKrDwrLo2MLa1y4Gau0Pa/S9Meuk3cRcekJ8l7vsvPUPfRyrY8vujVTOR+xXboViy0Rt0vsx8L6Ki+JBCjsWp6itpdlTKn63heXDkCx7VG3nmLNc02vPfnzBAr2g1jzqKj8TY31kZUtR3rmP2cSDp1/DFNjffh+3LxAueeinyNkXzTiNVRHsfMoLJ2pcc4hUt51Nb/C0mhj7S3rupuXOm0oLK/860Vx7S+pLqrMdbH7V9X1VCyqHqMW9lmff75V9OOeikBX2yUWXe4fnbtcOep2LFb/9FeRr4/+POehCiWlqQhvrCptqQj1LC0x21fWvPKO0wt/vxS938Voa0l5lCXv0lKnXeXZB+qWA+QEuEcuPiny9Y/ctB/o6utLcfNJIhaEXyoyTW77xOirkspQlarvvdh1Lq6eYq0/2lzHVKGteVRcuVfvxWHVnmiN1lHMPDQ1VzS19oq57pakLHnlb78257cq1FlPxSLGMaqq6bR5DFVR6UK7NBlHVdb+UfVy5fK/5k5EcrkC247fLTbNtuN3sfXYnWLTbD9+V+nJnOVBlbZUhHqWlpjtq6h5iZmnKnmUNu/SUqdd5d0H6pQD5FyiHHGp6AAXACIuPdH6/TFyuQLr9v9ZbJrtx+8iK0suSl8VV4aqY0rV914TdS6qnmLNc22vParQ1jzKb1ueub414naxacWoo1h5aHKuaGLtFXvdLUlZ8srbfk2Mu7L2r6rrqVjvoUrHqMfuYNuxkvtp69HbFfq4pyLQ1XaJpSr0j04FuXeeyEq8jC0hOR2ylIxi08Qnp+POE5mINVOfKm2pCPUsLTHbJ2Zet2MSRO93MeqnSh6lzbu01GlXefeBOuUAwMkrTwu9XDcvhSInnTbdjknAm8R3xaaJT07HyStPRemr4spQdUyp+t5ros5F1VOsNUPb65gqtDWP8kvIM9fjtTDXxcpDk3NFE2uv2OtuScqSV972a2LclbV/VV1PxXoPVTpGTclAQkrJ/SRLzayQx1AVia62SyxVoX906p5cWap4C6iYeWmy/PKuZ2mJ2T5R8yrhCxB1y1QnbXHpyvo+a2KcaGKMarIP1MkrVqba70yqmk4sqo5PbdRL7PdfU3UurHyx6l4e61hZy9PkZ4a257oYeWh6rojd3+VxbFCWvHL31dS4K1PdNPB5r418xC5TV483dbVdYqkK/aNTQa6FqVGFzEuT5Zd3PUtLzPaJmpeZoWh5qZu2uHRlfZ81MU40MUY12Qfq5GVtYaJSPqqmE4uq41Mb9RL7/ddUnQsrX6y6l8c6VtbyNPmZoe25LkYemp4rYvd3eRwblCWv3H01Ne7KVDcNfN5rIx+xy9TV401dbZdYqkL/6NTlyg71LWBZvfg3w7K6UYkLW83q/zxSv7yo0paKUM/SErN9Yubl2MBS9H4Xo36q5FHavEtLnXaVdx+oUw4AdGtnB0kJT8+XSHLSaZNjA0vUMjcuNk3N6kbo1s5OlL4qrgxVx5Sq770m6lxUPcVaM7S9jqlCW/MoP8s8c72mFua6WHlocq5oYu0Ve90tSVnyytt+TYy7svavquupWO+hSseoZoawNFMlMDWokMdQFYmutkssVaF/dCrIlUolwqPni/JVj2b4uqdDsWn692hW7r8PpUpbKkI9S0vM9lXUvMTMU5U8Spt3aanTrvLuA3XKAXKeZtjLtX6xaXq51tf67+VKpRL4fdam2DT9ezSDvr5UlL4qrgxVx5Sq770m6lxUPcWa59pee1ShrXmU31d55vrXvRyLTStGHcXKQ5NzRRNrr9jrbknKklfe9mti3JW1f1VdT8V6D1U6Ru3pgK96ltxPX3/oWKGPeyoCXW2XWKpC/+jcTwgBhf/mU83qRuhfwu/k5k9TEVSWepaWmO0rS175x6km+l2MPAv77TwAMDbUg55UorRdW+NEnXaJ1QeF5eFWwu/klqY/CvudXIkE5fY7ubnj9Oj5hwV+17Gw9on9O7llGVOqvvfFpQNU+51cVesp1jzX9DqWP0+gYD+INY+Kyt/MxACZWdlKv5Obu93nI0elcnN/miX/7+SKWUex8ygsnZmxPhQo/ndyC0ujjbW3rOtuXmYmBlAoFCq1QZXfyS2u/SXVRZW5Llb/qrueikXVY9TCPuvNjPXhU8Lv5JbHMVRFVtnbpek4qjL2j6o/IaSTQS6Q82jsO09kkKWmw8I053R7/m8jVElTEVSWepaWmO0rbV6FjVNN9LsYecrlCtyKScCtxwmABGhe3xLNG1oCQLmNE3XaJVYfFJZH3u01TAwBCZD0NqNM/ZGVJcfJK08RK0uDtYUJurWz0/oZ3Fx5x2lGRrZK/ajUJ9UMAQWQlJbTJ01tzXHvWSISktOR/DYDZqYGqGlmLGyPT3mHlNRMVK9mKFyOWpYxpep7X1y6wtoje5te6nqKNc81sY4V1/+anEdFvQYAtx4n4NaTBEABNG9oieYNLAuUmztO37xJwY2H8Rqro9h5FNXmvO+FmYkBUtIyhbmSN422197SrruFrY3qtCF/ubnrhartL25NKmwMir0O5SrNeioWVY9RC/us1+SxrK4eb1bmdmkjjqps/VPlg1widXCcUmXAcUqVAccpVQYcp1QZcJwWpGqQq1P35BIREREREVHVxiCXiIiIiIiIdAaDXCIiIiIiItIZDHKJiIiIiIhIZzDIJSIiIiIiIp3BIJeIiIiIiIh0BoNcIiIiIiIi0hkMcomIiIiIiEhnMMglIiIiIiIincEgl4iIiIiIiHQGg1wiIiIiIiLSGQxyiYiIiIiISGcwyCUiIiIiIiKdwSCXiIiIiIiIdAaDXCIiIiIiItIZDHKJiIiIiIhIZzDIJSIiIiIiIp3BIJeIiIiIiIh0BoNcIiIiIiIi0hkMcomIiIiIiEhnMMglIiIiIiIincEgl4iIiIiIiHQGg1wiIiIiIiLSGQxyiYiIiIiISGcwyCUiIiIiIiKdwSCXiIiIiIiIdAaDXCIiIiIiItIZDHKJiIiIiIhIZzDIJSIiIiIiIp3BIJeIiIiIiIh0hkShUChKs6NCoYBcrtquenpSZGfLS1MMkdZwnFJlwHFKlQHHKVUGHKdUGXCcKpNKJZBIJCWmK3WQS0RERERERFTR8HJlIiIiIiIi0hkMcomIiIiIiEhnMMglIiIiIiIincEgl4iIiIiIiHQGg1wiIiIiIiLSGQxyiYiIiIiISGcwyCUiIiIiIiKdwSCXiIiIiIiIdAaDXCIiIiIiItIZDHKJiIiIiIhIZzDIJSIiIiIiIp3BIJeIiIiIiIh0BoNcIiIiIiIi0hmiB7np6emYPXs23N3d4eLigokTJyI+Pr7Yfa5cuQJvb2+0b98eXbp0wfTp0yGTycSuGpGgNOM07759+vTBvn37NFxLqmrkcjlWrlyJLl26wNnZGcOGDcOTJ0+KTJ+QkICJEyfC1dUVbm5umD17NtLS0rRYY6qK1B2nefcbOnQoVq1apYVaUlWn7ji9e/cu/Pz80LFjR7i7u2Ps2LF4/vy5FmtMVZG64/Tvv/+Gj48PXFxc0KlTJ8ycORPJyclarHHlIXqQO2vWLPz+++9YtWoVwsPD8eDBA4wdO7bI9A8fPsSQIUPg6OiIXbt2Yfny5YiOjkZAQIDYVSMSqDtOcyUnJ2PUqFG4ffu2FmpJVU1wcDC2bduGOXPmYMeOHUJQkJGRUWj6sWPH4vHjx9i4cSN+/PFHnDlzBrNmzdJupanKUXecAkBGRgamTZuGs2fParGmVJWpM04TEhIwaNAgGBsbY/PmzQgNDUV8fDyGDh2K9PT0cqg9VRXqjNO4uDgMGjQItra22LdvH4KDgxEVFYXAwMByqHkloBDRy5cvFc2bN1ecPn1a2PbgwQOFg4OD4sqVK4Xus2zZMsWHH36okMvlwrZLly4pHBwcFDExMWJWj0ihUJRunCoUCsWJEycU77//vuLzzz9XODg4KPbu3auN6lIVkZ6ernBxcVFs3bpV2JaYmKhwcnJS/PzzzwXSX7lyReHg4KC4d++esO3s2bMKR0dHxcuXL7VSZ6p61B2nCoVCERUVpejdu7eie/fuig4dOihWrlyprepSFaXuON21a5fCxcVFkZaWJmx7/vy5wsHBQXHu3Dmt1JmqHnXH6bVr1xTjx49XZGZmCts2btyoaNu2rTaqW+mIeiY3KioKANCpUydhW+PGjWFjY4NLly4Vuk+fPn2wcOFCSCQSYVvu/xMTE8WsHhGA0o1TADh+/Dj69euHHTt2aLyOVPXcunULqampcHd3F7bVqFEDLVu2LHRcXr58GVZWVrC3txe2ubm5QSKRCGOcSGzqjlMAOHPmDLp06YL9+/ejevXq2qoqVWHqjlN3d3cEBwfD2NhY2CaV5hwiJyUlab7CVCWpO07btm2LZcuWQV9fHwBw//59HDhwAJ07d9ZanSsTfTEze/XqFSwtLWFkZKS03draGi9fvix0n7wHaLlCQ0NhZWUFR0dHMatHBKB04xQA5s+fr+mqURWWO/bq1q2rtL2ocfnq1asCaQ0NDWFhYYEXL15orqJUpak7TgFg/PjxGq8XUV7qjlM7OzvY2dkpbVu3bh2MjY3h6uqquYpSlVaa9TRXr1698OjRI9ja2iIoKEhjdazM1Apynz59iu7duxf5ekBAAAwNDQtsNzIyUvmehoULF+L06dMICgqCgYGBOtUjAqCdcUokttwHRuUfm0ZGRoVe1ZKWlsZxTFqn7jglKg9lHaebN2/Gli1bMGPGDNSsWVMjdSQqyzhdsmQJ0tLSsHjxYgwcOBAHDhyAqampxupaGakV5NrY2ODw4cNFvn7mzJlCb5ROT0+HiYlJsXlnZmZi5syZ2L9/P+bMmYMePXqoUzUigSbHKZGm5F4ml5GRoXTJXFHj0tjYuMhxXK1aNc1VlKo0dccpUXko7ThVKBT48ccfsWbNGowcORLe3t4arytVXWVZT9u0aQMACAoKQteuXXHs2DF89tlnGqtrZaRWkGtgYFDo5cW5bt++DZlMhoyMDKVvJWJjY2FjY1PkfikpKfD398fly5exbNkyfPzxx+pUi0iJpsYpkSblXq4UGxuLBg0aCNtjY2MLvXWjTp06OH78uNK2jIwMyGQyWFtba7ayVGWpO06JykNpxmlmZiamTp2KQ4cOYerUqfD19dVGVakKU3ecPnjwADExMXj//feFbTY2NrCwsMCrV680Xt/KRtQHT7Vv3x5yuVzpoScPHz7Eq1evirynISMjA8OHD0d0dDTCwsIY4JLGlWacEmla8+bNYWZmhgsXLgjbkpKScOPGjULHpaurK16+fInHjx8L2y5evAggZ4wTaYK645SoPJRmnE6ZMgVHjhzB0qVLGeCSVqg7Ts+dO4exY8cqPQwtJiYGCQkJxZ7cqapEDXJtbGzQu3dvzJgxAxcuXEB0dDQmTJgANzc3ODs7A8gJal+/fi1cZhcSEoKoqCjMmTMHTZo0wevXr4V/xf3mHlFplWacEmmaoaEhBgwYgCVLluDEiRO4desWxo8fjzp16uDDDz9EdnY2Xr9+jXfv3gHIecpiu3btMH78eERHRyMyMhIzZ87EZ599xisSSGPUHadE5UHdcbpv3z4cPnwY48ePh5ubm9KxKMcyaYq649TT0xMWFhaYPHky7t69i8uXL2Ps2LFwcnLCBx98UM6tqXhEDXIBYM6cOXB3d4e/vz+GDBmCJk2aYOXKlcLrV69ehYeHB65evQoAOHToEBQKBSZMmAAPDw+lf7lpiMSm7jgl0oaxY8fCy8sLM2bMQP/+/aGnp4ewsDAYGBjgxYsX8PDwEO43l0gkCAoKgp2dHXx8fDBu3Di89957mDVrVvk2gnSeOuOUqLyoM04PHToEAFi0aFGBY1GOZdIkdcaphYUFwsPDAQD9+/fH6NGj0bJlS4SFhUFPT688m1EhSRQKhaK8K0FEREREREQkBtHP5BIRERERERGVFwa5REREREREpDMY5BIREREREZHOYJBLREREREREOoNBLhEREREREekMBrlERERERESkMxjkEhERERERkc5gkEtEREREREQ6g0EuERFVSo6Ojli1apXo+Xp7e8Pb21vUPAMDA9GtWzdR88zVrVs3BAYGaiTvskhJScGIESPQtm1buLq64tGjRyrtp6n3lYiIqg798q4AERFRRfLdd9+VdxV0wv79+3Hq1CnMnDkTzZo1g52dXXlXiYiIqggGuURERHk0bdq0XMq9du0aLCws0KhRI2Hb3bt3kZWVhRYtWpRLncpCJpMBAL766itIJJLyrQwREVUpvFyZiIhEdfnyZQwYMABt27aFm5sbvvnmG8THxyulefDgAfz9/eHm5gZXV1cMHz4c9+/fF15PTk7GggUL0KNHD7Rp0waenp7Ys2dPseXGxsZi6tSp6Nq1K5ycnODl5YUTJ04opUlJScHMmTPh7u4OFxcXjB8/Hhs3boSjo6OQJv/lyhkZGVixYgW6d+8OJycneHp64qeffhJez87Oxrp16+Dp6QknJyc4OzujX79+iIyMVLnPFAoFZs+eDR8fHzx58gQAcP/+ffj4+GD+/Pkl7p+ZmYlFixahc+fOcHZ2xuDBg/H48WOlNLt378Z//vMfODs7w8nJCf/+97/x66+/Cq/v27cPLVu2xO7du9G5c2e4ubnh3r178Pb2xsyZMxEcHIwuXbqgbdu2GDZsGOLi4rB371707NkTLi4u8PX1xdOnT4U+zL3kuHnz5ggMDMSFCxfg6OiICxcuKNVLE5eHExFR1cYgl4ioiouPj8eNGzdEyevSpUvw9fWFsbExVqxYgWnTpuHixYsYOHAg3r17BwB49eoVvvzySzx69AizZs3C4sWLERcXBx8fH8hkMrx79w5fffUVfv75ZwwdOhTBwcFo3749pk+fjrVr1xZablxcHLy8vHD58mWMHz8eq1atgq2tLUaPHo2DBw8K6UaNGoVff/0VY8aMwfLly5GamoqlS5cW26ZJkyZhw4YN6Nu3L0JCQuDh4YHAwEAcOnQIALBkyRIEBwfjyy+/xP/+9z/MmTMHMpkMAQEBSEtLU6nfJBIJVq9eDT09PQwcOBB//PEHfHx8YG5ujhUrVpS4/+HDh3H37l388MMP+O677/DXX39h/Pjxwutbt27FzJkz0aNHD4SEhGDJkiUwNDTEpEmT8PLlSyFddnY21q9fj3nz5mHq1Kmwt7cHABw6dAjnz5/HvHnzMH36dJw/fx4DBgzApk2b8M033+D777/H9evX8f333wPIueTby8sLALBz506MGjVKpX4Q28WLF5GRkVEuZRMRUfnh5cpERFWYTCaDn58fFi1aJEp+S5cuRePGjRESEgI9PT0AQNu2bdG7d2/s3bsXX3/9NTZu3IiMjAxs2LABVlZWAHLO9vXv3x/Xr1/Hs2fPcOfOHezYsQMuLi4AgC5duiArKwvBwcHo168fLCwslMrdsGED4uPjERERAVtbWwBA165d4evri0WLFsHT0xMXLlzAhQsXsGrVKnz44YcAgPfeew+enp5KZ5HzunPnDiIiIjBt2jT4+PgAANzd3fHs2TNcuHABnp6eiI2Nxfjx45XORhoZGWHMmDG4ffs2nJ2dVeq7evXqYdOmTfD29sbgwYPRsGFDhIeHo1atWiXua2Njg+DgYBgYGAAAHj9+jDVr1iAlJQVmZmZ48uQJhgwZohRs2tra4j//+Q+ioqLQu3dvYfuIESPw/vvvK+WflZWFoKAgmJubAwCOHj2Ks2fP4vjx46hfvz6AnMutDxw4ACDnku86deoAgND+Fy9eqNQPYkpJScH48eOxfPlyGBoaar18IiIqHzyTS0RURd2/fx/9+/eHh4cHmjRpUub80tLScP36dXTt2hUKhQJZWVnIyspC/fr1YW9vjz/++AMAEBUVBWdnZyHABYA6derg1KlT6Nq1Ky5evAhbW1shwM3Vp08fpKen4/r16wXKvnjxIlxcXIQAN+8+r1+/xoMHDxAZGQkDAwP06NFDeF0qleKTTz4psk1RUVEAIATFuVatWoU5c+YAyAnsfXx8EB8fj8uXL2Pv3r3C2WN1zyLmv3dVoVCotJ+Tk5MQ4AIQHvKUlJQEIOfpzpMmTUJSUpIQjG7durXQOhZ2/6+9vb0Q4AJA7dq1YWlpKQS4AGBhYYHk5GSV6qst3bp1g56ennB5NRERVQ08k0tEVAVdu3YN3t7eyMjIQEREBI4fP67yvoaGhti3b1+B7UlJSZDL5QgNDUVoaGiB142MjADknD0u7km7iYmJSgFwrtq1awvlFLZP3oCrsH0SEhJgYWEBqVT5+93izpTmPjypuDR//vknZs+ejT///BMmJiZo2rQp6tWrB0D1IBUAXr58CR8fH0ilUmzcuBGTJ0+Gj48PNm/eXGh/5FWtWjWlv3PbKJfLAQAxMTGYOXMmzp8/DwMDAzRp0gTNmzcvtI758wIAMzOzEsvUhlevXmHIkCFq7ZOWloanT5+id+/eOHjwIGxsbDRUOyIiqigY5BIRVUHOzs5Yvnw5vv32WwwePBh9+/Ytc56mpqaQSCTw9fVVuvw1l4mJCQCgevXqBR5EBQDnz5+HnZ0dzM3NCzw0CQBev34NALC0tCzwmrm5ufB6UfvY2NggISEBcrlcKdB98+ZNkW2qUaMGgJz7lnMvvwVyzoLLZDI4Ojpi6NChcHR0xC+//IImTZpAKpXizJkziIiIKDLf/BQKBUaNGoXs7Gxs2bIFtra2CA8Ph7e3N8aNGyecdS0NuVwOPz8/GBgYYM+ePWjRogX09fVx79494fJibcg9S50beOdKTU2FqampSnnY2NgI90KrauHChTh9+jQWLlzIAJeIqIrg5cpERFVUjx49sGPHDoSFhRUaIKrLzMwMLVu2xIMHD9CmTRvhX7NmzbBq1SrhqbodOnTA9evXlQLdN2/eYOjQoThz5gxcXV3x7NkzXL16VSn/gwcPwsDAAE5OTgXKdnV1xdWrV/Hs2bMC+1hZWaFhw4Zwc3NDVlYWTp48KbyuUCiKPYvdvn17AFDaB8h52NS8efPw4MEDyGQyDBw4EE2bNhWC599++w1AwYCuKBKJBDNmzEB4eLhwybW9vT02btyI6dOnq5RHURISEvDw4UN4eXmhTZs20NfXL1Udyyr3bHDeB10lJiYWeT+0GKKjo3H58mXs2LGj0HFDRES6iWdyiYiqsIYNG2L16tW4c+dOiZfEqmLChAnw8/PDxIkT0adPH+FpvdevXxceeuTr64v9+/dj6NChGD58OAwMDLBmzRrUqVMHn376KQwNDbFt2zaMHj0aY8eOhZ2dHU6ePIm9e/fC399fOLua16BBg3Dw4EH4+vrC398fFhYW2L9/PyIjIzF//nxIpVK4urqic+fOmD59OuLi4lCvXj3s2bMHt2/fLvJ3XJs3b46PPvoIixcvxrt379CiRQv89ttvOHXqFIKCgtC4cWOYmZlh7dq10NfXh76+PiIiIoSfO1L16coA0K5duwLbHBwcVN6/KLVq1YKtrS22bt2KOnXqoEaNGjh79iw2bdqkdh3LwtHREXXr1sXq1athZmYGiUSCkJAQ4Qy/JsTExGDdunVK9xMTEZHu45lcIqIqzt7eHp07dxYlLw8PD4SFheHly5cYO3YspkyZAj09PWzYsEF4ym7dunWxbds2WFtbIzAwEFOnTkXdunURHh4Oc3NzmJiYYPPmzfjggw/w448/YuTIkYiKisK8efMwZsyYQsu1srLC9u3b0apVK8ydOxcBAQF48eIFgoOD8d///ldIt3z5cnTr1g1Lly5FQEAADA0N0b9//2LvL128eDG8vb0RHh6O4cOHIzIyEitXrkSPHj1QvXp1BAcHQ6FQICAgAFOmTMHz58+xZcsWmJqa4vLly6L0a1kFBwfDxsYGgYGBGDduHK5fv441a9agSZMmWqujnp4eVq5cidq1a2PChAmYN28eevfuXeChXmLy9PQs9PJ2IiLSbRKFOk/FICIiqqSePXuGa9euoXv37jA2Nha2jx07Fk+ePMFPP/1UjrUjIiIisfByZSIiqhKkUikCAwPRvXt3eHl5QU9PD2fPnsXRo0exYMGC8q4eERERiYRncomIqMqIjIzE6tWrcfPmTWRlZcHe3h6DBg2Cp6dneVeNiIiIRMIgl4iIiIiIiHQGHzxFREREREREOoNBLhEREREREekMBrlERERERESkMxjkEhERERERkc5gkEtEREREREQ6g0EuERERERER6QwGuURERERERKQzGOQSERERERGRzvg/jnRCkS+Bj3AAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1200x100 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "## plot the result\n",
        "fig = plt.figure(figsize=(12,1))\n",
        "ax = fig.add_subplot(1,1,1)\n",
        "ax.scatter(projections,y=np.zeros(len(projections)))\n",
        "for i in range(len(projections)):\n",
        "    if words[i] in [\"bleach\", \"fabric\", \"moisturizer\", \"deodorant\"]:\n",
        "        plt.text(projections[i]-0.01,0.01,words[i])\n",
        "ax.set_xlabel(f\"← {w1} × {w2} →\")\n",
        "ax.set_yticks([])\n",
        "plt.show()\n",
        "\n",
        "plt.savefig('Projection.png', dpi=300)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88Yggg_Ihou_"
      },
      "source": [
        "### Create a dataframe from results\n",
        "\n",
        "Create a dictionary out of it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-9-DteCthou_"
      },
      "outputs": [],
      "source": [
        "keywords_projections = dict(zip(keywords, projections))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oMjVn-skhou_"
      },
      "source": [
        "Add it back to the original dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hAxJo1TLhou_",
        "outputId": "0aa5a926-916c-4abe-a385-ca4b4ca0b444"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'bleach': 0.18269129,\n",
              " 'kitchen': -0.17648973,\n",
              " 'dishwasher': 0.040390026,\n",
              " 'window': -0.08415529,\n",
              " 'fabric': -0.1073504,\n",
              " 'laundry': -0.020215979,\n",
              " 'moisture': 0.16497886,\n",
              " 'disinfectant': 0.22512498,\n",
              " 'kettle': 0.010093902,\n",
              " 'spot': -0.052106146,\n",
              " 'glass': 0.072442845,\n",
              " 'stain': 0.1215781,\n",
              " 'machine': 0.034488656,\n",
              " 'odor': 0.27311346,\n",
              " 'detergent': 0.12791674,\n",
              " 'bathroom': -0.019817427,\n",
              " 'soap': 0.013865879,\n",
              " 'washing': 0.09492996,\n",
              " 'flashlight': 0.13304643,\n",
              " 'mouthwash': 0.21876492,\n",
              " 'contact': -0.07028686,\n",
              " 'prescription': 0.08751321,\n",
              " 'razor': 0.11169241,\n",
              " 'lotion': 0.09550172,\n",
              " 'deodorant': 0.13090566,\n",
              " 'sponges': -0.032149993,\n",
              " 'extension': -0.1517566,\n",
              " 'eye': 0.008585946,\n",
              " 'hydrogen': 0.17347816,\n",
              " 'allergy': 0.18052365,\n",
              " 'rubbing': 0.13179345,\n",
              " 'cough': 0.3156739,\n",
              " 'antibiotic': 0.16005474,\n",
              " 'light': 0.03893719,\n",
              " 'conditioner': 0.18681106,\n",
              " 'insect': 0.016078198,\n",
              " 'vitamins': 0.05060758,\n",
              " 'paper': -0.04610566,\n",
              " 'bar': 0.0026715558,\n",
              " 'toothpaste': 0.2410744,\n",
              " 'cotton': 0.0735845,\n",
              " 'toothbrush': 0.09180973,\n",
              " 'face': -0.030464083,\n",
              " 'thermometer': 0.23000024,\n",
              " 'first': -0.11862117,\n",
              " 'moisturizer': 0.017245635,\n",
              " 'disinfecting': 0.16192564,\n",
              " 'dish': -0.18180534,\n",
              " 'toilet': -0.03572768,\n",
              " 'hand': -0.07323136,\n",
              " 'facial': 0.012623558,\n",
              " 'cleaning': 0.06098247,\n",
              " 'reading': -0.073951714,\n",
              " 'hot': 0.029093029,\n",
              " 'nasal': 0.22404191,\n",
              " 'body': -0.0964644,\n",
              " 'sunscreen': 0.1878244,\n",
              " 'pain': 0.07799299,\n",
              " 'acne': 0.20240116,\n",
              " 'bandages': 0.17146039,\n",
              " 'shaving': 0.10158101,\n",
              " 'trash': 0.1534238,\n",
              " 'batteries': 0.1358005,\n",
              " 'floss': 0.041695595}"
            ]
          },
          "execution_count": 292,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "keywords_projections"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KVGYLiy8hovA",
        "outputId": "343580cf-df54-45b5-ef41-ffa5a62ebe2f"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>keyword</th>\n",
              "      <th>desc_green_count</th>\n",
              "      <th>keyword1</th>\n",
              "      <th>keyword2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acne+Treatment</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>acne</td>\n",
              "      <td>acne</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Allergy+Medications</td>\n",
              "      <td>1.291667</td>\n",
              "      <td>allergy</td>\n",
              "      <td>allergy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Antibiotic+Ointment</td>\n",
              "      <td>3.375000</td>\n",
              "      <td>antibiotic</td>\n",
              "      <td>antibiotic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bandages</td>\n",
              "      <td>1.229730</td>\n",
              "      <td>bandages</td>\n",
              "      <td>bandages</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bar+Soap</td>\n",
              "      <td>3.788462</td>\n",
              "      <td>bar</td>\n",
              "      <td>bar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Vitamins</td>\n",
              "      <td>4.041667</td>\n",
              "      <td>vitamins</td>\n",
              "      <td>vitamins</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Washing+powder</td>\n",
              "      <td>1.176471</td>\n",
              "      <td>washing</td>\n",
              "      <td>washing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Window+cleaner</td>\n",
              "      <td>1.642857</td>\n",
              "      <td>window</td>\n",
              "      <td>window</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Window+spray</td>\n",
              "      <td>1.981481</td>\n",
              "      <td>window</td>\n",
              "      <td>window</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>bleach+tablets</td>\n",
              "      <td>2.458333</td>\n",
              "      <td>bleach</td>\n",
              "      <td>bleach</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                keyword  desc_green_count    keyword1    keyword2\n",
              "0        Acne+Treatment          2.166667        acne        acne\n",
              "1   Allergy+Medications          1.291667     allergy     allergy\n",
              "2   Antibiotic+Ointment          3.375000  antibiotic  antibiotic\n",
              "3              Bandages          1.229730    bandages    bandages\n",
              "4              Bar+Soap          3.788462         bar         bar\n",
              "..                  ...               ...         ...         ...\n",
              "66             Vitamins          4.041667    vitamins    vitamins\n",
              "67       Washing+powder          1.176471     washing     washing\n",
              "68       Window+cleaner          1.642857      window      window\n",
              "69         Window+spray          1.981481      window      window\n",
              "70       bleach+tablets          2.458333      bleach      bleach\n",
              "\n",
              "[71 rows x 4 columns]"
            ]
          },
          "execution_count": 293,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "keyword_results = pd.DataFrame(mean_green_desc).reset_index()\n",
        "\n",
        "keyword_results[\"keyword1\"] = keyword_results[\"keyword\"].apply(lambda word: word.split('+', 1)[0].lower())\n",
        "keyword_results[\"keyword2\"] = keyword_results[\"keyword\"].apply(lambda word: word.split('+', 1)[0].lower())\n",
        "\n",
        "keyword_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ra8TgR1ohovA",
        "outputId": "8ea65ef6-8860-4c61-ce1c-01cf5585d73c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0           acne\n",
              "1        allergy\n",
              "2     antibiotic\n",
              "3       bandages\n",
              "4            bar\n",
              "         ...    \n",
              "66      vitamins\n",
              "67       washing\n",
              "68        window\n",
              "69        window\n",
              "70        bleach\n",
              "Length: 71, dtype: object"
            ]
          },
          "execution_count": 294,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "keyword_results.apply(lambda keyword: keyword['keyword1'], axis = 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtXLaN4xhovA"
      },
      "source": [
        "If there is more than one keyword, we take the maximum of the two"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DdqocjHIhovB"
      },
      "outputs": [],
      "source": [
        "keyword_results['perceived_harm'] = keyword_results.apply(lambda keyword:\n",
        "                      np.mean([keywords_projections[keyword['keyword1']], \n",
        "                       keywords_projections[keyword['keyword2']]]), axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qi_kMFgahovB",
        "outputId": "728636c8-d133-4670-ca84-7306ce544762"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>keyword</th>\n",
              "      <th>desc_green_count</th>\n",
              "      <th>keyword1</th>\n",
              "      <th>keyword2</th>\n",
              "      <th>perceived_harm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acne+Treatment</td>\n",
              "      <td>2.166667</td>\n",
              "      <td>acne</td>\n",
              "      <td>acne</td>\n",
              "      <td>0.202401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Allergy+Medications</td>\n",
              "      <td>1.291667</td>\n",
              "      <td>allergy</td>\n",
              "      <td>allergy</td>\n",
              "      <td>0.180524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Antibiotic+Ointment</td>\n",
              "      <td>3.375000</td>\n",
              "      <td>antibiotic</td>\n",
              "      <td>antibiotic</td>\n",
              "      <td>0.160055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bandages</td>\n",
              "      <td>1.229730</td>\n",
              "      <td>bandages</td>\n",
              "      <td>bandages</td>\n",
              "      <td>0.171460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bar+Soap</td>\n",
              "      <td>3.788462</td>\n",
              "      <td>bar</td>\n",
              "      <td>bar</td>\n",
              "      <td>0.002672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>Vitamins</td>\n",
              "      <td>4.041667</td>\n",
              "      <td>vitamins</td>\n",
              "      <td>vitamins</td>\n",
              "      <td>0.050608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Washing+powder</td>\n",
              "      <td>1.176471</td>\n",
              "      <td>washing</td>\n",
              "      <td>washing</td>\n",
              "      <td>0.094930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Window+cleaner</td>\n",
              "      <td>1.642857</td>\n",
              "      <td>window</td>\n",
              "      <td>window</td>\n",
              "      <td>-0.084155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Window+spray</td>\n",
              "      <td>1.981481</td>\n",
              "      <td>window</td>\n",
              "      <td>window</td>\n",
              "      <td>-0.084155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>bleach+tablets</td>\n",
              "      <td>2.458333</td>\n",
              "      <td>bleach</td>\n",
              "      <td>bleach</td>\n",
              "      <td>0.182691</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>71 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                keyword  desc_green_count    keyword1    keyword2  \\\n",
              "0        Acne+Treatment          2.166667        acne        acne   \n",
              "1   Allergy+Medications          1.291667     allergy     allergy   \n",
              "2   Antibiotic+Ointment          3.375000  antibiotic  antibiotic   \n",
              "3              Bandages          1.229730    bandages    bandages   \n",
              "4              Bar+Soap          3.788462         bar         bar   \n",
              "..                  ...               ...         ...         ...   \n",
              "66             Vitamins          4.041667    vitamins    vitamins   \n",
              "67       Washing+powder          1.176471     washing     washing   \n",
              "68       Window+cleaner          1.642857      window      window   \n",
              "69         Window+spray          1.981481      window      window   \n",
              "70       bleach+tablets          2.458333      bleach      bleach   \n",
              "\n",
              "    perceived_harm  \n",
              "0         0.202401  \n",
              "1         0.180524  \n",
              "2         0.160055  \n",
              "3         0.171460  \n",
              "4         0.002672  \n",
              "..             ...  \n",
              "66        0.050608  \n",
              "67        0.094930  \n",
              "68       -0.084155  \n",
              "69       -0.084155  \n",
              "70        0.182691  \n",
              "\n",
              "[71 rows x 5 columns]"
            ]
          },
          "execution_count": 296,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "keyword_results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzxTAq8BhovB"
      },
      "source": [
        "## Compare the two variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FfAo9u6JhovB"
      },
      "source": [
        "Plot the two variables: perceived harm (vector representation) and frequency of use of green words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7zflQbKNhovC",
        "outputId": "0489021a-6ad1-410d-da9f-00c0e9dabbc2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "\n",
        "sns.set(color_codes=True)\n",
        "np.random.seed(sum(map(ord, \"regression\")))\n",
        "sns.lmplot(x = 'perceived_harm', y = 'desc_green_count', data = keyword_results).set(\n",
        "    title='Description compensation \\n results',\n",
        "    xlabel='Perceived harm',\n",
        "    ylabel='Green decription occurance'\n",
        "    )\n",
        "\n",
        "plt.savefig('Results1.png', dpi=300, bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C3M5OTKthovC"
      },
      "outputs": [],
      "source": [
        "from random import seed\n",
        "from random import gauss\n",
        "\n",
        "seed(42)\n",
        "\n",
        "\n",
        "ideal_data = pd.DataFrame(\n",
        "    {'ideal_perceived_harm' : range(1, 100, 4)}\n",
        ")\n",
        "\n",
        "ideal_data['ideal_perceived_harm'] = ideal_data['ideal_perceived_harm'].apply(lambda x: x / 100 - 0.5).copy()\n",
        "\n",
        "ideal_data['ideal_perceived_harm'] = ideal_data['ideal_perceived_harm'].apply(lambda x: x + gauss(0, 1))\n",
        "\n",
        "ideal_data['ideal_desc_green'] = ideal_data['ideal_perceived_harm'].apply(lambda x: (x + gauss(0, 0.3) + 1) * 6).copy()\n",
        "\n",
        "\n",
        "\n",
        "# ideal_x = ideal_x / 100 - 0.5\n",
        "# ideal_y = ideal_x * 10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tu6q-Uf4hovD"
      },
      "source": [
        "Create an ideal graph for visualising during the in-class presentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AbGm8CbbhovD",
        "outputId": "c8db5edf-9ed2-47c4-dbb9-39d559c9c151"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set(color_codes=True)\n",
        "np.random.seed(sum(map(ord, \"regression\")))\n",
        "sns.lmplot(x = 'ideal_perceived_harm', y = 'ideal_desc_green', data = ideal_data).set(\n",
        "    title='Description compensation \\n ideal case',\n",
        "    xlabel='Perceived harm',\n",
        "    ylabel='Green decription occurance'\n",
        "    )\n",
        "\n",
        "plt.savefig('Ideal_case1.png', dpi=300, bbox_inches='tight')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "orig_nbformat": 4
  },
  "nbformat": 4,
  "nbformat_minor": 0
}