CS410_Sentiment_Analysis_Course_Project.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Yelp Data - Business Review Sentiment Analysis"
      ],
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        "id": "9uLrTWQ1_DAU"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "## 1. Topics to be explored\n",
        "\n",
        "1. Business categories\n",
        "2. Number of stars in reviews\n",
        "3. Number of businesses open and closed\n",
        "4. Sentiment analysis on all reviews"
      ],
      "metadata": {
        "id": "AUvyWrUo_eIT"
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      "cell_type": "markdown",
      "source": [
        "## 2. Start by downloading files and importing libraries for reading the data"
      ],
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      "cell_type": "code",
      "source": [
        "# Download files\n",
        "\n",
        "# Business data\n",
        "!wget -q --show-progress --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1vCSG6KgUYo0wpn2_8-tb0YZhMgY4wk1h' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1vCSG6KgUYo0wpn2_8-tb0YZhMgY4wk1h\" -O yelp_businesses.json && rm -rf /tmp/cookies.txt\n",
        "\n",
        "# Reviews data\n",
        "!wget -q --show-progress --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1GuxASYHSWWtGhaCJeh57WCA3RKsjJzaO' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=1GuxASYHSWWtGhaCJeh57WCA3RKsjJzaO\" -O yelp_reviews.json && rm -rf /tmp/cookies.txt\n"
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      "execution_count": 1,
      "outputs": [
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          "text": [
            "yelp_businesses.jso 100%[===================>] 113.36M   128MB/s    in 0.9s    \n",
            "yelp_reviews.json   100%[===================>]   4.97G   129MB/s    in 53s     \n"
          ]
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      "execution_count": 2,
      "metadata": {
        "id": "1osuW9_J3R8V"
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      "source": [
        "# General data analysis libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "# Reviews data types\n",
        "review_dtypes = {\n",
        "    'stars': np.float16\n",
        "}\n",
        "\n",
        "business_dtypes = {\n",
        "    'is_open': np.bool8\n",
        "}\n",
        "\n",
        "reviews_data = pd.read_json('yelp_reviews.json',\n",
        "                            lines=True,\n",
        "                            chunksize=2000,\n",
        "                            dtype=review_dtypes,\n",
        "                            orient='records')\n",
        "\n",
        "business_df = pd.read_json('yelp_businesses.json',\n",
        "                           lines=True,\n",
        "                           dtype=business_dtypes)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Only looking at data from 2018 and beyond"
      ],
      "metadata": {
        "id": "P95SFT6EX58r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Reviews chunk storage\n",
        "reviews_chunks = []\n",
        "\n",
        "for chunk in reviews_data:\n",
        "  # Drop unused columns and start from 2018 onward\n",
        "  min_chunk = chunk.drop(columns=['user_id', 'review_id']).query(\"`date` >= '2018-01-01'\")\n",
        "  reviews_chunks.append(min_chunk)\n",
        "\n",
        "reviews_df = pd.concat(reviews_chunks, ignore_index=True)"
      ],
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      "execution_count": 3,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "Sample the business data"
      ],
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      "cell_type": "code",
      "source": [
        "business_df.head(5)"
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              "              business_id                      name  \\\n",
              "0  Pns2l4eNsfO8kk83dixA6A  Abby Rappoport, LAC, CMQ   \n",
              "1  mpf3x-BjTdTEA3yCZrAYPw             The UPS Store   \n",
              "2  tUFrWirKiKi_TAnsVWINQQ                    Target   \n",
              "3  MTSW4McQd7CbVtyjqoe9mw        St Honore Pastries   \n",
              "4  mWMc6_wTdE0EUBKIGXDVfA  Perkiomen Valley Brewery   \n",
              "\n",
              "                           address           city state postal_code  \\\n",
              "0           1616 Chapala St, Ste 2  Santa Barbara    CA       93101   \n",
              "1  87 Grasso Plaza Shopping Center         Affton    MO       63123   \n",
              "2             5255 E Broadway Blvd         Tucson    AZ       85711   \n",
              "3                      935 Race St   Philadelphia    PA       19107   \n",
              "4                    101 Walnut St     Green Lane    PA       18054   \n",
              "\n",
              "    latitude   longitude  stars  review_count  is_open  \\\n",
              "0  34.426679 -119.711197    5.0             7    False   \n",
              "1  38.551126  -90.335695    3.0            15     True   \n",
              "2  32.223236 -110.880452    3.5            22    False   \n",
              "3  39.955505  -75.155564    4.0            80     True   \n",
              "4  40.338183  -75.471659    4.5            13     True   \n",
              "\n",
              "                                          attributes  \\\n",
              "0                      {'ByAppointmentOnly': 'True'}   \n",
              "1             {'BusinessAcceptsCreditCards': 'True'}   \n",
              "2  {'BikeParking': 'True', 'BusinessAcceptsCredit...   \n",
              "3  {'RestaurantsDelivery': 'False', 'OutdoorSeati...   \n",
              "4  {'BusinessAcceptsCreditCards': 'True', 'Wheelc...   \n",
              "\n",
              "                                          categories  \\\n",
              "0  Doctors, Traditional Chinese Medicine, Naturop...   \n",
              "1  Shipping Centers, Local Services, Notaries, Ma...   \n",
              "2  Department Stores, Shopping, Fashion, Home & G...   \n",
              "3  Restaurants, Food, Bubble Tea, Coffee & Tea, B...   \n",
              "4                          Brewpubs, Breweries, Food   \n",
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              "                                               hours  \n",
              "0                                               None  \n",
              "1  {'Monday': '0:0-0:0', 'Tuesday': '8:0-18:30', ...  \n",
              "2  {'Monday': '8:0-22:0', 'Tuesday': '8:0-22:0', ...  \n",
              "3  {'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...  \n",
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              "      <td>Shipping Centers, Local Services, Notaries, Ma...</td>\n",
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              "      <td>tUFrWirKiKi_TAnsVWINQQ</td>\n",
              "      <td>Target</td>\n",
              "      <td>5255 E Broadway Blvd</td>\n",
              "      <td>Tucson</td>\n",
              "      <td>AZ</td>\n",
              "      <td>85711</td>\n",
              "      <td>32.223236</td>\n",
              "      <td>-110.880452</td>\n",
              "      <td>3.5</td>\n",
              "      <td>22</td>\n",
              "      <td>False</td>\n",
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              "      <td>Department Stores, Shopping, Fashion, Home &amp; G...</td>\n",
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              "      <td>MTSW4McQd7CbVtyjqoe9mw</td>\n",
              "      <td>St Honore Pastries</td>\n",
              "      <td>935 Race St</td>\n",
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              "      <td>PA</td>\n",
              "      <td>19107</td>\n",
              "      <td>39.955505</td>\n",
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              "      <td>mWMc6_wTdE0EUBKIGXDVfA</td>\n",
              "      <td>Perkiomen Valley Brewery</td>\n",
              "      <td>101 Walnut St</td>\n",
              "      <td>Green Lane</td>\n",
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            "text/plain": [
              "              business_id  stars  useful  funny  cool  \\\n",
              "0  XQfwVwDr-v0ZS3_CbbE5Xw    3.0       0      0     0   \n",
              "1  cPepkJeRMtHapc_b2Oe_dw    4.0       1      0     1   \n",
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              "4  I6L0Zxi5Ww0zEWSAVgngeQ    4.0       0      0     0   \n",
              "\n",
              "                                                text                date  \n",
              "0  If you decide to eat here, just be aware it is... 2018-07-07 22:09:11  \n",
              "1  I was really between 3 and 4 stars for this on... 2018-07-17 03:30:07  \n",
              "2  My boyfriend and I tried this deli for the fir... 2018-08-23 21:39:38  \n",
              "3  Amazing biscuits and (fill in the blank). Grea... 2018-04-27 23:03:21  \n",
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              "      <th>1</th>\n",
              "      <td>cPepkJeRMtHapc_b2Oe_dw</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>I was really between 3 and 4 stars for this on...</td>\n",
              "      <td>2018-07-17 03:30:07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>kq5Ghhh14r-eCxlVmlyd8w</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>My boyfriend and I tried this deli for the fir...</td>\n",
              "      <td>2018-08-23 21:39:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Zx7n8mdt8OzLRXVzolXNhQ</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Amazing biscuits and (fill in the blank). Grea...</td>\n",
              "      <td>2018-04-27 23:03:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I6L0Zxi5Ww0zEWSAVgngeQ</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>The cafe was extremely cute. We came at 8am an...</td>\n",
              "      <td>2018-07-07 20:50:12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-239b4f57-411e-48d8-b395-12b1be209e98')\"\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-239b4f57-411e-48d8-b395-12b1be209e98 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-239b4f57-411e-48d8-b395-12b1be209e98');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Join all categories into one large string\n",
        "joint_categories = ', '.join(business_df['categories'].dropna())\n",
        "\n",
        "# Make a list with each separate category as an entry\n",
        "categories = pd.DataFrame(joint_categories.split(', '), columns=['category'])\n",
        "\n",
        "# Get a series count of unique values\n",
        "categories_series = categories.category.value_counts()\n",
        "categories_df = pd.DataFrame(categories_series)\n",
        "\n",
        "# Use default index\n",
        "categories_df.reset_index(inplace=True)\n",
        "\n",
        "# Build plot for visualization\n",
        "plt.figure(figsize=(12,10))\n",
        "categories_axis = sns.barplot(y = 'index', x = 'category', data = categories_df.iloc[0:20], palette='hls')\n",
        "categories_axis.set_ylabel('Category')\n",
        "categories_axis.set_xlabel('Number of businesses')\n",
        "categories_axis.set_title('Number of businesses by category')\n",
        "\n",
        "for p in categories_axis.patches:\n",
        "    categories_axis.annotate(int(p.get_width()),\n",
        "                ((p.get_x() + p.get_width()),\n",
        "                 p.get_y()),\n",
        "                 xytext=(2, -16),\n",
        "                fontsize=16,\n",
        "                color='#00244d',\n",
        "                textcoords='offset points',\n",
        "                horizontalalignment='right')\n",
        " \n",
        "plt.show()   "
      ],
      "metadata": {
        "id": "GWqBcLcGnvyk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "9aa7e32f-0325-4ed9-c468-019f96c273f0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Get a count of only restaurant- or food-related categories"
      ],
      "metadata": {
        "id": "HjVR5ZrMk5nI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "related_cats = ['Restaurants', 'Food', 'Bars', 'Sandwiches', 'American (Traditional)', 'Pizza', 'Coffee & Tea', 'Fast Food', 'Breakfast & Brunch', 'American (New)']\n",
        "\n",
        "# Filter for all the related categories\n",
        "related_df = categories.where(lambda category : category.isin(related_cats)).dropna()\n",
        "\n",
        "len(related_df)"
      ],
      "metadata": {
        "id": "Sb7MNRAJqWGr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28f0af75-cec2-4d5f-dedd-eeb4e358bdd2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "140223"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Filter only businesses that are food related"
      ],
      "metadata": {
        "id": "3zg3M2y3MpFX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "food_businesses = business_df.query(\"categories in @related_cats\")\n",
        "food_businesses.head(5)"
      ],
      "metadata": {
        "id": "XkNnEgn9Mve0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "21672dac-956e-4e8d-fb66-3680cb0d2bb0"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 business_id                                           name  \\\n",
              "3655  x2J-YIFeGZ-nsezzooVA9g                                      Twenty 21   \n",
              "4764  mRWlzIJpyQgatq_J7dzxWw  Audubon Aquarium of the Americas - Food Court   \n",
              "5142  KuLi91b-N_0XY_3IA72RJQ                           MexiPhilly Food Cart   \n",
              "5873  VlrSuulqTFeQfV2PToJGvg                                    Fung Garden   \n",
              "6856  mFE9V6LPpsDRUQLEBsBRRA                                  Pearl of East   \n",
              "\n",
              "                    address          city state postal_code   latitude  \\\n",
              "3655         2005 Market St  Philadelphia    PA       19103  39.954108   \n",
              "4764             1 Canal St   New Orleans    LA       70130  29.950518   \n",
              "5142  37TH St And Spruce St  Philadelphia    PA       19104  39.951029   \n",
              "5873         5118 Gall Blvd   Zephyrhills    FL       33542  28.232059   \n",
              "6856      2049 W Oregon Ave  Philadelphia    PA       19145  39.918635   \n",
              "\n",
              "      longitude  stars  review_count  is_open  \\\n",
              "3655 -75.174671    3.0             8    False   \n",
              "4764 -90.063024    2.5             7     True   \n",
              "5142 -75.197275    3.5            21     True   \n",
              "5873 -82.180394    3.5             8    False   \n",
              "6856 -75.182422    3.0            17     True   \n",
              "\n",
              "                                             attributes   categories  \\\n",
              "3655  {'BusinessAcceptsCreditCards': 'True', 'Restau...  Restaurants   \n",
              "4764  {'BusinessAcceptsCreditCards': 'True', 'Busine...         Food   \n",
              "5142  {'RestaurantsPriceRange2': '1', 'BusinessAccep...         Food   \n",
              "5873  {'RestaurantsTakeOut': 'True', 'HasTV': 'False...  Restaurants   \n",
              "6856  {'RestaurantsGoodForGroups': 'False', 'Busines...  Restaurants   \n",
              "\n",
              "                                                  hours  \n",
              "3655                                               None  \n",
              "4764                                               None  \n",
              "5142                                               None  \n",
              "5873  {'Monday': '10:0-21:0', 'Tuesday': '10:0-21:0'...  \n",
              "6856  {'Monday': '10:30-22:30', 'Tuesday': '10:30-22...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f3e2774-e245-44fa-8caa-9379e999a8b7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>business_id</th>\n",
              "      <th>name</th>\n",
              "      <th>address</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>postal_code</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>stars</th>\n",
              "      <th>review_count</th>\n",
              "      <th>is_open</th>\n",
              "      <th>attributes</th>\n",
              "      <th>categories</th>\n",
              "      <th>hours</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3655</th>\n",
              "      <td>x2J-YIFeGZ-nsezzooVA9g</td>\n",
              "      <td>Twenty 21</td>\n",
              "      <td>2005 Market St</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>PA</td>\n",
              "      <td>19103</td>\n",
              "      <td>39.954108</td>\n",
              "      <td>-75.174671</td>\n",
              "      <td>3.0</td>\n",
              "      <td>8</td>\n",
              "      <td>False</td>\n",
              "      <td>{'BusinessAcceptsCreditCards': 'True', 'Restau...</td>\n",
              "      <td>Restaurants</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4764</th>\n",
              "      <td>mRWlzIJpyQgatq_J7dzxWw</td>\n",
              "      <td>Audubon Aquarium of the Americas - Food Court</td>\n",
              "      <td>1 Canal St</td>\n",
              "      <td>New Orleans</td>\n",
              "      <td>LA</td>\n",
              "      <td>70130</td>\n",
              "      <td>29.950518</td>\n",
              "      <td>-90.063024</td>\n",
              "      <td>2.5</td>\n",
              "      <td>7</td>\n",
              "      <td>True</td>\n",
              "      <td>{'BusinessAcceptsCreditCards': 'True', 'Busine...</td>\n",
              "      <td>Food</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5142</th>\n",
              "      <td>KuLi91b-N_0XY_3IA72RJQ</td>\n",
              "      <td>MexiPhilly Food Cart</td>\n",
              "      <td>37TH St And Spruce St</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>PA</td>\n",
              "      <td>19104</td>\n",
              "      <td>39.951029</td>\n",
              "      <td>-75.197275</td>\n",
              "      <td>3.5</td>\n",
              "      <td>21</td>\n",
              "      <td>True</td>\n",
              "      <td>{'RestaurantsPriceRange2': '1', 'BusinessAccep...</td>\n",
              "      <td>Food</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5873</th>\n",
              "      <td>VlrSuulqTFeQfV2PToJGvg</td>\n",
              "      <td>Fung Garden</td>\n",
              "      <td>5118 Gall Blvd</td>\n",
              "      <td>Zephyrhills</td>\n",
              "      <td>FL</td>\n",
              "      <td>33542</td>\n",
              "      <td>28.232059</td>\n",
              "      <td>-82.180394</td>\n",
              "      <td>3.5</td>\n",
              "      <td>8</td>\n",
              "      <td>False</td>\n",
              "      <td>{'RestaurantsTakeOut': 'True', 'HasTV': 'False...</td>\n",
              "      <td>Restaurants</td>\n",
              "      <td>{'Monday': '10:0-21:0', 'Tuesday': '10:0-21:0'...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6856</th>\n",
              "      <td>mFE9V6LPpsDRUQLEBsBRRA</td>\n",
              "      <td>Pearl of East</td>\n",
              "      <td>2049 W Oregon Ave</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>PA</td>\n",
              "      <td>19145</td>\n",
              "      <td>39.918635</td>\n",
              "      <td>-75.182422</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17</td>\n",
              "      <td>True</td>\n",
              "      <td>{'RestaurantsGoodForGroups': 'False', 'Busines...</td>\n",
              "      <td>Restaurants</td>\n",
              "      <td>{'Monday': '10:30-22:30', 'Tuesday': '10:30-22...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f3e2774-e245-44fa-8caa-9379e999a8b7')\"\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-4f3e2774-e245-44fa-8caa-9379e999a8b7 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-4f3e2774-e245-44fa-8caa-9379e999a8b7');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Plot review stars count"
      ],
      "metadata": {
        "id": "SZs1xj3WuXU1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "axis = sns.countplot(x='stars', data = food_businesses, palette='Paired')\n",
        "axis.set_ylabel('Count')\n",
        "axis.set_xlabel('Stars')\n",
        "axis.set_title('Restaurant-related businesses by rating')\n",
        "\n",
        "for p in axis.patches:\n",
        "        width, height = p.get_width(), p.get_height()\n",
        "        x, y = p.get_xy() \n",
        "        axis.text(x+width-.4, \n",
        "                y+height+.3,\n",
        "                '{:.0f}'.format(height),\n",
        "                weight='bold',\n",
        "                horizontalalignment='center')\n",
        "                \n",
        "plt.show()"
      ],
      "metadata": {
        "id": "fQl_s6hJu6D6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "9938bdef-c515-4484-8454-1bd2f5a26c19"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Filter to see how many of the restaurants are still open "
      ],
      "metadata": {
        "id": "1P8A8VlsTC7i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "open_businesses = food_businesses.where(food_businesses['is_open'] == True)\n",
        "\n",
        "len(open_businesses)"
      ],
      "metadata": {
        "id": "tR5S_yuUTCqi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2ff7c63-6369-40b1-9b1f-f0116d870be1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "126"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Breakdown by Top 10 States"
      ],
      "metadata": {
        "id": "vujtO3gtt1XJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top10 = open_businesses['state'].value_counts(ascending=True).tail(10).to_frame()"
      ],
      "metadata": {
        "id": "wfHViW5JuLQ2"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "axis = sns.barplot(x=top10.index,y='state', data=top10, palette='Blues')\n",
        "axis.set_ylabel('Count')\n",
        "axis.set_xlabel('States')\n",
        "axis.set_title('Number of Businesses by State (Top 10)')\n",
        "\n",
        "for patch in axis.patches:\n",
        "        width, height = patch.get_width(), patch.get_height()\n",
        "        x, y = patch.get_xy() \n",
        "        axis.text(x+width-.4, \n",
        "                y+height+0.3,\n",
        "                '{:.0f}'.format(height),\n",
        "                weight='bold',\n",
        "                horizontalalignment='center',\n",
        "                size='medium') \n",
        "                \n",
        "plt.show()"
      ],
      "metadata": {
        "id": "lmjSUxZfuaOh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        },
        "outputId": "1069257c-ed4d-4cd9-acde-f935fe5d6dd3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### There are more reviews for PA than other States"
      ],
      "metadata": {
        "id": "AdEqLM5sdNdB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **8. Perform Sentiment Analysis**"
      ],
      "metadata": {
        "id": "xd2_TwgGdVax"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Some assumptions to keep in mind\n",
        "\n",
        "1. Negative reviews will be anything from 0 to 3 stars\n",
        "2. Positive reviews will be anything from 4 to 6 stars\n",
        "3. We will work with the a subset of the dataset, a sample of 125k entries\n",
        "4. The expected result will be a dataframe with the review ('text' in the dataset) as well as the 'sentiment' boolean variable"
      ],
      "metadata": {
        "id": "ETpbG2UidbSb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import nltk\n",
        "import re, string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "nltk.download('punkt')\n",
        "nltk.download('omw-1.4')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('stopwords')\n",
        "\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "metadata": {
        "id": "CsCzQ5Gjhkci",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28b89387-bea7-4e33-b4dc-d891de7dbc7b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Re-map review stars as 0 (negative) or 1 (positive) as described in the assumptions above\n",
        "\n",
        "reviews_df['sentiment'] = reviews_df['stars'].replace({\n",
        "    1: 0, # negative\n",
        "    2: 0, # negative\n",
        "    3: 0, # negative\n",
        "    4: 1, # positive\n",
        "    5: 1  # positive\n",
        "}).astype(int)"
      ],
      "metadata": {
        "id": "ZVCipKRRdXzY"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sample 125k entries\n",
        "\n",
        "sample_reviews_df = reviews_df.sample(125000).reset_index(drop=True)"
      ],
      "metadata": {
        "id": "xtbseTvde1A8"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the two columns as described in the assumptions above\n",
        "\n",
        "analysis_df = sample_reviews_df[\n",
        "    ['text', 'sentiment']\n",
        "]\n",
        "\n",
        "analysis_df.head(5)"
      ],
      "metadata": {
        "id": "MGG8y2apfEkf",
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          "base_uri": "https://localhost:8080/",
          "height": 206
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        "outputId": "e6db7eff-b7e8-4164-b2c9-272feaf28657"
      },
      "execution_count": 16,
      "outputs": [
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            "text/plain": [
              "                                                text  sentiment\n",
              "0  The array of food choices looks so wonderful. ...          1\n",
              "1  Updating my review to add that my puppy is all...          1\n",
              "2  I've been using A1 pharmacy for almost 10 year...          1\n",
              "3  Love this place! Their curry and sushi are del...          1\n",
              "4  This place is awesome! The desserts are always...          1"
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      "cell_type": "code",
      "source": [
        "# Convert English contractions into separate words to standardize\n",
        "# For instance: \"wouldn't\" becomes \"would not\"\n",
        "# There is a comprehensive list of contractions accessible at https://www.sjsu.edu/writingcenter/docs/handouts/Contractions.pdf\n",
        "# and a python object can be found at https://stackoverflow.com/questions/19790188/expanding-english-language-contractions-in-python\n",
        "# For purposes of this project, we shall use a simple function to take care of the most common patterns\n",
        "\n",
        "def get_canonized_contractions(word):\n",
        "     word = re.sub(r\"couldn’t\", \"could not\", word)\n",
        "     word = re.sub(r\"wouldn’t\", \"would not\", word)\n",
        "     word = re.sub(r\"won’t\", \"will not\", word)\n",
        "     word = re.sub(r\"can\\’t\", \"can not\", word)\n",
        "     word = re.sub(r\"\\’d\",  \" would\", word)\n",
        "     word = re.sub(r\"\\’ve\", \" have\", word)\n",
        "     word = re.sub(r\"\\’ll\", \" will\", word)\n",
        "     word = re.sub(r\"\\’re\", \" are\", word)\n",
        "     word = re.sub(r\"n\\’t\", \" not\", word)\n",
        "     word = re.sub(r\"\\’t\", \" not\", word)\n",
        "     word = re.sub(r\"\\’m\", \" am\", word)\n",
        "     word = re.sub(r\"\\’s\", \" is\", word)\n",
        "     return word"
      ],
      "metadata": {
        "id": "eQ7rujJRjfNg"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Because we will use bag of words, we convert all strings to lower case\n",
        "analysis_df['processed'] = analysis_df['text'].apply(lambda x: ' '.join(x.lower() for x in str(x).split()))\n",
        "\n",
        "# Substitute each contraction in the already lowercase review strings\n",
        "analysis_df['processed'] = analysis_df['processed'].apply(lambda x: get_canonized_contractions(x))\n",
        "\n",
        "# Keep only alphabetical chars\n",
        "alpha = '[^A-Za-z]+'\n",
        "analysis_df['processed'] = analysis_df['processed'].apply(lambda x: ' '.join(\n",
        "    [re.sub(alpha, '', x) for x in nltk.word_tokenize(x)]\n",
        "))\n",
        "\n",
        "# Remove any extra spaces between words\n",
        "analysis_df['processed'] = analysis_df['processed'].apply(lambda x: re.sub(' +', ' ', x))\n",
        "\n",
        "# Remove stop words based on the ones downloaded from nltk\n",
        "stop_words = stopwords.words('english')\n",
        "analysis_df['processed'] = analysis_df['processed'].apply(lambda x: ' '.join(\n",
        "    [x for x in x.split() if x not in stop_words]\n",
        "))\n",
        "\n",
        "# As a final processing step, use a lemmatizer\n",
        "# This will transform words in the following way:\n",
        "# dogs -> dog\n",
        "# abaci -> abacus\n",
        "# churches -> church\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "analysis_df['processed'] = analysis_df['processed'].apply(lambda x: ' '.join(\n",
        "    [lemmatizer.lemmatize(word) for word in nltk.word_tokenize(x)]\n",
        "))"
      ],
      "metadata": {
        "id": "Tgu4apuIfwKX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3857e728-a873-49d5-ce7e-7162ac7cbec9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-18-edaad96e0c1e>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  analysis_df['processed'] = analysis_df['text'].apply(lambda x: ' '.join(x.lower() for x in str(x).split()))\n",
            "<ipython-input-18-edaad96e0c1e>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  analysis_df['processed'] = analysis_df['processed'].apply(lambda x: get_canonized_contractions(x))\n",
            "<ipython-input-18-edaad96e0c1e>:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  analysis_df['processed'] = analysis_df['processed'].apply(lambda x: ' '.join(\n",
            "<ipython-input-18-edaad96e0c1e>:14: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  analysis_df['processed'] = analysis_df['processed'].apply(lambda x: re.sub(' +', ' ', x))\n",
            "<ipython-input-18-edaad96e0c1e>:18: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  analysis_df['processed'] = analysis_df['processed'].apply(lambda x: ' '.join(\n",
            "<ipython-input-18-edaad96e0c1e>:28: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  analysis_df['processed'] = analysis_df['processed'].apply(lambda x: ' '.join(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate into A and B for an A/B test\n",
        "\n",
        "A = analysis_df['processed']\n",
        "B = analysis_df['sentiment']\n",
        "\n",
        "# Use sklern to split arrays into random train and test subsets\n",
        "# test_size = the proportion of the dataset to include in the test split\n",
        "# random_state = controls the shuffling applied to the data before applying the split\n",
        "A_train, A_test, B_train, B_test = train_test_split(A, B, test_size=0.2, stratify=B, random_state=45)\n",
        "\n",
        "print('A - Training step shape', A_train.shape)\n",
        "print('A - Test step shape', A_test.shape)\n",
        "\n",
        "print('B - Training step shape', B_train.shape)\n",
        "print('B - Test step shape', B_test.shape)"
      ],
      "metadata": {
        "id": "a3zso6YqjbSh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf046945-2191-4bf7-b1da-e6f9ed30eede"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A - Training step shape (100000,)\n",
            "A - Test step shape (25000,)\n",
            "B - Training step shape (100000,)\n",
            "B - Test step shape (25000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert a collection of raw documents to a matrix of TF-IDF features\n",
        "# that can be used as input to an estimator\n",
        "\n",
        "vectorizer = TfidfVectorizer()\n",
        "tf_A_train = vectorizer.fit_transform(A_train)\n",
        "tf_A_test = vectorizer.transform(A_test)"
      ],
      "metadata": {
        "id": "GOCtzMqEm1PB"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build a report\n",
        "\n",
        "classification = LinearSVC(random_state = 0)\n",
        "\n",
        "# Fit the model according to the given training data\n",
        "classification.fit(tf_A_train, B_train)\n",
        "\n",
        "B_test_prediction = classification.predict(tf_A_test)\n",
        "\n",
        "report = classification_report(B_test, B_test_prediction, output_dict = True)\n",
        "\n",
        "pd.DataFrame(report)"
      ],
      "metadata": {
        "id": "Yaep6inmn34C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "7eebd256-2e05-49a9-be23-c0cde5496e62"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     0             1  accuracy     macro avg  weighted avg\n",
              "precision     0.902303      0.935877   0.92532      0.919090      0.924875\n",
              "recall        0.865845      0.954307   0.92532      0.910076      0.925320\n",
              "f1-score      0.883698      0.945003   0.92532      0.914350      0.924914\n",
              "support    8192.000000  16808.000000   0.92532  25000.000000  25000.000000"
            ],
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              "      <th>weighted avg</th>\n",
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              "      <th>precision</th>\n",
              "      <td>0.902303</td>\n",
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              "      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-68e0ad63-0982-4a87-a8fc-7f1173508b23 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-68e0ad63-0982-4a87-a8fc-7f1173508b23');\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": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **8.1 Let's run some tests and evaluations**"
      ],
      "metadata": {
        "id": "ocHGdpLxpAUW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Positive Review"
      ],
      "metadata": {
        "id": "iWZOTQJ1qj0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Positive review from the original dataset\n",
        "\n",
        "analysis_df['text'][1]"
      ],
      "metadata": {
        "id": "8EPbAXRvo_nv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "109b4f64-bf58-4264-ebca-0485f9085e6a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Updating my review to add that my puppy is allowed in the store and always gets a treat and a pat on the head. It's his favorite place to shop next to Doggie Style - thanks Barbour!\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The same positive review, from the processed column\n",
        "\n",
        "analysis_df['processed'][1]"
      ],
      "metadata": {
        "id": "ukEf5pjwpQ34",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b0f2d387-c099-419f-b8d5-c290f94a9f94"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'updating review add puppy allowed store always get treat pat head favorite place shop next doggie style thanks barbour'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Notice the stop words are gone and the main words stay\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "78bHL74tpeZD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "positive_review = analysis_df['processed'][1]\n",
        "\n",
        "positive_review_transformed = vectorizer.transform([positive_review])\n",
        "\n",
        "positive_review_prediction = classification.predict(positive_review_transformed)\n",
        "\n",
        "positive_review_prediction"
      ],
      "metadata": {
        "id": "7_E7q1htpnAK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f9ba487-d1ab-4368-862c-006ca295046d"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The value '1' means we predicted positive, which is correct**"
      ],
      "metadata": {
        "id": "FfZoYVpTqeKF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Custom Positive Review"
      ],
      "metadata": {
        "id": "qna-qL_VrXYj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Very positive\n",
        "\n",
        "custom_pos_review = '''\n",
        "Excellent soondubu jjigae I had many coworkers from Korea recommend this restaurant, so that is how you know it is legit. I had pretty high expectations. \n",
        "I decided to try out their not spicy option. This is my first time ever trying soft tofu soup without any gochujang. It is definitely on the more salty and savory side.\n",
        "I got the seafood one (Haemul) and it came with squid, mini mussels, tiny shrimp, and one prawn. Not a ton of seafood.\n",
        "The soup was definitely dominated by the silken tofu, but that is how I prefer it. Of the side dishes, the pickled radish was my favorite.\n",
        "Normally I see this served with fried chicken, so I was pretty delighted to see it and it was cut into strips. The kimchi was tasty, but more spicy than your typical kimchi.\n",
        "'''\n",
        "\n",
        "custom_pos_review_transformed = vectorizer.transform([custom_pos_review])\n",
        "\n",
        "custom_pos_review_prediction = classification.predict(custom_pos_review_transformed)\n",
        "\n",
        "custom_pos_review_prediction"
      ],
      "metadata": {
        "id": "uyHeyMkDsAGM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e28b1e4-66d7-414d-9f53-ef12c63db727"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The value '1' means we predicted positive, which is correct**"
      ],
      "metadata": {
        "id": "Al9OJ8EGs1Mn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dubiously positive (4 stars or more)\n",
        "\n",
        "custom_pos_review = '''\n",
        "Fish and chips for $10.50 in the bay area. I can't complain.\n",
        "Just across the street there is a disgusting seafood restaurant.\n",
        "They don't seem to even wash their hands, and seafood is not fresh.\n",
        "But this place lives up to the expectations. Good food, gotta love it\n",
        "'''\n",
        "\n",
        "custom_pos_review_transformed = vectorizer.transform([custom_pos_review])\n",
        "\n",
        "custom_pos_review_prediction = classification.predict(custom_pos_review_transformed)\n",
        "\n",
        "custom_pos_review_prediction"
      ],
      "metadata": {
        "id": "GnXJJ2gvty2u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fee94fd-7ddb-4a98-d141-566f61d0bb8f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The value '1' means we predicted positive, which is correct**"
      ],
      "metadata": {
        "id": "cIxQn03rvTBo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Negative Review"
      ],
      "metadata": {
        "id": "ZrGBinPTqmgo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Negative review from the original dataset\n",
        "\n",
        "analysis_df['text'][6]"
      ],
      "metadata": {
        "id": "UAdlLR31qoYL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "1a6ca32c-2637-4b7f-beec-3f903711e578"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The service was really well meaning, but awkward. Our table was the worst in the whole restaurant, even though there were many empty tables around us. The silverware was not cleaned properly, and had hair in it as well. The one redeeming quality was that the food was decent, but it was not spectacular.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The same negative review, from the processed column\n",
        "\n",
        "analysis_df['processed'][6]"
      ],
      "metadata": {
        "id": "8vmYVW1-q1Xx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1f21655e-ffdb-4eb2-9007-1bb6550924ab"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'service really well meaning awkward table worst whole restaurant even though many empty table around u silverware cleaned properly hair well one redeeming quality food decent spectacular'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "negative_review = analysis_df['processed'][1]\n",
        "\n",
        "negative_review_transformed = vectorizer.transform([negative_review])\n",
        "\n",
        "negative_review_prediction = classification.predict(negative_review_transformed)\n",
        "\n",
        "negative_review_prediction"
      ],
      "metadata": {
        "id": "64bqF7NXq_0z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28f3e8ef-abb8-4ba5-96e0-1a3baf9c8014"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The value '1' means we predicted positive, which is incorrect**\n",
        "\n"
      ],
      "metadata": {
        "id": "zW831uXQrO8L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Custom Negative Review"
      ],
      "metadata": {
        "id": "E9KF1vI3rbGk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Very negative\n",
        "\n",
        "custom_negative_review = '''\n",
        "I was highly disappointed on my last visit there last night.\n",
        "May have a talk with the manager. Use to love this place.\n",
        "Now thinking I don't need to go here again. They were very busy.\n",
        "Very disorganized and need stronger management leadership.\n",
        "Food was overcooked and Very Small portions.\n",
        "Added roasted asparagus for $3 and only got 3 stocks!\n",
        "Just overall a bad experience.\n",
        "'''\n",
        "\n",
        "custom_negative_review_transformed = vectorizer.transform([custom_negative_review])\n",
        "\n",
        "custom_negative_review_prediction = classification.predict(custom_negative_review_transformed)\n",
        "\n",
        "custom_negative_review_prediction"
      ],
      "metadata": {
        "id": "vP1qsmvGs7gA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfa018dd-932c-4e3b-b423-0f0ab1c73777"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The value '0' means we predicted negative, which is correct**\n"
      ],
      "metadata": {
        "id": "Iptqjfpjtm13"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dubiously negative (less than 3 stars)\n",
        "\n",
        "custom_negative_review = '''\n",
        "Updated my reviews. Food is still good. But they keep missing side orders.\n",
        "At least twice this happened to me. It's annoying but I keep coming anyway\n",
        "'''\n",
        "\n",
        "custom_negative_review_transformed = vectorizer.transform([custom_negative_review])\n",
        "\n",
        "custom_negative_review_prediction = classification.predict(custom_negative_review_transformed)\n",
        "\n",
        "custom_negative_review_prediction"
      ],
      "metadata": {
        "id": "B9HOumQ4tQHc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1b9d74c2-2dca-4aa1-c90e-a4007726c16c"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The value '0' means we predicted negative, which is correct**\n"
      ],
      "metadata": {
        "id": "-p7kYyj0uKiY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Custom Neutral Review"
      ],
      "metadata": {
        "id": "vdVzn41QwHDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Neutral\n",
        "\n",
        "custom_neutral_review = '''\n",
        "This place is alright. Price is okay and food is normal. Not bad, not great. Service is pretty good though.\n",
        "'''\n",
        "\n",
        "custom_neutral_review_transformed = vectorizer.transform([custom_neutral_review])\n",
        "\n",
        "custom_neutral_review_prediction = classification.predict(custom_neutral_review_transformed)\n",
        "\n",
        "custom_neutral_review_prediction"
      ],
      "metadata": {
        "id": "rZCcDdJdwJCP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e82e96b-6d8b-4481-de51-0f15cef19036"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Although it was neutral leaning towards positive, the model predicted '0' negative"
      ],
      "metadata": {
        "id": "9K5FBPS1wd9B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "aa7uXnDQvrLc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(report)"
      ],
      "metadata": {
        "id": "w4faxqMev4e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "07972074-20c5-4ee7-e6b7-c289e8f9c601"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     0             1  accuracy     macro avg  weighted avg\n",
              "precision     0.902303      0.935877   0.92532      0.919090      0.924875\n",
              "recall        0.865845      0.954307   0.92532      0.910076      0.925320\n",
              "f1-score      0.883698      0.945003   0.92532      0.914350      0.924914\n",
              "support    8192.000000  16808.000000   0.92532  25000.000000  25000.000000"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
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              "      <th>weighted avg</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>precision</th>\n",
              "      <td>0.902303</td>\n",
              "      <td>0.935877</td>\n",
              "      <td>0.92532</td>\n",
              "      <td>0.919090</td>\n",
              "      <td>0.924875</td>\n",
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              "    <tr>\n",
              "      <th>recall</th>\n",
              "      <td>0.865845</td>\n",
              "      <td>0.954307</td>\n",
              "      <td>0.92532</td>\n",
              "      <td>0.910076</td>\n",
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              "      <th>f1-score</th>\n",
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              "      <td>0.914350</td>\n",
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              "      <th>support</th>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80ba34d4-790a-4468-8a6d-851e7777f8eb 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-80ba34d4-790a-4468-8a6d-851e7777f8eb');\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": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.  This model has accuracy of ~92%\n",
        "\n",
        "2.  Negative reviews were more difficult to predict\n",
        "\n",
        "    Positive - 93% accuracy\n",
        "\n",
        "    Negative - 89% accuracy\n",
        "\n",
        "3. This model accurately predicted 2 custom positive reviews, and 1 negative review, and tends to lean towards negative given a neutral review"
      ],
      "metadata": {
        "id": "f0dxFYqxv8uk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9. Environment take down\n"
      ],
      "metadata": {
        "id": "AQmUIfF0wJjB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Uncomment and run the code below to remove downloaded files from the environment\n",
        "\n",
        "# !rm yelp_businesses.json\n",
        "# !rm yelp_reviews.json"
      ],
      "metadata": {
        "id": "yXwahrSLwP8k"
      },
      "execution_count": 34,
      "outputs": []
    }
  ]
}