Finding_N_Gram_from_the_given_sentences.ipynb

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    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YebuAE06C0xt"
      },
      "source": [
        "Finding N-Gram from the given sentences"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vh7VrMtoD0aE"
      },
      "source": [
        "BoW shortcomings\n",
        "Examples:\n",
        "*   The movie was good and not boring -> positive\n",
        "*   The movie was not good and boring -> negative\n",
        "*  Exactly the same BoW representation!\n",
        "*  Context of the words is lost.\n",
        "*  Sentiment dependent on the position of not\n",
        "\n",
        "#n-grams\n",
        "##     *  Contiguous sequence of n elements (or words) in a given document.\n",
        "#       *  Bi-grams / Tri-grams\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jO52EY5yGZwW"
      },
      "source": [
        "n-gram models for movie tag lines\n",
        "In this exercise, we have been provided with a corpus of more than 9000 movie tag lines. Our job is to generate n-gram models up to n equal to 1, n equal to 2 and n equal to 3 for this data and discover the number of features for each model.\n",
        "\n",
        "We will then compare the number of features generated for each model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eh8hdrM9Gq-L"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import spacy\n",
        "\n",
        "plt.rcParams['figure.figsize'] = (8, 8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "_AHHD4CUJ2jo",
        "outputId": "a9a716cb-0127-47d5-f578-39ddd7e34dd0"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8ca8fcb1-7c3a-4aaf-8f07-5ca2c32d2ac7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8ca8fcb1-7c3a-4aaf-8f07-5ca2c32d2ac7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving movie_overviews.csv to movie_overviews.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vgOvuMvKGdH"
      },
      "source": [
        "import io\n",
        "movies= pd.read_csv(io.BytesIO(uploaded['movie_overviews.csv']))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies = movies.fillna(method=\"ffill\")\n",
        "movies.head()"
      ],
      "metadata": {
        "id": "NeHLNrpDgITU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b761d567-e8d9-467d-bb33-efc3899dc295"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                        title  \\\n",
              "0   8844                      Jumanji   \n",
              "1  15602             Grumpier Old Men   \n",
              "2  31357            Waiting to Exhale   \n",
              "3  11862  Father of the Bride Part II   \n",
              "4    949                         Heat   \n",
              "\n",
              "                                            overview  \\\n",
              "0  When siblings Judy and Peter discover an encha...   \n",
              "1  A family wedding reignites the ancient feud be...   \n",
              "2  Cheated on, mistreated and stepped on, the wom...   \n",
              "3  Just when George Banks has recovered from his ...   \n",
              "4  Obsessive master thief, Neil McCauley leads a ...   \n",
              "\n",
              "                                             tagline  \n",
              "0          Roll the dice and unleash the excitement!  \n",
              "1  Still Yelling. Still Fighting. Still Ready for...  \n",
              "2  Friends are the people who let you be yourself...  \n",
              "3  Just When His World Is Back To Normal... He's ...  \n",
              "4                           A Los Angeles Crime Saga  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>overview</th>\n",
              "      <th>tagline</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8844</td>\n",
              "      <td>Jumanji</td>\n",
              "      <td>When siblings Judy and Peter discover an encha...</td>\n",
              "      <td>Roll the dice and unleash the excitement!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15602</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31357</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>Friends are the people who let you be yourself...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11862</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>949</td>\n",
              "      <td>Heat</td>\n",
              "      <td>Obsessive master thief, Neil McCauley leads a ...</td>\n",
              "      <td>A Los Angeles Crime Saga</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a45764d2-f092-4d03-886c-337f6f9fc1d5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "          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",
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              "        }\n",
              "      </script>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LycBB3eSG3n5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "6ed06228-d621-424d-db0b-f6bbb73bdb4a"
      },
      "source": [
        "import io\n",
        "#movies= pd.read_csv(io.BytesIO(uploaded['movie_reviwe.csv']))\n",
        "\n",
        "#movies = pd.read_csv(uploaded['movie_overviews.csv'])\n",
        "movies['tagline'] = movies['tagline'].str.lower()\n",
        "movies.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id                        title  \\\n",
              "0   8844                      Jumanji   \n",
              "1  15602             Grumpier Old Men   \n",
              "2  31357            Waiting to Exhale   \n",
              "3  11862  Father of the Bride Part II   \n",
              "4    949                         Heat   \n",
              "\n",
              "                                            overview  \\\n",
              "0  When siblings Judy and Peter discover an encha...   \n",
              "1  A family wedding reignites the ancient feud be...   \n",
              "2  Cheated on, mistreated and stepped on, the wom...   \n",
              "3  Just when George Banks has recovered from his ...   \n",
              "4  Obsessive master thief, Neil McCauley leads a ...   \n",
              "\n",
              "                                             tagline  \n",
              "0          roll the dice and unleash the excitement!  \n",
              "1  still yelling. still fighting. still ready for...  \n",
              "2  friends are the people who let you be yourself...  \n",
              "3  just when his world is back to normal... he's ...  \n",
              "4                           a los angeles crime saga  "
            ],
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              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
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              "      <td>31357</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>friends are the people who let you be yourself...</td>\n",
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              "      <td>11862</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>just when his world is back to normal... he's ...</td>\n",
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              "      <td>Obsessive master thief, Neil McCauley leads a ...</td>\n",
              "      <td>a los angeles crime saga</td>\n",
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              "          const element = document.querySelector('#df-15990c52-d19d-4fc2-afbb-e0cbce5b600b');\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rJtQmFFCG9f3"
      },
      "source": [
        "corpus = movies['tagline']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJpYl_FqHChM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee1295d6-77c5-43d3-eb66-76eecdf19cff"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# Create CountVectorizer object\n",
        "vectorizer = CountVectorizer()\n",
        "\n",
        "# Generate matrix of word vectors\n",
        "bow_matrix = vectorizer.fit_transform(corpus)\n",
        "\n",
        "# Print the shape of bow_matrix\n",
        "print(bow_matrix.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(9098, 6613)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xngAskEIHYC-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "448cf4e6-205d-4894-b788-b02a8472a30b"
      },
      "source": [
        "nlp = spacy.load('en_core_web_sm')\n",
        "stopwords = spacy.lang.en.stop_words.STOP_WORDS\n",
        "\n",
        "\n",
        "lem_corpus = corpus.apply(lambda row: ' '.join([t.lemma_ for t in nlp(row) \n",
        "                                                if t.lemma_ not in stopwords \n",
        "                                                and t.lemma_.isalpha()]))\n",
        "\n",
        "lem_corpus"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                            roll dice unleash excitement\n",
              "1                                   yell fight ready love\n",
              "2                            friend people let let forget\n",
              "3                              world normal surprise life\n",
              "4                                  los angeles crime saga\n",
              "                              ...                        \n",
              "9093                       happen vegas stay vegas happen\n",
              "9094    decorate officer devoted family man defend hon...\n",
              "9095    decorate officer devoted family man defend hon...\n",
              "9096                              god incarnate city doom\n",
              "9097                                      band know story\n",
              "Name: tagline, Length: 9098, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJ7RJeB4HnUG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c532b59b-a6d8-4e74-9464-3ba4a920b862"
      },
      "source": [
        "# Create CountVectorizer object\n",
        "vectorizer = CountVectorizer()\n",
        "\n",
        "# Generate of word vectors\n",
        "bow_lem_matrix = vectorizer.fit_transform(lem_corpus)\n",
        "\n",
        "# Print the shape of how_lem_matrix\n",
        "print(bow_lem_matrix.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(9098, 4960)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4RPPse3HoCa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "7271b39b-7c02-4912-819a-15b6ee424b7f"
      },
      "source": [
        "sentences = ['The lion is the king of the jungle',\n",
        "             'Lions have lifespans of a decade', \n",
        "             'The lion is an endangered species']\n",
        "\n",
        "\n",
        "# Create CountVectorizer object\n",
        "vectorizer = CountVectorizer()\n",
        "\n",
        "# Generate matrix of word vectors\n",
        "bow_matrix = vectorizer.fit_transform(sentences)\n",
        "\n",
        "# Convert bow_matrix into a DataFrame\n",
        "bow_df = pd.DataFrame(bow_matrix.toarray())\n",
        "\n",
        "# Map the column names to vocabulary\n",
        "bow_df.columns = vectorizer.get_feature_names()\n",
        "\n",
        "# Print bow_df\n",
        "bow_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   an  decade  endangered  have  is  jungle  king  lifespans  lion  lions  of  \\\n",
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              "       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-a5e623ea-513c-479b-a824-3cc1827c0a78 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-a5e623ea-513c-479b-a824-3cc1827c0a78');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "onmJ771PV9pR",
        "outputId": "d23c57f1-e7c5-4142-83e3-e99a8650dcef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-96ee2e9f-22b4-4813-b45a-8f96d1f5c4d9\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-96ee2e9f-22b4-4813-b45a-8f96d1f5c4d9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving movie_reviews_clean.csv to movie_reviews_clean.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "movie_reviews_clean = pd.read_csv(io.BytesIO(uploaded['movie_reviews_clean.csv']))"
      ],
      "metadata": {
        "id": "O4MVymPYXLSH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4TWio0zHx6i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d33ae97e-07b3-4d31-e18e-45fb81c3f5dd"
      },
      "source": [
        "#movie_reviews = pd.read_csv('./dataset/movie_reviews_clean.csv')\n",
        "#movie_reviews.head()\n",
        "movie_reviews_clean.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              review  sentiment\n",
              "0  this anime series starts out great interesting...          0\n",
              "1  some may go for a film like this but i most as...          0\n",
              "2  i ve seen this piece of perfection during the ...          1\n",
              "3  this movie is likely the worst movie i ve ever...          0\n",
              "4  it ll soon be 10 yrs since this movie was rele...          1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ce58431-b8dc-4ca8-b5be-7f5c280afe09\">\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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>this anime series starts out great interesting...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>some may go for a film like this but i most as...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>i ve seen this piece of perfection during the ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>this movie is likely the worst movie i ve ever...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>it ll soon be 10 yrs since this movie was rele...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ce58431-b8dc-4ca8-b5be-7f5c280afe09')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3JSx2huH5K9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23a9fb31-cd5b-4eca-e61e-5d0f27746bcc"
      },
      "source": [
        "X = movie_reviews_clean['review']\n",
        "y = movie_reviews_clean['sentiment']\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n",
        "\n",
        "# Create a CounterVectorizer object\n",
        "vectorizer = CountVectorizer(lowercase=True, stop_words='english')\n",
        "\n",
        "# fit and transform X_train\n",
        "X_train_bow = vectorizer.fit_transform(X_train)\n",
        "\n",
        "# Transform X_test\n",
        "X_test_bow = vectorizer.transform(X_test)\n",
        "\n",
        "# Print shape of X_train_bow and X_test_bow\n",
        "print(X_train_bow.shape)\n",
        "print(X_test_bow.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(750, 14801)\n",
            "(250, 14801)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ieI0XXjxIDzV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c9be7b8-beb9-4ff2-bf0e-a4457c2c35df"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "# Create a MultinomialNB object\n",
        "clf = MultinomialNB()\n",
        "\n",
        "# Fit the classifier\n",
        "clf.fit(X_train_bow, y_train)\n",
        "\n",
        "# Measure the accuracy\n",
        "accuracy = clf.score(X_test_bow, y_test)\n",
        "print(\"The accuracy of the classifier on the test set is %.3f\" % accuracy)\n",
        "\n",
        "# Predict the sentiment of a negative review\n",
        "review = 'The movie was terrible. The music was underwhelming and the acting mediocre.'\n",
        "prediction = clf.predict(vectorizer.transform([review]))[0]\n",
        "print(\"The sentiment predicted by the classifier is %i\" % (prediction))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the classifier on the test set is 0.764\n",
            "The sentiment predicted by the classifier is 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myT7uvfpGcNy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "381cf9fe-4412-4f74-8539-043fa0ed62f6"
      },
      "source": [
        "# Generate n-grams upto n=1\n",
        "vectorizer_ng1 = CountVectorizer(ngram_range=(1, 1))\n",
        "ng1 = vectorizer_ng1.fit_transform(corpus)\n",
        "\n",
        "# Generate n-grams upto n=2\n",
        "vectorizer_ng2 = CountVectorizer(ngram_range=(1, 2))\n",
        "ng2 = vectorizer_ng2.fit_transform(corpus)\n",
        "\n",
        "# Generate n-grams upto n=3\n",
        "vectorizer_ng3 = CountVectorizer(ngram_range=(1, 3))\n",
        "ng3 = vectorizer_ng3.fit_transform(corpus)\n",
        "\n",
        "# Print the number of features for each model\n",
        "print(\"ng1, ng2 and ng3 have %i, %i and %i features respectively\" % \n",
        "      (ng1.shape[1], ng2.shape[1], ng3.shape[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ng1, ng2 and ng3 have 6613, 37098 and 76878 features respectively\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tierr5TVeK7y"
      },
      "source": [
        "ng_vectorizer = CountVectorizer(ngram_range=(1, 2))\n",
        "X_train_ng = ng_vectorizer.fit_transform(X_train)\n",
        "X_test_ng = ng_vectorizer.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmpvLNKXeO6q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c306d496-297a-4322-96a1-1d053bb321e8"
      },
      "source": [
        "# Define an instance of MultinomialNB\n",
        "clf_ng = MultinomialNB()\n",
        "\n",
        "# Fit the classifier\n",
        "clf_ng.fit(X_train_ng, y_train)\n",
        "\n",
        "# Measure the accuracy\n",
        "accuracy = clf_ng.score(X_test_ng, y_test)\n",
        "print(\"The accuracy of the classifier on the test set is %.3f\" % accuracy)\n",
        "\n",
        "# Predict the sentiment of a negative review\n",
        "review = 'The movie was not good. The plot had several holes and the acting lacked panache'\n",
        "prediction = clf_ng.predict(ng_vectorizer.transform([review]))[0]\n",
        "print(\"The sentiment predicted by the classifier is %i\" % (prediction))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the classifier on the test set is 0.772\n",
            "The sentiment predicted by the classifier is 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FTjNgxqgeXeu"
      },
      "source": [
        "Comparing performance of n-gram models\n",
        "You now know how to conduct sentiment analysis by converting text into various n-gram representations and feeding them to a classifier. In this exercise, we will conduct sentiment analysis for the same movie reviews from before using two n-gram models: unigrams and n-grams upto n equal to 3.\n",
        "\n",
        "We will then compare the performance using three criteria: accuracy of the model on the test set, time taken to execute the program and the number of features created when generating the n-gram representation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gC_afCcBeaAu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a747145-58e1-4c9d-b44a-0060f5505d50"
      },
      "source": [
        "import time\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "# Splitting the data into training and test sets\n",
        "train_X, test_X, train_y, test_y = train_test_split(movie_reviews_clean['review'],\n",
        "                                                    movie_reviews_clean['sentiment'], \n",
        "                                                    test_size=0.5, \n",
        "                                                    random_state=42,\n",
        "                                                    stratify=movie_reviews_clean['sentiment'])\n",
        "\n",
        "# Generateing ngrams\n",
        "vectorizer = CountVectorizer(ngram_range=(1,1))\n",
        "train_X = vectorizer.fit_transform(train_X)\n",
        "test_X = vectorizer.transform(test_X)\n",
        "\n",
        "# Fit classifier\n",
        "clf = MultinomialNB()\n",
        "clf.fit(train_X, train_y)\n",
        "\n",
        "# Print the accuracy, time and number of dimensions\n",
        "print(\"The program took %.3f seconds to complete. The accuracy on the test set is %.2f. \" %\n",
        "      (time.time() - start_time, clf.score(test_X, test_y)))\n",
        "print(\"The ngram representation had %i features.\" % (train_X.shape[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The program took 0.154 seconds to complete. The accuracy on the test set is 0.75. \n",
            "The ngram representation had 12347 features.\n"
          ]
        }
      ]
    }
  ]
}