Text_Classification_NLP.ipynb

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      "execution_count": null,
      "metadata": {
        "id": "B1RX2FVMgjjb"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sEI-qTd9gvCs",
        "outputId": "2d600e84-cb67-4e73-a1cc-816c3a3cdc1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/datasets_files/IMDB_Dataset.csv')"
      ],
      "metadata": {
        "id": "KXiozpySg0Yx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.iloc[:10000]"
      ],
      "metadata": {
        "id": "Zrot_M5Fg9fT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 206
        },
        "id": "UD77FfSghC_5",
        "outputId": "bc038046-dbc6-4a15-8719-6ec0b2787c03"
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              "                                              review sentiment\n",
              "0  One of the other reviewers has mentioned that ...  positive\n",
              "1  A wonderful little production. <br /><br />The...  positive\n",
              "2  I thought this was a wonderful way to spend ti...  positive\n",
              "3  Basically there's a family where a little boy ...  negative\n",
              "4  Petter Mattei's \"Love in the Time of Money\" is...  positive"
            ],
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              "      <th>1</th>\n",
              "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
              "      <td>positive</td>\n",
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              "      <td>I thought this was a wonderful way to spend ti...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Basically there's a family where a little boy ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"review\"]=df[\"review\"].str.lower()"
      ],
      "metadata": {
        "id": "K7QKRTxLhELI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"review\"][0]"
      ],
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        "id": "erWHLTxbhMgS",
        "outputId": "4655d157-1969-4c7a-b412-a3e43cbb4109"
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          "data": {
            "text/plain": [
              "\"one of the other reviewers has mentioned that after watching just 1 oz episode you'll be hooked. they are right, as this is exactly what happened with me.<br /><br />the first thing that struck me about oz was its brutality and unflinching scenes of violence, which set in right from the word go. trust me, this is not a show for the faint hearted or timid. this show pulls no punches with regards to drugs, sex or violence. its is hardcore, in the classic use of the word.<br /><br />it is called oz as that is the nickname given to the oswald maximum security state penitentary. it focuses mainly on emerald city, an experimental section of the prison where all the cells have glass fronts and face inwards, so privacy is not high on the agenda. em city is home to many..aryans, muslims, gangstas, latinos, christians, italians, irish and more....so scuffles, death stares, dodgy dealings and shady agreements are never far away.<br /><br />i would say the main appeal of the show is due to the fact that it goes where other shows wouldn't dare. forget pretty pictures painted for mainstream audiences, forget charm, forget romance...oz doesn't mess around. the first episode i ever saw struck me as so nasty it was surreal, i couldn't say i was ready for it, but as i watched more, i developed a taste for oz, and got accustomed to the high levels of graphic violence. not just violence, but injustice (crooked guards who'll be sold out for a nickel, inmates who'll kill on order and get away with it, well mannered, middle class inmates being turned into prison bitches due to their lack of street skills or prison experience) watching oz, you may become comfortable with what is uncomfortable viewing....thats if you can get in touch with your darker side.\""
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          "metadata": {},
          "execution_count": 7
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      "cell_type": "code",
      "source": [
        "df[\"sentiment\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DfQnlu9ZhUc1",
        "outputId": "f9c9e84c-5d1e-497c-f76b-5e1e2672f641"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "positive    5028\n",
              "negative    4972\n",
              "Name: sentiment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUBganqxhafo",
        "outputId": "fc4ec3df-2ca0-4864-ad76-32ad25de384e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "review       0\n",
              "sentiment    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to check duplicate reviews\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iPwGp5Huhfiv",
        "outputId": "09ebdf7a-8a29-47a0-98f7-8731bda19bbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "eJRy84GHhwDB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjdBIKF8h415",
        "outputId": "83707a9f-08e2-4605-a0c3-190873361208"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Basic preprocessing\n",
        "# remove tags\n",
        "# lowercase\n",
        "# remove stopwords\n"
      ],
      "metadata": {
        "id": "OwGsKHFSh9uj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# using regex\n",
        "import re\n",
        "def remove_html_tags(text):\n",
        "  pattern = re.compile(\"<.*?>\")\n",
        "  return pattern.sub(r'',text)"
      ],
      "metadata": {
        "id": "Ya4R5KdbiNcw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"review\"]=df[\"review\"].apply(remove_html_tags)"
      ],
      "metadata": {
        "id": "C8f6Nr_riQQF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords"
      ],
      "metadata": {
        "id": "Xguaem73iS0A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nltk.download(\"stopwords\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6ZxDRLTicmX",
        "outputId": "297aceb4-af0a-4c18-efe1-4c16e4fdde17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stopwords.words(\"english\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvtbyIP4ihGj",
        "outputId": "95ee06e5-ea67-4198-ff2a-d283230e94fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i',\n",
              " 'me',\n",
              " 'my',\n",
              " 'myself',\n",
              " 'we',\n",
              " 'our',\n",
              " 'ours',\n",
              " 'ourselves',\n",
              " 'you',\n",
              " \"you're\",\n",
              " \"you've\",\n",
              " \"you'll\",\n",
              " \"you'd\",\n",
              " 'your',\n",
              " 'yours',\n",
              " 'yourself',\n",
              " 'yourselves',\n",
              " 'he',\n",
              " 'him',\n",
              " 'his',\n",
              " 'himself',\n",
              " 'she',\n",
              " \"she's\",\n",
              " 'her',\n",
              " 'hers',\n",
              " 'herself',\n",
              " 'it',\n",
              " \"it's\",\n",
              " 'its',\n",
              " 'itself',\n",
              " 'they',\n",
              " 'them',\n",
              " 'their',\n",
              " 'theirs',\n",
              " 'themselves',\n",
              " 'what',\n",
              " 'which',\n",
              " 'who',\n",
              " 'whom',\n",
              " 'this',\n",
              " 'that',\n",
              " \"that'll\",\n",
              " 'these',\n",
              " 'those',\n",
              " 'am',\n",
              " 'is',\n",
              " 'are',\n",
              " 'was',\n",
              " 'were',\n",
              " 'be',\n",
              " 'been',\n",
              " 'being',\n",
              " 'have',\n",
              " 'has',\n",
              " 'had',\n",
              " 'having',\n",
              " 'do',\n",
              " 'does',\n",
              " 'did',\n",
              " 'doing',\n",
              " 'a',\n",
              " 'an',\n",
              " 'the',\n",
              " 'and',\n",
              " 'but',\n",
              " 'if',\n",
              " 'or',\n",
              " 'because',\n",
              " 'as',\n",
              " 'until',\n",
              " 'while',\n",
              " 'of',\n",
              " 'at',\n",
              " 'by',\n",
              " 'for',\n",
              " 'with',\n",
              " 'about',\n",
              " 'against',\n",
              " 'between',\n",
              " 'into',\n",
              " 'through',\n",
              " 'during',\n",
              " 'before',\n",
              " 'after',\n",
              " 'above',\n",
              " 'below',\n",
              " 'to',\n",
              " 'from',\n",
              " 'up',\n",
              " 'down',\n",
              " 'in',\n",
              " 'out',\n",
              " 'on',\n",
              " 'off',\n",
              " 'over',\n",
              " 'under',\n",
              " 'again',\n",
              " 'further',\n",
              " 'then',\n",
              " 'once',\n",
              " 'here',\n",
              " 'there',\n",
              " 'when',\n",
              " 'where',\n",
              " 'why',\n",
              " 'how',\n",
              " 'all',\n",
              " 'any',\n",
              " 'both',\n",
              " 'each',\n",
              " 'few',\n",
              " 'more',\n",
              " 'most',\n",
              " 'other',\n",
              " 'some',\n",
              " 'such',\n",
              " 'no',\n",
              " 'nor',\n",
              " 'not',\n",
              " 'only',\n",
              " 'own',\n",
              " 'same',\n",
              " 'so',\n",
              " 'than',\n",
              " 'too',\n",
              " 'very',\n",
              " 's',\n",
              " 't',\n",
              " 'can',\n",
              " 'will',\n",
              " 'just',\n",
              " 'don',\n",
              " \"don't\",\n",
              " 'should',\n",
              " \"should've\",\n",
              " 'now',\n",
              " 'd',\n",
              " 'll',\n",
              " 'm',\n",
              " 'o',\n",
              " 're',\n",
              " 've',\n",
              " 'y',\n",
              " 'ain',\n",
              " 'aren',\n",
              " \"aren't\",\n",
              " 'couldn',\n",
              " \"couldn't\",\n",
              " 'didn',\n",
              " \"didn't\",\n",
              " 'doesn',\n",
              " \"doesn't\",\n",
              " 'hadn',\n",
              " \"hadn't\",\n",
              " 'hasn',\n",
              " \"hasn't\",\n",
              " 'haven',\n",
              " \"haven't\",\n",
              " 'isn',\n",
              " \"isn't\",\n",
              " 'ma',\n",
              " 'mightn',\n",
              " \"mightn't\",\n",
              " 'mustn',\n",
              " \"mustn't\",\n",
              " 'needn',\n",
              " \"needn't\",\n",
              " 'shan',\n",
              " \"shan't\",\n",
              " 'shouldn',\n",
              " \"shouldn't\",\n",
              " 'wasn',\n",
              " \"wasn't\",\n",
              " 'weren',\n",
              " \"weren't\",\n",
              " 'won',\n",
              " \"won't\",\n",
              " 'wouldn',\n",
              " \"wouldn't\"]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_stopwords(text):\n",
        "  new_text=[]\n",
        "\n",
        "  for word in text.split():\n",
        "    if word in stopwords.words(\"english\"):\n",
        "      new_text.append('')\n",
        "    else:\n",
        "      new_text.append(word)\n",
        "  x=new_text[:]\n",
        "  new_text.clear()\n",
        "  return \" \".join(x)"
      ],
      "metadata": {
        "id": "iycmDOGFil84"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"review\"] = df[\"review\"].apply(remove_stopwords)"
      ],
      "metadata": {
        "id": "WdGq7isxirIV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
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          "height": 423
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        "id": "MT1ZkqgjiwD_",
        "outputId": "b75357ed-d57a-4fa5-8b7c-e0bd1ccf6fd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 review sentiment\n",
              "0     one    reviewers  mentioned   watching  1 oz e...  positive\n",
              "1      wonderful little production.  filming techniq...  positive\n",
              "2      thought    wonderful way  spend time    hot s...  positive\n",
              "3     basically there's  family   little boy (jake) ...  negative\n",
              "4     petter mattei's \"love   time  money\"   visuall...  positive\n",
              "...                                                 ...       ...\n",
              "9995  fun, entertaining movie  wwii german spy (juli...  positive\n",
              "9996  give   break.   anyone say     \"good hockey mo...  negative\n",
              "9997   movie   bad movie.   watching  endless series...  negative\n",
              "9998     movie   probably made  entertain  middle sc...  negative\n",
              "9999  smashing film  film-making. shows  intense  st...  positive\n",
              "\n",
              "[9983 rows x 2 columns]"
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              "      <th>sentiment</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>one    reviewers  mentioned   watching  1 oz e...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>wonderful little production.  filming techniq...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>thought    wonderful way  spend time    hot s...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>basically there's  family   little boy (jake) ...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>petter mattei's \"love   time  money\"   visuall...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>fun, entertaining movie  wwii german spy (juli...</td>\n",
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              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>give   break.   anyone say     \"good hockey mo...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>movie   bad movie.   watching  endless series...</td>\n",
              "      <td>negative</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>movie   probably made  entertain  middle sc...</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>smashing film  film-making. shows  intense  st...</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9983 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb2e5674-73f7-4604-9fff-b42f1d8da52e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </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",
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              "\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eb2e5674-73f7-4604-9fff-b42f1d8da52e 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-eb2e5674-73f7-4604-9fff-b42f1d8da52e');\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": "code",
      "source": [
        "x= df.iloc[:,0:1]\n",
        "y = df[\"sentiment\"]"
      ],
      "metadata": {
        "id": "YwkJE0DKi53K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "DkjksJHUjHAl",
        "outputId": "0005b26f-5230-4f24-e2e3-fd42acfb7766"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 review\n",
              "0     one    reviewers  mentioned   watching  1 oz e...\n",
              "1      wonderful little production.  filming techniq...\n",
              "2      thought    wonderful way  spend time    hot s...\n",
              "3     basically there's  family   little boy (jake) ...\n",
              "4     petter mattei's \"love   time  money\"   visuall...\n",
              "...                                                 ...\n",
              "9995  fun, entertaining movie  wwii german spy (juli...\n",
              "9996  give   break.   anyone say     \"good hockey mo...\n",
              "9997   movie   bad movie.   watching  endless series...\n",
              "9998     movie   probably made  entertain  middle sc...\n",
              "9999  smashing film  film-making. shows  intense  st...\n",
              "\n",
              "[9983 rows x 1 columns]"
            ],
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "      <th>0</th>\n",
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              "      <td>basically there's  family   little boy (jake) ...</td>\n",
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              "      <td>petter mattei's \"love   time  money\"   visuall...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>9995</th>\n",
              "      <td>fun, entertaining movie  wwii german spy (juli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>give   break.   anyone say     \"good hockey mo...</td>\n",
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              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>movie   bad movie.   watching  endless series...</td>\n",
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              "      <th>9998</th>\n",
              "      <td>movie   probably made  entertain  middle sc...</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>smashing film  film-making. shows  intense  st...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>9983 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-808654c9-0807-4f3d-8bfa-04ba288b3376')\"\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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-808654c9-0807-4f3d-8bfa-04ba288b3376 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-808654c9-0807-4f3d-8bfa-04ba288b3376');\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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rktesluvjIgz",
        "outputId": "d4838b0f-e7ea-4b39-be2d-29c1963fa0ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       positive\n",
              "1       positive\n",
              "2       positive\n",
              "3       negative\n",
              "4       positive\n",
              "          ...   \n",
              "9995    positive\n",
              "9996    negative\n",
              "9997    negative\n",
              "9998    negative\n",
              "9999    positive\n",
              "Name: sentiment, Length: 9983, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "\n",
        "y=le.fit_transform(y)"
      ],
      "metadata": {
        "id": "BYrAlVLujIyj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Td82yb3TjXYk",
        "outputId": "98ff99d8-5e70-4326-ca7c-61938d49ebfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=1)"
      ],
      "metadata": {
        "id": "WKIM1vNqjYkq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "psR7xjftjtiE",
        "outputId": "4593e44f-f67c-4345-a0f4-4a5a6babdce3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7986, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BOW**"
      ],
      "metadata": {
        "id": "eYVj8A9jj0iA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n"
      ],
      "metadata": {
        "id": "1r-w9Ex3jzjM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer()"
      ],
      "metadata": {
        "id": "0qeD0sTcjtkv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_bow = cv.fit_transform(x_train[\"review\"]).toarray()\n",
        "x_test_bow = cv.transform(x_test[\"review\"]).toarray()"
      ],
      "metadata": {
        "id": "b7ncopnZjtm8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_bow.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t6LgTGYZjtqf",
        "outputId": "3eabba73-0668-4cf4-913a-b32a2f96b74a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7986, 48282)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_bow.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2yMHLQnlugN",
        "outputId": "0f02b277-4f65-4c37-8bde-e9ee7b6cb694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1997, 48282)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "gnb = GaussianNB()\n",
        "\n",
        "gnb.fit(x_train_bow,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "reV6396vkjOG",
        "outputId": "fad420ff-0312-46ee-a7b0-f51a5a68bb74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = gnb.predict(x_test_bow)\n",
        "\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHg1SO7Yk3XH",
        "outputId": "5a25f7dc-b777-4ebd-b2e1-a8404e3e08ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6324486730095142"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vXaMRck3lNxT",
        "outputId": "99e716d9-e5c1-440c-d21e-370bc6929742"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[717, 235],\n",
              "       [499, 546]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest**"
      ],
      "metadata": {
        "id": "7u6jgBZVm1VN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "rf.fit(x_train_bow,y_train)\n",
        "y_pred = rf.predict(x_test_bow)\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5TS29XimaaE",
        "outputId": "fd221ea7-cc6d-4327-a212-ae6185b7f022"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8482724086129194"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer(max_features=3000) #insteadof taking 48282 features we took 3000\n",
        "x_train_bow = cv.fit_transform(x_train[\"review\"]).toarray()\n",
        "x_test_bow = cv.transform(x_test[\"review\"]).toarray()\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "rf.fit(x_train_bow,y_train)\n",
        "y_pred = rf.predict(x_test_bow)\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBaFyNomm-K2",
        "outputId": "12e6f9c7-9fe6-44e9-d114-88d174875375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8407611417125689"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Memory Error\n",
        "\n",
        "cv = CountVectorizer(ngram_range=(1,2))\n",
        "x_train_bow = cv.fit_transform(x_train[\"review\"]).toarray()\n",
        "x_test_bow = cv.transform(x_test[\"review\"]).toarray()\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "rf.fit(x_train_bow,y_train)\n",
        "y_pred = rf.predict(x_test_bow)\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "id": "vOh73oVmmxXM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer(ngram_range=(1,2),max_features=5000)\n",
        "x_train_bow = cv.fit_transform(x_train[\"review\"]).toarray()\n",
        "x_test_bow = cv.transform(x_test[\"review\"]).toarray()\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "rf.fit(x_train_bow,y_train)\n",
        "y_pred = rf.predict(x_test_bow)\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "id": "THQp7mYEofbY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**TF Idf**"
      ],
      "metadata": {
        "id": "BL9-_Pu8pB01"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer()\n",
        "\n",
        "x_train_tfidf = tfidf.fit_transform(x_train[\"review\"]).toarray()\n",
        "x_test_tfidf = tfidf.transform(x_test[\"review\"]).toarray()\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "rf.fit(x_train_tfidf,y_train)\n",
        "y_pred = rf.predict(x_test_tfidf)\n",
        "accuracy_score(y_test,y_pred)"
      ],
      "metadata": {
        "id": "uu4KVyYZpGj5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}