Sentiment_two.ipynb

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    "# Sentiment analysis of women's clothes reviews\n",
    "\n",
    "## Part 3: Sentiment analysis based on customers rating.\n",
    "\n",
    "The aim of this project is sentiment analysis to determined whether the product is recommended or not based on a review as an input text. In this approach I have used a recommendation based on customers rating where I assigned the values from the ranking to the positive, neutral and negative values.  I have used different machine learning algorithms to get more accurate predictions such as: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest and Ada Boosting as well.\n",
    "\n",
    "### Dataset\n",
    "\n",
    "The dataset comes from Woman Clothing Review that can be find at Kaggle. For this project I used three variables: Rating, Recommended and Review Text.\n",
    "\n",
    "Attributes:\n",
    "\n",
    "- Review Text (text): review text;\n",
    "- Rating (numeric): product score granted by the customer from 1 Worst, to 5 Best;\n",
    "- Recommended IND (numeric): customer recommends the product where 1 is recommended, 0 is not recommended.\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Import libriaries and data"
   ]
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  {
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.pipeline import Pipeline as imbpipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
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      "text/plain": [
       "   Clothing ID  Age                    Title  \\\n",
       "0          767   33                      NaN   \n",
       "1         1080   34                      NaN   \n",
       "2         1077   60  Some major design flaws   \n",
       "3         1049   50         My favorite buy!   \n",
       "4          847   47         Flattering shirt   \n",
       "\n",
       "                                         Review Text  Rating  Recommended IND  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...       4                1   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...       5                1   \n",
       "2  I had such high hopes for this dress and reall...       3                0   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...       5                1   \n",
       "4  This shirt is very flattering to all due to th...       5                1   \n",
       "\n",
       "   Positive Feedback Count   Division Name Department Name Class Name  \n",
       "0                        0       Initmates        Intimate  Intimates  \n",
       "1                        4         General         Dresses    Dresses  \n",
       "2                        0         General         Dresses    Dresses  \n",
       "3                        0  General Petite         Bottoms      Pants  \n",
       "4                        6         General            Tops    Blouses  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "df = pd.read_csv('Womens Clothing Reviews.csv', header=0,index_col=0)\n",
    "df.head()"
   ]
  },
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   "source": [
    "Loading chosen variables: Rating, Recommended and Review Text."
   ]
  },
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    "id": "KYc-CGfElODI",
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      "text/plain": [
       "   Rating  Recommended IND                                        Review Text\n",
       "0       4                1  Absolutely wonderful - silky and sexy and comf...\n",
       "1       5                1  Love this dress!  it's sooo pretty.  i happene...\n",
       "2       3                0  I had such high hopes for this dress and reall...\n",
       "3       5                1  I love, love, love this jumpsuit. it's fun, fl...\n",
       "4       5                1  This shirt is very flattering to all due to th..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_text = df[['Rating', 'Recommended IND', 'Review Text']]\n",
    "df_text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FEP9dxRblODL"
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   "source": [
    "**First observations**:"
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  {
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   "execution_count": 7,
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 23486 entries, 0 to 23485\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Rating           23486 non-null  int64 \n",
      " 1   Recommended IND  23486 non-null  int64 \n",
      " 2   Review Text      22641 non-null  object\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 733.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df_text.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Kns0qsdslODT"
   },
   "source": [
    "Shape of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "(23486, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_text.shape"
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   "cell_type": "markdown",
   "metadata": {
    "id": "N34tWXK5lODe"
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   "source": [
    "Type of variables:"
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   "cell_type": "code",
   "execution_count": 9,
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    "executionInfo": {
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  {
   "cell_type": "markdown",
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    "id": "gFqqsAIblODo"
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   "source": [
    "Checking the missing values in data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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       "</div><br><label><b>dtype:</b> int64</label>"
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      "text/plain": [
       "Rating               0\n",
       "Recommended IND      0\n",
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   "source": [
    "df_text.isnull().sum()"
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sNvyURkFlODu"
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   "source": [
    "The dataset includes information about 23486 women's clothes reviews placed in 3 columns. After first observations one can see that there are missing values in Review variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "edYGCV-tlODv"
   },
   "source": [
    "## Data preparation\n",
    "\n",
    "Rename of chosen variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
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    "executionInfo": {
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      "text/plain": [
       "   Rating  Recommended                                             Review\n",
       "0       4            1  Absolutely wonderful - silky and sexy and comf...\n",
       "1       5            1  Love this dress!  it's sooo pretty.  i happene...\n",
       "2       3            0  I had such high hopes for this dress and reall...\n",
       "3       5            1  I love, love, love this jumpsuit. it's fun, fl...\n",
       "4       5            1  This shirt is very flattering to all due to th..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_text = df_text.rename(columns={\"Review Text\": \"Review\", \"Recommended IND\":\"Recommended\"})\n",
    "df_text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LXrVaaCnlOD6"
   },
   "source": [
    "Deleting missing values in Review variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 374,
     "status": "ok",
     "timestamp": 1738348179105,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "L2SM18o1lOD8"
   },
   "outputs": [],
   "source": [
    "df_text.dropna(subset=['Review'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 178
    },
    "executionInfo": {
     "elapsed": 336,
     "status": "ok",
     "timestamp": 1738348181414,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "7UGjGXnVlOD9",
    "outputId": "9dd9c6d8-6aa5-48c0-8776-95456ace1fcc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "Rating         0\n",
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       "dtype: int64"
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     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "df_text.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 328,
     "status": "ok",
     "timestamp": 1738348205799,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "h1mLC6RTlOD_"
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   "outputs": [],
   "source": [
    "df_text['Review'] = df_text['Review'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EnotSxjBlOEL"
   },
   "source": [
    "Now I create a new column that will consider the ratings as follows: assign the values from the ranking to the positive, neutral and negative values. Rating 4 or higher to Positive, equal to 3 Neutral and rating 2 or less to Negative.\n",
    "\n",
    "Function to assign rating class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
     "elapsed": 294,
     "status": "ok",
     "timestamp": 1738348208761,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "LBeHVtKVlOEM"
   },
   "outputs": [],
   "source": [
    "def rating(row):\n",
    "    if row >= 4:\n",
    "        return 'Positive'\n",
    "    if row == 3:\n",
    "        return 'Neutral'\n",
    "    if row <= 2:\n",
    "        return 'Negative'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "executionInfo": {
     "elapsed": 332,
     "status": "ok",
     "timestamp": 1738348212099,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "ADXjrxj5lOEO"
   },
   "outputs": [],
   "source": [
    "df_text['Class'] = df_text['Rating'].apply(rating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 320,
     "status": "ok",
     "timestamp": 1738348214831,
     "user": {
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     "user_tz": -60
    },
    "id": "M2zUqO5ElOEP",
    "outputId": "517bd03f-8e89-4feb-e102-529e47478942"
   },
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>I love, love, love this jumpsuit. it's fun, fl...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71d2c67d-8c82-401d-b514-e1ea7287d8ee')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "    [theme=dark] .colab-df-convert {\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
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       "        document.querySelector('#df-71d2c67d-8c82-401d-b514-e1ea7287d8ee button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\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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       "\n",
       "\n",
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       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b7b8156-bf61-4c56-a779-ea8ed69995be')\"\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-8b7b8156-bf61-4c56-a779-ea8ed69995be button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   Rating  Recommended                                             Review  \\\n",
       "0       4            1  Absolutely wonderful - silky and sexy and comf...   \n",
       "1       5            1  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2       3            0  I had such high hopes for this dress and reall...   \n",
       "3       5            1  I love, love, love this jumpsuit. it's fun, fl...   \n",
       "4       5            1  This shirt is very flattering to all due to th...   \n",
       "\n",
       "      Class  \n",
       "0  Positive  \n",
       "1  Positive  \n",
       "2   Neutral  \n",
       "3  Positive  \n",
       "4  Positive  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3OgHUPwBlOES"
   },
   "source": [
    "Saved cleaned dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WHAr84bKlOEU"
   },
   "outputs": [],
   "source": [
    "CLEANED_DATA_PATH = r'data\\reviews_class.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "V3k3Arn-lOEV"
   },
   "outputs": [],
   "source": [
    "df_text.to_csv(CLEANED_DATA_PATH, encoding='utf-8')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OARFl77_lOEX"
   },
   "source": [
    "## Basic data analysis\n",
    "\n",
    "Checking proportion target class variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "executionInfo": {
     "elapsed": 329,
     "status": "ok",
     "timestamp": 1738348225137,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "NVL6OnvSlOEY",
    "outputId": "3ae9511d-9428-4401-c25c-be3ca82f3fd3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Positive</th>\n",
       "      <td>17448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neutral</th>\n",
       "      <td>2823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Negative</th>\n",
       "      <td>2370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "Class\n",
       "Positive    17448\n",
       "Neutral      2823\n",
       "Negative     2370\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_text['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "executionInfo": {
     "elapsed": 947,
     "status": "ok",
     "timestamp": 1738348232324,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "ZEdDuorWlOEa",
    "outputId": "6e60c6db-c365-4fe2-ffe7-9f28f87cc89d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lKPRERMQyFHoiImIZCj0REbEMhZ6IiFiGQk9ERCyjykJv2LBh+Pj4EBMT42zr06cPHTt2JCwsjLCwMM6cOQNAXl4effv2pX379kRFRVFSUgJASUkJUVFRtG/fnr59+5KXlweAMYZJkyYRFBREeHg4hw4dqqphiIjITaTKQm/69Om89dZbl7WvXbuWtLQ00tLSaNCgAQDz588nOjqajIwMAgICiI+PByA+Pp6AgAAyMjKIjo5m/vz5AKSkpJCXl0dmZiZxcXHMmjWrqoYhIiI3kSoLvT59+uDl5XVNyyYnJzN27FgAxowZw4YNG665ffDgwezcuRNjzI0egoiI3GRcfk3voYceokuXLixcuNDZVlhYiLe3NwD+/v5kZ2cDkJOTg7+/PwB2u52CgoLL2m02Gz4+PuTn57twFCIiUhu5u7KzVatW4e/vT2FhIffffz8dOnRg8ODBVdpnfHy883Rpbm5ulfYlIiI1m0uP9C4dnXl7e/Pggw+yd+9e53RhYSEA2dnZ+Pn5AeDn5+c86isoKMBut1/WbozhxIkT+Pr6XrHP2NhYUlNTSU1NpVmzZlU2NhERqflcFnqlpaXOuy/PnTvH+++/T3BwMACRkZGsWLECgJUrVzJkyJArtkdGRl7WnpKSQkREBDabzVVDERGRWqrKTm/269eP/fv3U1RURMuWLVmzZg3Tpk3j/PnzXLhwgSFDhjg/zjBnzhxiYmJYtGgRISEhPPPMMwBMnDiRUaNGERQUhL+/P2vXrgUuht7GjRsJDAzEbreTkJBQVcMQEZGbiM1Y6LZHh8NBamrqNS+ftTirCquRX6L19NbVXYKI1HBXe6/XE1lERMQyFHoiImIZCj0REbEMhZ6IiFiGQk9ERCxDoSciIpah0BMREctQ6ImIiGUo9ERExDIUeiIiYhkKPRERsQyFnoiIWIZCT0RELEOhJyIilqHQExERy1DoiYiIZSj0RETEMhR6IiJiGQo9ERGxDIWeiIhYRpWF3rBhw/Dx8SEmJgaA4uJiBg4cSMeOHQkODuall15yLhsXF0fLli0JCwsjLCyM7du3A2CMYdKkSQQFBREeHs6hQ4ec68ybN4+goCCCg4PZs2dPVQ1DRERuIlUWetOnT+ett94q1zZ79my+/vprdu/ezcsvv0xmZma5eWlpaaSlpdGrVy8AUlJSyMvLIzMzk7i4OGbNmgVAeno6mzZt4uuvvyYhIYEpU6ZU1TBEROQmUmWh16dPH7y8vJzTDRs2pHfv3gA0atSIDh06cOzYsatuIzk5mbFjxwIwePBgdu7ciTGGDRs2MGrUKNzd3enUqRPnz58nJyenqoYiIiI3iWq5pnf06FG++OILunbt6mxbuHAhoaGhTJ48mdOnTwOQk5ODv78/ADabDR8fH/Lz88u1A/j7+yv0RESkUi4PvbNnzzJixAheeOEFPD09AZg8eTIZGRns27ePhg0b8vTTT9+w/uLj43E4HDgcDnJzc2/YdkVEpPZxaegZY3j44YcZNGiQ8wYXgObNm+Pm5oabmxsTJkxg7969APj5+ZGdne1c98SJE/j6+pZrB8jOzsbPz++KfcbGxpKamkpqairNmjWrwtGJiEhN59LQmzNnDg0bNuTJJ58s1/7Ta3vr168nODgYgMjISFasWAFcvKklIiICm81GZGQkq1evprS0lPT0dOrWrVth6ImIiFziXlUb7tevH/v376eoqIiWLVuyevVqFixYwB133EFYWBgACxYs4L777mPmzJmkpaVhs9m47bbbWLZsGXAx9DZu3EhgYCB2u52EhAQAQkNDGTBgAB06dMDDw4M33nijqoYhIiI3EZsxxlR3Ea7icDhITU295uWzFmdVYTXyS7Se3rq6SxCRGu5q7/V6IouIiFiGQk9ERCxDoSciIpah0BMREctQ6ImIiGUo9ERExDIUeiIiYhkKPRERsQyFnoiIWIZCT0RELEOhJyIilqHQExERy1DoiYiIZSj0RETEMhR6IiJiGQo9ERGxDIWeiIhYhkJPREQsQ6EnIiKWodATERHLUOiJiIhlVFnoDRs2DB8fH2JiYpxte/bsITg4mKCgIObNm+dsP3ToEOHh4QQFBTFp0iSMMQDk5eXRt29f2rdvT1RUFCUlJQCUlJQQFRVF+/bt6du3L3l5eVU1DBERuYlUGnoffPDBNbX93PTp03nrrbfKtU2ZMoXVq1fzzTffsGnTJtLT0wGYNWsWcXFxZGZmkpeXR0pKCgDz588nOjqajIwMAgICiI+PByA+Pp6AgAAyMjKIjo5m/vz5lY9UREQsr9LQ+/Of/3xZ2xNPPFHphvv06YOXl5dzOicnh9LSUkJDQ3Fzc2PkyJFs3LgRYww7d+5k8ODBAIwZM4YNGzYAkJyczNixY6+5XURE5GrcK5rxz3/+k82bN5OTk8PMmTOd7SdPnsTNze26O8rJycHf39857e/vzyeffEJ+fj5NmjTBZrM527OzswEoLCzE29v7svafbstut1NQUHDd9YiIiPVUGHrNmjUjJCSE+vXrExwc7Gz38vLiueeec0lxN0J8fLzztGhubm41VyMiItWpwtDr3LkznTt3ZuzYsdSp8+vvd/Hz83MeqQFkZ2fj5+eHr68vx48fxxiDzWZztgN4e3s7j/Z+2n5pW02bNqWgoAC73V5hv7GxscTGxgLgcDh+9ThERKT2qjTNPv30U/r160f79u0JCAigXbt2BAQEXHdHfn5+uLm58cUXX3DhwgUSEhIYMmQINpsNh8PhvHll1apVDBkyBIDIyEhWrFgBwMqVKytsj4yMvO56RETEemzm0ucDKtChQweWLl3KnXfeWe5anqen51U33K9fP/bv309RURFNmjQhMTERm83GI488QklJCWPHjiUuLg6AjIwMRo4cSUFBAffccw9Lly6lTp06/Pjjj8TExJCdnU1ISAirV6+mQYMGnDlzhlGjRnHgwAH8/f1Zu3Ytt9xyS6WDdTgcpKamXsOv5aKsxVnXvKy4Ruvprau7BBGp4a72Xl9p6EVERLBr164qKczVFHq1n0JPRCpztff6Cq/pXXLXXXcxbtw4hg4dSv369Z3tgwYNunEVioiIuECloVdYWEidOnVITk52ttlsNoWeiIjUOpWG3t///ndX1CEiIlLlKg293/3ud84Pjv/Um2++WSUFiYiIVJVKQ++nD4w+e/YsSUlJ+Pj4VGlRIiIiVaHS0Lv0TMxLoqKi9CFvERGplSoNveLiYufrsrIy0tLSOHHiRJUWJSIiUhUqDb3g4GBsNhvGGNzd3Wnbti3Lli1zRW0iIiI3VKWhd+TIEVfUISIiUuUqDb1z587xyiuvsH37dgDuvvtuJk+eTL169aq8OBERkRup0gdOP/roo3z77bfMmDGDGTNmkJGRwaOPPuqK2kRERG6oSo/09u3bx/79+53TERERhIWFVWVNIiIiVaLSIz0PDw8+++wz5/Tnn39e7hmcIiIitUWlR3pLly5l3LhxnDt3DmMMHh4eLF++3AWliYiI3FiVht7q1av55JNPnN+efuHCBZ5//nm6dOlS5cWJiIjcSJWe3vznP/+Jj48P3t7eeHt706RJEzZv3uyK2kRERG6oSkOvrKyMU6dOOadPnjzJ+fPnq7QoERGRqlDp6c3p06fTs2dPRowYAcCaNWv4wx/+UOWFiYiI3GiVht6ECRPo3r07W7ZsAeDtt98mODi4ygsTERG50SoNPYCQkBBCQkKquhYREZEqVek1vRvtm2++ISwszPnToEEDkpKSGD9+PAEBAc72Q4cOAVBSUkJUVBTt27enb9++5OXlAWCMYdKkSQQFBREeHu5cXkREpCIuD70OHTqQlpZGWloaO3bswNPTk3vvvReAJUuWOOcFBgYCEB8fT0BAABkZGURHRzN//nwAUlJSyMvLIzMzk7i4OGbNmuXqoYiISC3j8tD7qeTkZO655x48PT2vuszYsWMBGDNmDBs2bLisffDgwezcuRNjTNUXLSIitVa1ht4777zjvCsUYMaMGXTu3Jk5c+Zw4cIFAHJycvD39wfAbrdTUFBwWbvNZsPHx4f8/HzXDkBERGqVagu9kydPsnPnTgYNGgTAc889x8GDB9m9ezeHDx9m6dKlN6Sf+Ph4HA4HDoeD3NzcG7JNERGpnaot9NavX0///v3x8PAA4NZbb8Vms+Hh4cHDDz/M3r17AfDz8yM7OxuAgoIC7Hb7Ze3GGE6cOIGvr+9l/cTGxpKamkpqairNmjVzwchERKSmqrbQ+/mpzWPHjgEXnwCTnJzs/CxgZGQkK1asAGDlypVERkZe1p6SkkJERAQ2m82VQxARkVrmmj6nd6MVFhayZ88e1q1b52wbPXo0eXl5lJWV4XA4mDZtGgATJ05k1KhRBAUF4e/vz9q1a4GLobdx40YCAwOx2+0kJCRUx1BERKQWqZbQ8/b25ocffijX9vHHH19x2Uuf4/u5OnXqsGzZsqooT0REblLVevemiIiIKyn0RETEMhR6IiJiGQo9ERGxDIWeiIhYhkJPREQsQ6EnIiKWodATERHLUOiJiIhlKPRERMQyFHoiImIZCj0REbEMhZ6IiFiGQk9ERCxDoSciIpah0BMREctQ6ImIiGUo9ERExDIUeiIiYhkKPRERsQyFnoiIWEa1hF7btm0JDQ0lLCyMvn37AnDo0CHCw8MJCgpi0qRJGGMAyMvLo2/fvrRv356oqChKSkoAKCkpISoqivbt29O3b1/y8vKqYygiIlKLVNuR3s6dO0lLS2PLli0AzJo1i7i4ODIzM8nLyyMlJQWA+fPnEx0dTUZGBgEBAcTHxwMQHx9PQEAAGRkZREdHM3/+/OoaioiI1BI14vSmMYadO3cyePBgAMaMGcOGDRsASE5OZuzYsdfcLiIiUpFqCT2bzUbv3r3p1q0bq1atIj8/nyZNmmCz2QDw9/cnOzsbgMLCQry9vS9rz8nJwd/fHwC73U5BQcEV+4qPj8fhcOBwOMjNza3ikYmISE3mXh2d7tixA39/f44dO0a/fv1o1apVlfUVGxtLbGwsAA6Ho8r6ERGRmq9ajvQuHaHdeuutDBo0iEOHDnH8+HHnzSvZ2dn4+fkB4O3tTWFh4WXtfn5+zqO+goIC7Ha7i0chIiK1jctDr6ioiFOnTgFw+vRpPv74Y0JCQnA4HM6bV1atWsWQIUMAiIyMZMWKFQCsXLmywvbIyEhXD0VERGoZl4feDz/8QM+ePencuTMOh4OHH36Ybt26sWDBAubOnUtgYCA+Pj7Om1rmzJlDYmIiQUFBZGZmOk9VTpw4kczMTIKCgkhMTGT27NmuHoqIiNQyNnPpnKIFOBwOUlNTr3n5rMVZVViN/BKtp7eu7hJEpIa72nt9jfjIgoiIiCso9ERExDIUeiIiYhkKPRERsQyFnoiIWIZCT0RELEOhJyIilqHQExERy1DoiYiIZSj0RETEMhR6IiJiGQo9ERGxDIWeiIhYhkJPREQsQ6EnIiKWodATERHLUOiJiIhlKPRERMQyFHoiImIZCj0REbEMd1d3ePToUcaOHUtubi7u7u78x3/8B8OHD2f8+PFs27aNxo0bA7Bu3ToCAwMpKSnhoYceIj09nZYtW5KYmEjTpk0xxjB58mT+9a9/YbfbWbNmDYGBga4ejtykshZnVXcJ8jOtp7eu7hLkJuDyIz13d3cWLVrEV199xebNm/n3f/93ioqKAFiyZAlpaWmkpaU5Ayw+Pp6AgAAyMjKIjo5m/vz5AKSkpJCXl0dmZiZxcXHMmjXL1UMREZFaxuWhd+uttxIWFgZAixYtaNq0KcePH69w+eTkZMaOHQvAmDFj2LBhw2XtgwcPZufOnRhjqrZ4ERGp1ar1mt7nn3/OhQsXaNWqFQAzZsygc+fOzJkzhwsXLgCQk5ODv78/AHa7nYKCgsvabTYbPj4+5Ofnu34QIiJSa1Rb6B0/fpyHH36YZcuWAfDcc89x8OBBdu/ezeHDh1m6dOkN6Sc+Ph6Hw4HD4SA3N/eGbFNERGonl9/IAnD27FmGDh3K7Nmz+c1vfgNcPO0J4OHhwcMPP0xiYiIAfn5+ZGdn07RpUwoKCrDb7eXaw8PDMcZw4sQJfH19L+srNjaW2NhYABwOhwtGJyK1lW5gqplu5E1MLj/SM8Ywfvx4fvvb3zqvyQEcO3YMgLKyMpKTkwkODgYgMjKSFStWALBy5UoiIyMva09JSSEiIgKbzebKoYiISC3j8iO9Tz/9lDVr1hAaGkpSUhIAK1asYPr06eTl5VFWVobD4WDatGkATJw4kVGjRhEUFIS/vz9r164FLobexo0bCQwMxG63k5CQ4OqhiIhILePy0OvZsydlZWWXtX/88cdXXL5BgwbOcPypOnXqOK8HioiIXAs9kUVERCxDoSciIpah0BMREctQ6ImIiGUo9ERExDIUeiIiYhkKPRERsQyFnoiIWIZCT0RELEOhJyIilqHQExERy1DoiYiIZSj0RETEMhR6IiJiGQo9ERGxDIWeiIhYhkJPREQsQ6EnIiKWodATERHLUOiJiIhl1OrQ27hxIx06dKB9+/bEx8dXdzkiIlLDuVd3Ab9UaWkpf/zjH9myZQve3t7ceeedDBs2DF9f3+ouTUREaqhae6S3Z88egoOD8ff3p1GjRgwcOJDNmzdXd1kiIlKD1drQy8nJwd/f3znt7+9PdnZ2NVYkIiI1Xa09vXmt4uPjndf7vvrqKxwORzVXVD1yc3Np1qxZdZfx662u7gJql5tmv4P2/XWy8r4/cuRIhfNqbej5+fmVO7LLzs6me/fuly0XGxtLbGysK0urkRwOB6mpqdVdhriY9rt1ad9fWa09vdm9e3cOHDhAdnY2p0+f5v333+e+++6r7rJERKQGq7VHeu7u7vzXf/0Xffv2paysjJkzZ+rOzavQ0a41ab9bl/b9ldmMMaa6ixAREXGFWnt6U0RE5Hop9ERExDIUejWcu7s7YWFhhISEMHz4cIqLi69r/aVLl7JmzRoAli9fTm5urnPeoEGDOHPmzA2tV24cm83Gk08+6ZyeMWMGy5cv/0Xb2rp1K3v27Lnu9caPH8/GjRt/UZ9y7W7kvq5IUlIS3377rXM6NjaWQ4cO3dA+agOFXg1nt9tJS0vjwIED1KtXj6VLl17X+pMmTWLEiBHA5aG3adMmGjRocEPrlRunUaNGrFq1ilOnTv3qbV0t9C5cuPCrty+/zo3c1xX5eejFx8cTGBhYZf3VVAq9WqRXr15kZmaSl5fHkCFDCA0NpU+fPnz33XcAJCQkcPvtt9O5c2ceeOABAOLi4vjb3/7Ge++9x2effUZMTAzh4eEAtG3bltOnTzNr1izefPNNZz8TJkzgvffe48KFCzz++ON069aNzp07s2rVKpeP2crq16/P6NGjeeWVVy6bd+jQIe677z7Cw8P57W9/6/wb6NOnDwcOHADgwIED9OnTh6NHj7J06VLmz59PWFgYaWlpjB8/nsmTJ9O9e3fmz59PUlIS3bt3p0uXLgwePJiCggIXjlR+yb7etWsXwcHBdOnShSlTphATEwNwxX25e/dukpOTmTZtGmFhYeTm5jr/Vl599VWeeuopZ3/z5s3jxRdfBGDBggV069aN0NBQ/vM//7PqfxGuYKRG8/X1NcYYc/78eXP//febV155xUyZMsUsWLDAGGNMQkKCGTJkiDHGmJCQEPPtt98aY4wpKCgwxhgzd+5c89JLLxljjOndu7dJT093brtNmzbm1KlTZu/evWbAgAHOflq3bm3OnDljXnvtNfPCCy8YY4wpLi42oaGhJi8vzwWjFmMu7vvc3FwTEBBgzpw5Yx5//HHz97//3RhjTP/+/c2RI0eMMcZ89NFHJiYmxhhTfh+np6eb3r17G2PK/x0YY8y4cePM8OHDTVlZmTHGmOPHjztfL1682PzlL39xLrdhw4aqHqrl/ZJ9HRwcbD7//HNjjDGjR4820dHRxphr35eX/lb+93//1wQHBzvbQ0NDzdGjR82HH35opk6dasrKykxpaanp27dvufeP2qrWfk7PKgoKCggLCwPg7rvv5pFHHqF79+5s2rQJgAcffJDp06cD0KNHDx599FFGjx7t/F/ftQgPD+fw4cOcOHGCPXv20KNHDzw8PNi8eTMHDhxg5cqVABQWFnL48GF9HtKFbrnlFiIjI8sdiZ8+fZrt27czdOhQAIwxeHp6Xve2Y2JisNlsAGRlZTF8+HB++OEHzpw5w1133XVD6pdrdz37uqCggPPnz9O1a1cARowYwT/+8Q/g+vdl8+bNueWWW5yXUDw9PWnZsiWLFi0iJSWF7du3A3Dq1Cm+/fZbQkJCqmD0rqPQq+EuXdO7mktvXK+++iqpqals2LCB8PBw0tPTr7mfoUOHkpSUxM6dOxk+fDgAZWVlvPbaa/Tu3fsX1y+/3owZM+jXrx8DBw4ELu6X5s2bX/Hvwt3dnbKyMgDOnj171e02bNjQ+XratGk88cQT9O/fn40bN97wmyjk2lzrvj5x4kSF2/gl+/LBBx8kMTGRevXqlfv3P3fuXMaNG/eLx1MT6ZpeLdSzZ0/efvttANauXet85ujhw4eJiIjgr3/9K/Xq1SM/P7/cel5eXhVeKB8+fDirV6/mX//6l/MfXP/+/XnllVecNzocOHBANz1Ug1atWtGjRw/WrVsHQOPGjWnevDkbNmwALt6Icuk6Xps2bZxvkO+++65zG1fb9wAnT57E398fYwxvvfVWFY1EKnOt+9rHxwd3d3fnvk5MTHRuo6J9ebW/gaioKN59913WrVvnDL3+/fsTHx/vvGP8u+++o7Cw8IaP2dUUerVQXFwcW7duJTQ0lJdffpnFixcDF/+X2KlTJzp16sSwYcNo2bJlufXGjx/P+PHjnTey/FR4eDiZmZlERETg4eEBwMSJE2nbti1dunQhJCSEP/zhDxg9wKdazJo1i5ycHOf022+/zUsvvUTnzp3p1KkTH330EQB//OMfef7557nzzjs5d+6cc/khQ4awevVq540sPzd37lyGDBlCt27daNWqVZWPRyp2rft62bJljB49mq5du1K/fn0aN24MVLwvR44cyTPPPOO8keWnmjdvTtOmTfHw8HC+bwwYMIBhw4bhcDgICQlhzJgxlJSUVPXwq5weQyYiUgsVFRU5r+VOnTqVjh07MnXq1GququbTkZ6ISC20fv16wsLCuOOOO8jPz+eRRx6p7pJqBR3piYiIZehIT0RELEOhJyIilqHQExERy9CH00Vqme+//55p06axf/9+fHx8aNeuHeHh4fz44483z/MRRaqIjvREahFjDA888ACDBw/m0KFDfPbZZ0ybNu0XPYZMxIoUeiK1yEcffUSjRo3K3Z7eq1cvGjVq5Jyu6BsTrvQtHFu2bKFTp0507tz5ig8tELnZKPREapGvvvrK+ZDhivTu3Zvdu3ezb98+7rvvPl5++WUA/vrXv5KcnMz+/fudj6dauHAhCxcuZP/+/c4nfYjczBR6IjeZrKws7r33Xjp16sSSJUv46quvgP//LRzx8fHOx8n16NGD2bNns2TJEuczFkVuZgo9kVrk9ttvZ9++fVddZtq0acycOZP09HQWLVrk/LaFV199lWeffZbDhw8THh7OmTNnmD17Nm+++SanTp3C4XCQnZ3timGIVBuFnkgt0q9fP06ePFnu62J27NjB6dOnndMVPWX/St/CcejQITp37swTTzzBHXfcwZEjR1w5HBGXU+iJ1CI2m42kpCSSkpIIDAwkODiYl156iaKiIucyFT1l/0rfwvHiiy8SHBxMaGgofn5+REREVMewRFxGz94UERHL0JGeiIhYhkJPREQsQ6EnIiKWodATERHLUOiJiIhlKPRERMQyFHoiImIZCj0REbGM/wMtho3A6V3CLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 490x280 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,4), dpi=70)\n",
    "sns.countplot(x='Class',data=df_text, color='violet')\n",
    "plt.xlabel('Class')\n",
    "plt.title('Proportion of sentiment class');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L-uakFZjlOEc"
   },
   "source": [
    "The target class variable is imbalanced, where \"Positive\" class are more dominating than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8e70jIm2lOEd"
   },
   "source": [
    "Length of characters in each review:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 2694,
     "status": "ok",
     "timestamp": 1738348239105,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "6_tvH2NYlOEe",
    "outputId": "6f77622a-8a0d-40e0-dd13-855c13fa68b2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df_text\",\n  \"rows\": 22641,\n  \"fields\": [\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          5,\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recommended\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22634,\n        \"samples\": [\n          \"This top is really pretty and nice quality. runs big - i went down a size, and its perfect. coloring is more subtle in person than in the photo.\",\n          \"I was so excited to splurge on a mara hoffman suit and was sorely disappointed when i received this suit in the mail. i'm petite and fairly curvy, so i knew it would likely be a little long in the torso but the whole suit was so oddly shaped, i couldn't imagine anyone it would fit. the square shape in between the halter pieces is awkward, and the pieces that are supposed to hold your chest are super wide and loose.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 143,\n        \"min\": 9,\n        \"max\": 508,\n        \"num_unique_values\": 497,\n        \"samples\": [\n          9,\n          150\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "\n",
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       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   Rating  Recommended                                             Review  \\\n",
       "0       4            1  Absolutely wonderful - silky and sexy and comf...   \n",
       "1       5            1  Love this dress!  it's sooo pretty.  i happene...   \n",
       "2       3            0  I had such high hopes for this dress and reall...   \n",
       "3       5            1  I love, love, love this jumpsuit. it's fun, fl...   \n",
       "4       5            1  This shirt is very flattering to all due to th...   \n",
       "\n",
       "      Class  length  \n",
       "0  Positive      53  \n",
       "1  Positive     303  \n",
       "2   Neutral     500  \n",
       "3  Positive     124  \n",
       "4  Positive     192  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_text['length'] = df_text.Review.apply(len)\n",
    "df_text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g4OimxFjlOEg"
   },
   "source": [
    "Histogram of reviews length:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "78eRJaLJlOEi",
    "outputId": "e2b3bbfa-d793-44b3-bd2a-3ff52a4c31f6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 4))\n",
    "df_text[df_text.Class == 'Positive'].length.plot(bins=35, kind='hist', color='green', label='Positive', alpha=0.6)\n",
    "df_text[df_text.Class == 'Negative'].length.plot(kind='hist', color='blue', label='Negative', alpha=0.6)\n",
    "df_text[df_text.Class == 'Neutral'].length.plot(kind='hist', color='red', label='Neutral', alpha=0.6)\n",
    "plt.legend()\n",
    "plt.xlabel(\"Reviews Length\")\n",
    "plt.title('Distribution of reviews length');\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h6BvibcTlOEk"
   },
   "source": [
    "The largest reviews has over 500 characters but some of them are very short."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ufYX7mqclOEl"
   },
   "source": [
    "## Text Pre-processing\n",
    "\n",
    "In this part I change text to tokens and convertion all of the words to lower case. Then I remove non-alphabetic character, bad characters, numbers and stop words. The next step I apply lemmatizing for each line of text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2789,
     "status": "ok",
     "timestamp": 1738348274211,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "GSRwK4tBlOEn",
    "outputId": "fa86044e-47bf-471f-9ee0-f5bbdcec67e1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
      "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 295,
     "status": "ok",
     "timestamp": 1738348285782,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "1ahNA-NslOEp",
    "outputId": "3714b61d-c411-426e-fc29-6d6269584663"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', \"you've\", 'himself', 'they', 'that', 'been', 'a', 'while', 'through', 'in', 'here', 'few', 'own', 'just', 're', 'doesn', 'ma', \"shouldn't\"]\n"
     ]
    }
   ],
   "source": [
    "stop = stopwords.words('english')\n",
    "print(stop[::10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 237,
     "status": "ok",
     "timestamp": 1738348288737,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "_bJKkLTNlOEr"
   },
   "outputs": [],
   "source": [
    "#clothing stopwords\n",
    "clothes =['dress','color','wear','top','sweater','material','shirt','jeans','pant',\n",
    "          'skirt','order','white','black','fabric','blouse','sleeve','even', 'jacket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 314,
     "status": "ok",
     "timestamp": 1738348290667,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "v_fKBGpSlOEs"
   },
   "outputs": [],
   "source": [
    "lem = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 282,
     "status": "ok",
     "timestamp": 1738348309691,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "L5HwOJJklOEu"
   },
   "outputs": [],
   "source": [
    "def clean_text(words: str) -> str:\n",
    "    \"\"\"Clean text\"\"\"\n",
    "    words = re.sub(\"[^a-zA-Z]\",\" \", words)\n",
    "    text = words.lower().split()\n",
    "    return \" \".join(text)\n",
    "\n",
    "def remove_stopwords(review: str) -> str:\n",
    "    \"\"\"Removing stopwords\"\"\"\n",
    "    text = [word.lower() for word in review.split() if word.lower() not in stop and word.lower() not in clothes]\n",
    "    return \" \".join(text)\n",
    "\n",
    "def remove_numbers(text: str) -> str:\n",
    "    \"\"\"Removing numbers from text\"\"\"\n",
    "    new_text = []\n",
    "    for word in text.split():\n",
    "        if not re.search('\\d', word):\n",
    "            new_text.append(word)\n",
    "    return ' '.join(new_text)\n",
    "\n",
    "def get_lemmatize(text: str) -> str:\n",
    "    \"\"\"Applying lemmatizing\"\"\"\n",
    "    lem_text = [lem.lemmatize(word) for word in text.split()]\n",
    "    return \" \".join(lem_text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 12293,
     "status": "ok",
     "timestamp": 1738348331641,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "EOKYnhOqlOEw"
   },
   "outputs": [],
   "source": [
    "df_text['Review'] = df_text['Review'].apply(clean_text)\n",
    "df_text['Review'] = df_text['Review'].apply(remove_stopwords)\n",
    "df_text['Review'] = df_text['Review'].apply(remove_numbers)\n",
    "df_text['Review'] = df_text['Review'].apply(get_lemmatize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 365,
     "status": "ok",
     "timestamp": 1738348334301,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "xI1lJ0X_lOEy",
    "outputId": "5f66005d-d062-40f6-8a52-aa216ed4e56b"
   },
   "outputs": [
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      ],
      "text/plain": [
       "   Rating  Recommended                                             Review  \\\n",
       "0       4            1        absolutely wonderful silky sexy comfortable   \n",
       "1       5            1  love sooo pretty happened find store glad bc n...   \n",
       "2       3            0  high hope really wanted work initially ordered...   \n",
       "3       5            1  love love love jumpsuit fun flirty fabulous ev...   \n",
       "4       5            1  flattering due adjustable front tie perfect le...   \n",
       "\n",
       "      Class  length  \n",
       "0  Positive      53  \n",
       "1  Positive     303  \n",
       "2   Neutral     500  \n",
       "3  Positive     124  \n",
       "4  Positive     192  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hr7n93ndlOE0"
   },
   "source": [
    "## Build models\n",
    "\n",
    "I have used the following classification models:\n",
    "- Logistic Regression,\n",
    "- Random Forest Classifier,\n",
    "- Naive Bayes Classifier,\n",
    "- SVM (Support Vector Machine),\n",
    "- Ada Boosting.\n",
    "\n",
    "To run machine learning algorithms need to convert the text files into numerical feature vectors. I have used bag of words model for modelling. This model convert the text documents to a matrix of token counts (CountVectorizer), then transform a count matrix to a normalized TF-IDF representation (tf-idf transformer).\n",
    "\n",
    "**Pipeline**\n",
    "\n",
    "To make the vectorizer => transformer => SMOTE  => classifier easier to work with I have used Pipeline class in scikit-learn.\n",
    "\n",
    "**Model Evaluation**\n",
    "\n",
    "To evaluate the models I have used accuracy score and classification report(precision, recall, F1-score).\n",
    "\n",
    "Spliting data into train and test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "executionInfo": {
     "elapsed": 241,
     "status": "ok",
     "timestamp": 1738348379463,
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      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
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     "user_tz": -60
    },
    "id": "6IKmOkPelOE4"
   },
   "outputs": [],
   "source": [
    "X = df_text['Review']\n",
    "y = df_text['Class']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GIFxwrUglOE8"
   },
   "source": [
    "**Logistic regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "executionInfo": {
     "elapsed": 15046,
     "status": "ok",
     "timestamp": 1738348423967,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "CLOmxcdFlOE_"
   },
   "outputs": [],
   "source": [
    "model_log = imbpipeline(steps=[('vect', CountVectorizer(min_df=5, ngram_range=(1, 2))),\n",
    "                               ('tfidf', TfidfTransformer()),\n",
    "                               ('smote', SMOTE()),\n",
    "                               ('clf', LogisticRegression())])\n",
    "\n",
    "model_log.fit(X_train, y_train)\n",
    "\n",
    "ytest = np.array(y_test)\n",
    "pred_y = model_log.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 424,
     "status": "ok",
     "timestamp": 1738348469656,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "sLuSoVNGlOFC",
    "outputId": "62cdb6b3-3534-46f8-9920-8a3ac2d106b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.794435857805255\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.50      0.54      0.52       489\n",
      "     Neutral       0.40      0.43      0.42       628\n",
      "    Positive       0.92      0.90      0.91      3412\n",
      "\n",
      "    accuracy                           0.79      4529\n",
      "   macro avg       0.61      0.62      0.61      4529\n",
      "weighted avg       0.80      0.79      0.80      4529\n",
      "\n"
     ]
    }
   ],
   "source": [
    "log_acc = accuracy_score(pred_y, y_test)\n",
    "print('accuracy %s' % log_acc)\n",
    "print(classification_report(ytest, pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dbsGHi4DlOFE"
   },
   "source": [
    "**Random forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "executionInfo": {
     "elapsed": 57627,
     "status": "ok",
     "timestamp": 1738348657487,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "hwgF1XnblOFE"
   },
   "outputs": [],
   "source": [
    "model_rf = imbpipeline([('vect', CountVectorizer(min_df=5, ngram_range=(1,2))),\n",
    "                      ('tfidf', TfidfTransformer()),\n",
    "                      ('smote', SMOTE()),\n",
    "                      ('clf-rf', RandomForestClassifier(n_estimators=50))])\n",
    "\n",
    "model_rf.fit(X_train, y_train)\n",
    "\n",
    "pred = model_rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 504,
     "status": "ok",
     "timestamp": 1738348670110,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "rEY9V9g-lOFH",
    "outputId": "22bd543d-aac4-424e-e0eb-2ea117df74ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.788474276882314\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.59      0.31      0.41       489\n",
      "     Neutral       0.46      0.11      0.18       628\n",
      "    Positive       0.81      0.98      0.89      3412\n",
      "\n",
      "    accuracy                           0.79      4529\n",
      "   macro avg       0.62      0.47      0.49      4529\n",
      "weighted avg       0.74      0.79      0.74      4529\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rf_acc = accuracy_score(pred, y_test)\n",
    "print('accuracy %s' % rf_acc)\n",
    "print(classification_report(ytest, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lHJu9hivlOFJ"
   },
   "source": [
    "**Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "executionInfo": {
     "elapsed": 3058,
     "status": "ok",
     "timestamp": 1738348734251,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "senvAJ9alOFK"
   },
   "outputs": [],
   "source": [
    "model_nb = imbpipeline([('vect', CountVectorizer(min_df=5, ngram_range=(1,2))),\n",
    "                      ('tfidf', TfidfTransformer()),\n",
    "                      ('smote', SMOTE()),\n",
    "                      ('model',MultinomialNB()),\n",
    "                      ])\n",
    "\n",
    "model_nb.fit(X_train,y_train)\n",
    "\n",
    "preds = model_nb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 454,
     "status": "ok",
     "timestamp": 1738348745981,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "XdVU0eAalOFL",
    "outputId": "96b5a09b-99f2-47e9-890a-9841f33eed72"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.7683815411790682\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.46      0.59      0.52       489\n",
      "     Neutral       0.36      0.50      0.42       628\n",
      "    Positive       0.95      0.84      0.89      3412\n",
      "\n",
      "    accuracy                           0.77      4529\n",
      "   macro avg       0.59      0.64      0.61      4529\n",
      "weighted avg       0.81      0.77      0.79      4529\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nb_acc = accuracy_score(preds, y_test)\n",
    "print('accuracy %s' % nb_acc)\n",
    "print(classification_report(ytest, preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hzz9bZcWlOFM"
   },
   "source": [
    "**SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 5473,
     "status": "ok",
     "timestamp": 1738348797672,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "4G51Q2rflOFN"
   },
   "outputs": [],
   "source": [
    "model_svm = imbpipeline([('vect', CountVectorizer(min_df=5, ngram_range=(1, 2))),\n",
    "                       ('tfidf', TfidfTransformer()),\n",
    "                       ('smote', SMOTE()),\n",
    "                       ('svm',LinearSVC()),\n",
    "                       ])\n",
    "\n",
    "model_svm.fit(X_train, y_train)\n",
    "\n",
    "y_predict = model_svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 315,
     "status": "ok",
     "timestamp": 1738348828751,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "_kowTLQ-lOFO",
    "outputId": "baded3eb-6481-46f9-d13f-22e97ca917e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.7917862662839479\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.48      0.49      0.49       489\n",
      "     Neutral       0.41      0.37      0.39       628\n",
      "    Positive       0.90      0.91      0.91      3412\n",
      "\n",
      "    accuracy                           0.79      4529\n",
      "   macro avg       0.60      0.59      0.59      4529\n",
      "weighted avg       0.79      0.79      0.79      4529\n",
      "\n"
     ]
    }
   ],
   "source": [
    "svm_acc = accuracy_score(y_predict, y_test)\n",
    "print('accuracy %s' % svm_acc)\n",
    "print(classification_report(ytest, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D9vK75nYlOFP"
   },
   "source": [
    "**Ada Boosting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "executionInfo": {
     "elapsed": 32765,
     "status": "ok",
     "timestamp": 1738348900599,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "d6Zy-phalOFQ"
   },
   "outputs": [],
   "source": [
    "model_ab = imbpipeline([('vect', CountVectorizer(min_df=5, ngram_range=(1,2))),\n",
    "                       ('tfidf', TfidfTransformer()),\n",
    "                       ('smote', SMOTE()),\n",
    "                       ('ada', AdaBoostClassifier()),\n",
    "                       ])\n",
    "\n",
    "model_ab.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model_ab.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 269,
     "status": "ok",
     "timestamp": 1738348922057,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "kI5M8VL9lOFS",
    "outputId": "ae84c8f5-f3a5-47b3-943f-fd162e7314d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.677633031574299\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.32      0.33      0.33       489\n",
      "     Neutral       0.26      0.40      0.31       628\n",
      "    Positive       0.87      0.78      0.82      3412\n",
      "\n",
      "    accuracy                           0.68      4529\n",
      "   macro avg       0.48      0.50      0.49      4529\n",
      "weighted avg       0.73      0.68      0.70      4529\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ab_acc = accuracy_score(y_pred, y_test)\n",
    "print('accuracy %s' % ab_acc)\n",
    "print(classification_report(ytest, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CfP8yx86lOFT"
   },
   "source": [
    "### Best model\n",
    "\n",
    "I have tested five different models and check which one is the best:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 497,
     "status": "ok",
     "timestamp": 1738348931107,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "nzGBPSVHlOFV",
    "outputId": "470decf4-c3bf-48d9-c7fd-543f18b763aa"
   },
   "outputs": [
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       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
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       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-20f9866b-5c38-4dc5-82c7-9ac3b35a8516 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "                 Model     Score\n",
       "0  Logistic Regression  0.794436\n",
       "3                  SVM  0.791786\n",
       "1        Random Forest  0.788474\n",
       "2          Naive Bayes  0.768382\n",
       "4         Ada Boosting  0.677633"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "                      'Model': ['Logistic Regression', 'Random Forest', 'Naive Bayes', 'SVM', 'Ada Boosting'],\n",
    "                      'Score': [log_acc, rf_acc, nb_acc, svm_acc, ab_acc]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AeBkRkRSlOFW"
   },
   "source": [
    "## Summary\n",
    "\n",
    "This project was aimed to used sentiment analysis to determined product recommendation. In this case I have used a recommendation based on customers rating. I have started with assigned the values from the ranking to the positive, neutral and negative values. To resolve the imbalance problem in the data I have used SMOTE method. Finally I started training 5 different classification models. The best model was Logistic Regression algorithm which reached accuracy equal to 79 %."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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