Sentiment_vader.ipynb

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    "# Sentiment analysis of women's clothes reviews\n",
    "\n",
    "## Part 5: Sentiment analysis with Vader and TextBlob models.\n",
    "\n",
    "The aim of this project is sentiment analysis of women's clothes reviews based on their reviews as an input text. In this approach I have used a Vader and TextBlob methods to assigned the sentiment based on reviews and compare their relevance.\n",
    "\n",
    "Sentiment analysis is a text mining technique that could extract emotions of a given text. It is mainly based on identifying positive, neutral and negative words and return a sentiment score. This technique is usually used on reviews or social media texts.\n",
    "\n",
    "**VADER** (Valence Aware Dictionary for sEntiment Reasoning) is a kind of sentiment analysis based on the lexicon of words related to sentiment. In this method, each word is assessed whether it is positive, neutral or negative. In addition, the probability of assigning to each class is assessed.\n",
    "\n",
    "**TextBlob** is a python library that offers a simple API to access its methods to perform various NLP tasks. The sentiment function of textblob can assign polarity to words and estimate the sentiment of the whole text as an average. It returns two properties: polarity and subjectivity.\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."
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     "text": [
      "Requirement already satisfied: textblob in /usr/local/lib/python3.11/dist-packages (0.17.1)\n",
      "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.11/dist-packages (from textblob) (3.9.1)\n",
      "Requirement already satisfied: click in /usr/local/lib/python3.11/dist-packages (from nltk>=3.1->textblob) (8.1.8)\n",
      "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (from nltk>=3.1->textblob) (1.4.2)\n",
      "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.11/dist-packages (from nltk>=3.1->textblob) (2024.11.6)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from nltk>=3.1->textblob) (4.67.1)\n"
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   "source": [
    "!pip install textblob"
   ]
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   "source": [
    "## Import libriaries and data"
   ]
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import re\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from textblob import TextBlob\n",
    "\n",
    "from sklearn.metrics import accuracy_score,classification_report, confusion_matrix\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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Womens Clothing Reviews.csv', header=0,index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NiP_wLk6sn_H"
   },
   "source": [
    "## Data preparation\n",
    "\n",
    "Loading chosen variables: Rating, Recommended and Review Text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 359,
     "status": "ok",
     "timestamp": 1738349727360,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "OZ-xHzoqsn_J",
    "outputId": "1727771c-a9ac-42f3-ce98-3fa7e4916df1"
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   "outputs": [
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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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = df[['Rating', 'Recommended IND', 'Review Text']]\n",
    "text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W85oylrLsn_N"
   },
   "source": [
    "Rename of chosen variables:"
   ]
  },
  {
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   "execution_count": 8,
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    "executionInfo": {
     "elapsed": 432,
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    "id": "MEW5Hmcvsn_P",
    "outputId": "c5b8ff93-1b3a-43de-a0e4-f6694d031339"
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       "        const element = document.querySelector('#df-68f9b978-1371-4052-b5ff-4329e508e1b8');\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",
       "\n",
       "\n",
       "<div id=\"df-0d6a69ed-ee04-4da7-a3cc-758840a6deb3\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d6a69ed-ee04-4da7-a3cc-758840a6deb3')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
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       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </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",
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       "    20% {\n",
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       "      border-top-color: var(--fill-color);\n",
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       "    30% {\n",
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       "      border-top-color: var(--fill-color);\n",
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       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    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-0d6a69ed-ee04-4da7-a3cc-758840a6deb3 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..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = text.rename(columns={\"Review Text\": \"Review\", \"Recommended IND\":\"Recommended\"})\n",
    "text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JBMBQfnjsn_V"
   },
   "source": [
    "**First observations**\n",
    "\n",
    "Shape of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 429,
     "status": "ok",
     "timestamp": 1738349739672,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "TF8Xfjk6sn_X",
    "outputId": "c71a0c5f-b725-4e15-ded3-5eb232e599a2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23486, 3)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RXlYSG3psn_b"
   },
   "source": [
    "Checking the missing values in data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 178
    },
    "executionInfo": {
     "elapsed": 687,
     "status": "ok",
     "timestamp": 1738349741683,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "RQgY9qiisn_c",
    "outputId": "135a80fe-e21f-4c89-b79f-841dc4dc6561"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rating</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recommended</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Review</th>\n",
       "      <td>845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "Rating           0\n",
       "Recommended      0\n",
       "Review         845\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "14UHG0QQsn_d"
   },
   "source": [
    "Deleting missing values in Review variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 429,
     "status": "ok",
     "timestamp": 1738349746352,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "RsvfPYWUsn_f"
   },
   "outputs": [],
   "source": [
    "text.dropna(subset=['Review'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IOM_sezAsn_g"
   },
   "source": [
    "Change a type of Review variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 343,
     "status": "ok",
     "timestamp": 1738349753089,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "e8dG15z-sn_i"
   },
   "outputs": [],
   "source": [
    "text['Review'] = text['Review'].astype(str)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-X4GGzqUsn_k"
   },
   "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": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 291,
     "status": "ok",
     "timestamp": 1738349756121,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "33k8Q5hAsn_l"
   },
   "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": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 309,
     "status": "ok",
     "timestamp": 1738349762597,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "CtCeDWGvsn_n",
    "outputId": "95dee223-a810-4c65-b02c-40f3cfeef9cd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"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}",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "\n",
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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...   \n",
       "5       2            0  I love tracy reese dresses, but this one is no...   \n",
       "6       5            1  I aded this in my basket at hte last mintue to...   \n",
       "7       4            1  I ordered this in carbon for store pick up, an...   \n",
       "8       5            1  I love this dress. i usually get an xs but it ...   \n",
       "9       5            1  I'm 5\"5' and 125 lbs. i ordered the s petite t...   \n",
       "\n",
       "      Class  \n",
       "0  Positive  \n",
       "1  Positive  \n",
       "2   Neutral  \n",
       "3  Positive  \n",
       "4  Positive  \n",
       "5  Negative  \n",
       "6  Positive  \n",
       "7  Positive  \n",
       "8  Positive  \n",
       "9  Positive  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['Class'] = text['Rating'].apply(rating)\n",
    "text.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "executionInfo": {
     "elapsed": 333,
     "status": "ok",
     "timestamp": 1738349768764,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "u29nV9x3sn_o",
    "outputId": "5bfff664-6e4a-4108-accf-9770791eda12"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "  <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",
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       "    <tr>\n",
       "      <th>Negative</th>\n",
       "      <td>2370</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
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      "text/plain": [
       "Class\n",
       "Positive    17448\n",
       "Neutral      2823\n",
       "Negative     2370\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['Class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8PEwdWhbsn_q"
   },
   "source": [
    "## Vader sentiment\n",
    "\n",
    "In the first approach I teste **Vader** model. I create the sentiment classifier and function to determine the sentiment score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 307,
     "status": "ok",
     "timestamp": 1738349829015,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "27obAPaPuODY",
    "outputId": "a6d942c5-cc31-478c-fd2f-9d934b27886b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 298,
     "status": "ok",
     "timestamp": 1738349832411,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "bksCBrkMsn_r"
   },
   "outputs": [],
   "source": [
    "analyser = SentimentIntensityAnalyzer()\n",
    "\n",
    "# function to determine the sentiment score\n",
    "def sentiment_scores(text):\n",
    "    score = analyser.polarity_scores(text)\n",
    "    print(\"{:-<40} {}\".format(text, str(score)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 354,
     "status": "ok",
     "timestamp": 1738349837528,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "huv6tGQXsn_t",
    "outputId": "068e3944-e9fa-4e1a-e770-4cae07ece6dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I had such high hopes for this dress and really wanted it to work for me. i initially ordered the petite small (my usual size) but i found this to be outrageously small. so small in fact that i could not zip it up! i reordered it in petite medium, which was just ok. overall, the top half was comfortable and fit nicely, but the bottom half had a very tight under layer and several somewhat cheap (net) over layers. imo, a major design flaw was the net over layer sewn directly into the zipper - it c {'neg': 0.027, 'neu': 0.792, 'pos': 0.181, 'compound': 0.9427}\n"
     ]
    }
   ],
   "source": [
    "sentiment_scores(text['Review'].iloc[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yDWj95ptsn_u"
   },
   "source": [
    "Positive, negative and neutral results represent the proportion of the text that falls in these categories. This means that the opinion was rated as 79% neutral, 18% positive and 0.3% negative. All results should add up to 1. The Compound score is a metric that calculates the sum of all lexicon scores that have been normalized between -1 (most extreme negative) and +1 (most extreme positive). Compound is 0.94, which means very high positive sentiment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qX_iIcm-sn_w"
   },
   "source": [
    "Now I add score to the all reviews:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 18547,
     "status": "ok",
     "timestamp": 1738349868479,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "CfvDPcnysn_x",
    "outputId": "b2907b7a-aa58-407a-8ef5-49ab87fed314"
   },
   "outputs": [
    {
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       "        document.querySelector('#df-12c0a5a9-ad42-42a2-97b2-dc668ecc961f 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-12c0a5a9-ad42-42a2-97b2-dc668ecc961f');\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",
       "\n",
       "\n",
       "<div id=\"df-1d31f61f-0100-4864-8884-e7b1005190fb\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1d31f61f-0100-4864-8884-e7b1005190fb')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </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",
       "    }\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-1d31f61f-0100-4864-8884-e7b1005190fb 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                                             Scores  \n",
       "0  Positive  {'neg': 0.0, 'neu': 0.272, 'pos': 0.728, 'comp...  \n",
       "1  Positive  {'neg': 0.0, 'neu': 0.664, 'pos': 0.336, 'comp...  \n",
       "2   Neutral  {'neg': 0.027, 'neu': 0.792, 'pos': 0.181, 'co...  \n",
       "3  Positive  {'neg': 0.226, 'neu': 0.34, 'pos': 0.434, 'com...  \n",
       "4  Positive  {'neg': 0.0, 'neu': 0.7, 'pos': 0.3, 'compound...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['Scores'] = text['Review'].apply(lambda Review: analyser.polarity_scores(Review))\n",
    "text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Yl1i3T4sn_z"
   },
   "source": [
    "Then I calculate \"Compound\" to all sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 550,
     "status": "ok",
     "timestamp": 1738349884396,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "DmKgUuPIsn_0",
    "outputId": "94178cc3-6282-43a4-fbd5-5a9241201ce5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"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\": \"Scores\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Compound\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37165012481338233,\n        \"min\": -0.9562,\n        \"max\": 0.9959,\n        \"num_unique_values\": 5296,\n        \"samples\": [\n          -0.9227,\n          0.9879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "text"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-b11dbe51-339b-41bd-ab25-4c30affde08f\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.664, 'pos': 0.336, 'comp...</td>\n",
       "      <td>0.9729</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>I had such high hopes for this dress and reall...</td>\n",
       "      <td>Neutral</td>\n",
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       "      <td>0.9427</td>\n",
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       "      <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",
       "      <td>{'neg': 0.226, 'neu': 0.34, 'pos': 0.434, 'com...</td>\n",
       "      <td>0.5727</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>This shirt is very flattering to all due to th...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.7, 'pos': 0.3, 'compound...</td>\n",
       "      <td>0.9291</td>\n",
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       "  </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-b11dbe51-339b-41bd-ab25-4c30affde08f')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
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       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\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",
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       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-b11dbe51-339b-41bd-ab25-4c30affde08f 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-b11dbe51-339b-41bd-ab25-4c30affde08f');\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",
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       "\n",
       "\n",
       "<div id=\"df-3637e2f3-fb55-4ed5-9b56-10c839c7b93d\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3637e2f3-fb55-4ed5-9b56-10c839c7b93d')\"\n",
       "            title=\"Suggest charts\"\n",
       "            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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       "    </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",
       "    }\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-3637e2f3-fb55-4ed5-9b56-10c839c7b93d 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                                             Scores  Compound  \n",
       "0  Positive  {'neg': 0.0, 'neu': 0.272, 'pos': 0.728, 'comp...    0.8932  \n",
       "1  Positive  {'neg': 0.0, 'neu': 0.664, 'pos': 0.336, 'comp...    0.9729  \n",
       "2   Neutral  {'neg': 0.027, 'neu': 0.792, 'pos': 0.181, 'co...    0.9427  \n",
       "3  Positive  {'neg': 0.226, 'neu': 0.34, 'pos': 0.434, 'com...    0.5727  \n",
       "4  Positive  {'neg': 0.0, 'neu': 0.7, 'pos': 0.3, 'compound...    0.9291  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['Compound'] = text['Scores'].apply(lambda score_dict: score_dict['compound'])\n",
    "text.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zUdULyNFsn_6"
   },
   "source": [
    "Each review has a negative, neutral, positive and complex result. The complex result is a comprehensive assessment of the first three points and this score is in the range of -1 to 1. Based on these results one can determine the sentiment. One can set score threshold ourselves and here I set it to ± 0.5. If Compound is bigger than 0.5 the review is positive, from 0 to 0.5 is neutral and below than 0 is negative.\n",
    "\n",
    "Function to determine the sentiment for all sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1738349893267,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "VGphYa9tsn_8"
   },
   "outputs": [],
   "source": [
    "def sentiment (score):\n",
    "    if score >= 0.5:\n",
    "        return 'Positive'\n",
    "    if (score > 0) and (score < 0.5):\n",
    "        return 'Neutral'\n",
    "    if score <= 0:\n",
    "        return 'Negative'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ha8M8CQGsn_9"
   },
   "source": [
    "I add \"Sentiment\" column to all sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 378,
     "status": "ok",
     "timestamp": 1738349903327,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "qhxPt4v0soAK",
    "outputId": "b4b50447-67c0-46ba-cc70-2a3a8e828e0e"
   },
   "outputs": [
    {
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       "summary": "{\n  \"name\": \"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\": \"Scores\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Compound\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37165012481338233,\n        \"min\": -0.9562,\n        \"max\": 0.9959,\n        \"num_unique_values\": 5296,\n        \"samples\": [\n          -0.9227,\n          0.9879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "    animation:\n",
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       "  }\n",
       "\n",
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       "    0% {\n",
       "      border-color: transparent;\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",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
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       "\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",
       "5       2            0  I love tracy reese dresses, but this one is no...   \n",
       "6       5            1  I aded this in my basket at hte last mintue to...   \n",
       "7       4            1  I ordered this in carbon for store pick up, an...   \n",
       "8       5            1  I love this dress. i usually get an xs but it ...   \n",
       "9       5            1  I'm 5\"5' and 125 lbs. i ordered the s petite t...   \n",
       "\n",
       "      Class                                             Scores  Compound  \\\n",
       "0  Positive  {'neg': 0.0, 'neu': 0.272, 'pos': 0.728, 'comp...    0.8932   \n",
       "1  Positive  {'neg': 0.0, 'neu': 0.664, 'pos': 0.336, 'comp...    0.9729   \n",
       "2   Neutral  {'neg': 0.027, 'neu': 0.792, 'pos': 0.181, 'co...    0.9427   \n",
       "3  Positive  {'neg': 0.226, 'neu': 0.34, 'pos': 0.434, 'com...    0.5727   \n",
       "4  Positive  {'neg': 0.0, 'neu': 0.7, 'pos': 0.3, 'compound...    0.9291   \n",
       "5  Negative  {'neg': 0.0, 'neu': 0.853, 'pos': 0.147, 'comp...    0.9419   \n",
       "6  Positive  {'neg': 0.023, 'neu': 0.881, 'pos': 0.096, 'co...    0.8004   \n",
       "7  Positive  {'neg': 0.046, 'neu': 0.901, 'pos': 0.053, 'co...   -0.0909   \n",
       "8  Positive  {'neg': 0.0, 'neu': 0.818, 'pos': 0.182, 'comp...    0.7175   \n",
       "9  Positive  {'neg': 0.12, 'neu': 0.753, 'pos': 0.126, 'com...   -0.3724   \n",
       "\n",
       "  Sentiment  \n",
       "0  Positive  \n",
       "1  Positive  \n",
       "2  Positive  \n",
       "3  Positive  \n",
       "4  Positive  \n",
       "5  Positive  \n",
       "6  Positive  \n",
       "7  Negative  \n",
       "8  Positive  \n",
       "9  Negative  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['Sentiment'] = text['Compound'].apply(sentiment)\n",
    "text.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DkkwiVfbsoAM"
   },
   "source": [
    "How sentiments are distributed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "executionInfo": {
     "elapsed": 454,
     "status": "ok",
     "timestamp": 1738349911146,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "7oYCcq8lsoAN",
    "outputId": "b6cfc66f-4d2e-47bc-be4a-4c8086673263"
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>Sentiment</th>\n",
       "      <th></th>\n",
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       "      <th>Positive</th>\n",
       "      <td>19126</td>\n",
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       "      <td>1941</td>\n",
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       "      <th>Negative</th>\n",
       "      <td>1574</td>\n",
       "    </tr>\n",
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       "</div><br><label><b>dtype:</b> int64</label>"
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      "text/plain": [
       "Sentiment\n",
       "Positive    19126\n",
       "Neutral      1941\n",
       "Negative     1574\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yMkad4gMsoAO"
   },
   "source": [
    "One can see that the Positive sentiment is much more than others (Neutral and Negative)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_l4Djbg1soAP"
   },
   "source": [
    "### Assessment of received sentiment\n",
    "\n",
    "In this part I compare received sentiment by The Vader method with those that I originally assigned by ranking (Class).\n",
    "\n",
    "Calculating accuracy score:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 351,
     "status": "ok",
     "timestamp": 1738349935488,
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    },
    "id": "iMqldHNosoAQ",
    "outputId": "3e58d60b-c101-48c7-f3a1-07064ef6f9fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.762024645554525"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(text['Class'],text['Sentiment'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g9YxMBAqsoAR"
   },
   "source": [
    "Evaluation scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 548,
     "status": "ok",
     "timestamp": 1738349939341,
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      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
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    },
    "id": "0NhuMiwEsoAT",
    "outputId": "dbe23d66-08ca-44fc-b997-d50adc9d4021"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.43      0.28      0.34      2370\n",
      "     Neutral       0.27      0.18      0.22      2823\n",
      "    Positive       0.84      0.92      0.88     17448\n",
      "\n",
      "    accuracy                           0.76     22641\n",
      "   macro avg       0.51      0.46      0.48     22641\n",
      "weighted avg       0.73      0.76      0.74     22641\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(text['Class'],text['Sentiment']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1738349943058,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "2K8qa09usoAU",
    "outputId": "c8956244-df75-49b1-8ac7-81a00b2582e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  674   493  1203]\n",
      " [  441   519  1863]\n",
      " [  459   929 16060]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(text['Class'],text['Sentiment']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ds61YD-qsoAX"
   },
   "source": [
    "Based on the above report one can see that the fit has achieved 76% accuracy. The received results show different sentiment distribution. The Vader method assigned more positive reviews than original rating so the amount of neutral and negative reviews reduced. There are also differences in the designation of individual sentiments, for example one of them was marked as positive, where it was negative in the original ranking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QXMbWQ2YsoAY"
   },
   "source": [
    "**Results after cleaning data**\n",
    "\n",
    "I try to make a better results by cleaning data. I check how the results look like after remove punctuation and Stopwords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 401,
     "status": "ok",
     "timestamp": 1738350311255,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "NR5TlAYjvn1u",
    "outputId": "1398b01f-c4af-48b2-f332-cd3004a684db"
   },
   "outputs": [
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       "        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-6658e56e-054e-48b4-94ff-c94bcaf6e9f1 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": [
       "      Class                                             Review\n",
       "0  Positive  Absolutely wonderful - silky and sexy and comf...\n",
       "1  Positive  Love this dress!  it's sooo pretty.  i happene...\n",
       "2   Neutral  I had such high hopes for this dress and reall...\n",
       "3  Positive  I love, love, love this jumpsuit. it's fun, fl...\n",
       "4  Positive  This shirt is very flattering to all due to th..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = text[['Class', 'Review']]\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5g1T9YnJsoAa"
   },
   "source": [
    "Stopwords:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 353,
     "status": "ok",
     "timestamp": 1738349962052,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "ocKGYJ-Pu00r",
    "outputId": "1d6126ff-5b23-49fa-e921-d01aa58c4089"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 329,
     "status": "ok",
     "timestamp": 1738349964665,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "zBDmq1e8soAb",
    "outputId": "64bae1d3-faf3-4a49-e091-1e2947894386"
   },
   "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": [
    "# Show some stop words\n",
    "stop_words = stopwords.words('english')\n",
    "print(stop_words[::10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9pB47VXlsoAc"
   },
   "source": [
    "Remove punctuation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "executionInfo": {
     "elapsed": 306,
     "status": "ok",
     "timestamp": 1738350318505,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "Urj86vjnsoAd"
   },
   "outputs": [],
   "source": [
    "def clean_text(words):\n",
    "    words = re.sub(\"[^a-zA-Z]\",\" \", words)\n",
    "    text = words.lower().split()\n",
    "    return \" \".join(text)\n",
    "\n",
    "def remove_stopwords(rows):\n",
    "    rows = [word.lower() for word in rows.split() if word.lower() not in stop_words]\n",
    "    return \" \".join(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "executionInfo": {
     "elapsed": 3497,
     "status": "ok",
     "timestamp": 1738350332798,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "7OSoa2aKsoAe"
   },
   "outputs": [],
   "source": [
    "reviews['Review'] = reviews['Review'].apply(clean_text)\n",
    "reviews['Review'] = reviews['Review'].apply(remove_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 434,
     "status": "ok",
     "timestamp": 1738350336211,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "vteAZzYRsoAj",
    "outputId": "91955eef-3c82-4b23-82d8-939d4be6e116"
   },
   "outputs": [
    {
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       "variable_name": "reviews"
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       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "        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",
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       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
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       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "\n",
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      ],
      "text/plain": [
       "      Class                                             Review\n",
       "0  Positive        absolutely wonderful silky sexy comfortable\n",
       "1  Positive  love dress sooo pretty happened find store gla...\n",
       "2   Neutral  high hopes dress really wanted work initially ...\n",
       "3  Positive  love love love jumpsuit fun flirty fabulous ev...\n",
       "4  Positive  shirt flattering due adjustable front tie perf..."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1EpHJOKwsoAk"
   },
   "source": [
    "Now I use the Vader sentiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 10476,
     "status": "ok",
     "timestamp": 1738350357401,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "v8N0LDS9soAl",
    "outputId": "526e45b7-0547-4606-87f8-9fecbdc80581"
   },
   "outputs": [
    {
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       "summary": "{\n  \"name\": \"reviews\",\n  \"rows\": 22641,\n  \"fields\": [\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Neutral\",\n          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22631,\n        \"samples\": [\n          \"top really pretty nice quality runs big went size perfect coloring subtle person photo\",\n          \"excited splurge mara hoffman suit sorely disappointed received suit mail petite fairly curvy knew would likely little long torso whole suit oddly shaped imagine anyone would fit square shape halter pieces awkward pieces supposed hold chest super wide loose\",\n          \"lot reviewers saying dress simply busy wearable right love cut fit dress love style awesome stylist retailer told take belt try ribbon like one charisma sheath much cute completely changed look dress made look classy belt looks cheap person little hoochie mamma wear dress\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Scores\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neutral</td>\n",
       "      <td>high hopes dress really wanted work initially ...</td>\n",
       "      <td>{'neg': 0.038, 'neu': 0.693, 'pos': 0.269, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Positive</td>\n",
       "      <td>love love love jumpsuit fun flirty fabulous ev...</td>\n",
       "      <td>{'neg': 0.171, 'neu': 0.185, 'pos': 0.644, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Positive</td>\n",
       "      <td>shirt flattering due adjustable front tie perf...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.494, 'pos': 0.506, 'comp...</td>\n",
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       "\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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       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
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       "  .colab-df-quickchart:hover {\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "\n",
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       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
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       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
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       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-958fbd1f-cb2b-4cf0-b717-92b5b162ce66 button');\n",
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       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      Class                                             Review  \\\n",
       "0  Positive        absolutely wonderful silky sexy comfortable   \n",
       "1  Positive  love dress sooo pretty happened find store gla...   \n",
       "2   Neutral  high hopes dress really wanted work initially ...   \n",
       "3  Positive  love love love jumpsuit fun flirty fabulous ev...   \n",
       "4  Positive  shirt flattering due adjustable front tie perf...   \n",
       "\n",
       "                                              Scores  \n",
       "0  {'neg': 0.0, 'neu': 0.154, 'pos': 0.846, 'comp...  \n",
       "1  {'neg': 0.0, 'neu': 0.492, 'pos': 0.508, 'comp...  \n",
       "2  {'neg': 0.038, 'neu': 0.693, 'pos': 0.269, 'co...  \n",
       "3  {'neg': 0.171, 'neu': 0.185, 'pos': 0.644, 'co...  \n",
       "4  {'neg': 0.0, 'neu': 0.494, 'pos': 0.506, 'comp...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews['Scores'] = reviews['Review'].apply(lambda Review: analyser.polarity_scores(Review))\n",
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 373,
     "status": "ok",
     "timestamp": 1738350369450,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "SoyfBYAMsoAn",
    "outputId": "95cf1602-76cf-4ad3-b4c3-14b6120eb3a6"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"reviews\",\n  \"rows\": 22641,\n  \"fields\": [\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Neutral\",\n          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22631,\n        \"samples\": [\n          \"top really pretty nice quality runs big went size perfect coloring subtle person photo\",\n          \"excited splurge mara hoffman suit sorely disappointed received suit mail petite fairly curvy knew would likely little long torso whole suit oddly shaped imagine anyone would fit square shape halter pieces awkward pieces supposed hold chest super wide loose\",\n          \"lot reviewers saying dress simply busy wearable right love cut fit dress love style awesome stylist retailer told take belt try ribbon like one charisma sheath much cute completely changed look dress made look classy belt looks cheap person little hoochie mamma wear dress\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Scores\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Compound\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2716942052554215,\n        \"min\": -0.9361,\n        \"max\": 0.9958,\n        \"num_unique_values\": 2283,\n        \"samples\": [\n          0.7447,\n          0.7712,\n          0.8935\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Neutral\",\n          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "reviews"
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       "      <td>love tracy reese dresses one petite feet tall ...</td>\n",
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       "      <td>Positive</td>\n",
       "      <td>love dress usually get xs runs little snug bus...</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.698, 'pos': 0.302, 'comp...</td>\n",
       "      <td>0.7579</td>\n",
       "      <td>Positive</td>\n",
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       "\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",
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       "\n",
       "\n",
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       "\n",
       "  .colab-df-quickchart {\n",
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       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "    fill: var(--button-hover-fill-color);\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "    background-color: var(--disabled-bg-color);\n",
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       "\n",
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       "    animation:\n",
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       "    async function quickchart(key) {\n",
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       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "      Class                                             Review  \\\n",
       "0  Positive        absolutely wonderful silky sexy comfortable   \n",
       "1  Positive  love dress sooo pretty happened find store gla...   \n",
       "2   Neutral  high hopes dress really wanted work initially ...   \n",
       "3  Positive  love love love jumpsuit fun flirty fabulous ev...   \n",
       "4  Positive  shirt flattering due adjustable front tie perf...   \n",
       "5  Negative  love tracy reese dresses one petite feet tall ...   \n",
       "6  Positive  aded basket hte last mintue see would look lik...   \n",
       "7  Positive  ordered carbon store pick ton stuff always try...   \n",
       "8  Positive  love dress usually get xs runs little snug bus...   \n",
       "9  Positive  lbs ordered petite make sure length long typic...   \n",
       "\n",
       "                                              Scores  Compound Sentiment  \n",
       "0  {'neg': 0.0, 'neu': 0.154, 'pos': 0.846, 'comp...    0.8991  Positive  \n",
       "1  {'neg': 0.0, 'neu': 0.492, 'pos': 0.508, 'comp...    0.9710  Positive  \n",
       "2  {'neg': 0.038, 'neu': 0.693, 'pos': 0.269, 'co...    0.9062  Positive  \n",
       "3  {'neg': 0.171, 'neu': 0.185, 'pos': 0.644, 'co...    0.9464  Positive  \n",
       "4  {'neg': 0.0, 'neu': 0.494, 'pos': 0.506, 'comp...    0.9062  Positive  \n",
       "5  {'neg': 0.0, 'neu': 0.732, 'pos': 0.268, 'comp...    0.9153  Positive  \n",
       "6  {'neg': 0.048, 'neu': 0.827, 'pos': 0.125, 'co...    0.6361  Positive  \n",
       "7  {'neg': 0.042, 'neu': 0.832, 'pos': 0.126, 'co...    0.5709  Positive  \n",
       "8  {'neg': 0.0, 'neu': 0.698, 'pos': 0.302, 'comp...    0.7579  Positive  \n",
       "9  {'neg': 0.0, 'neu': 0.564, 'pos': 0.436, 'comp...    0.9643  Positive  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews['Compound'] = reviews['Scores'].apply(lambda score_dict: score_dict['compound'])\n",
    "reviews['Sentiment'] = reviews['Compound'].apply(sentiment)\n",
    "reviews.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FNiMO8bxsoAo"
   },
   "source": [
    "Checking the distribution of variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "executionInfo": {
     "elapsed": 383,
     "status": "ok",
     "timestamp": 1738350376235,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
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    "outputId": "ff41c7b8-128c-4c0c-bb4f-4a4e528c9e4a",
    "scrolled": true
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   "outputs": [
    {
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       "Sentiment\n",
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    "reviews['Sentiment'].value_counts()"
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  {
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   "execution_count": 43,
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    "id": "j3FPlA0-soAq",
    "outputId": "034d7cfa-ff2a-40d1-e41b-ddf1bacd4dbd"
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       "    fill: var(--disabled-fill-color);\n",
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       "\n",
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       "\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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       "\n",
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       "    async function quickchart(key) {\n",
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       "      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",
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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": [
       "        Class                                             Review  \\\n",
       "77   Negative  zipper broke piece first time wore disappointi...   \n",
       "104  Negative  runs big looked unflattering petite might work...   \n",
       "110  Negative  thin poor quality especially price felt like t...   \n",
       "191  Negative  minimally torn whether return ultimately going...   \n",
       "214  Negative  skirt received little blue green mostly white ...   \n",
       "\n",
       "                                                Scores  Compound Sentiment  \n",
       "77   {'neg': 0.295, 'neu': 0.475, 'pos': 0.231, 'co...   -0.2263  Negative  \n",
       "104  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000  Negative  \n",
       "110  {'neg': 0.25, 'neu': 0.565, 'pos': 0.184, 'com...   -0.3892  Negative  \n",
       "191  {'neg': 0.184, 'neu': 0.816, 'pos': 0.0, 'comp...   -0.5456  Negative  \n",
       "214  {'neg': 0.225, 'neu': 0.602, 'pos': 0.173, 'co...   -0.3182  Negative  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.loc[reviews['Sentiment'] == 'Negative'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 441,
     "status": "ok",
     "timestamp": 1738350390298,
     "user": {
      "displayName": "Anna Staśkiewicz",
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    },
    "id": "8sMTbN34soAs",
    "outputId": "b4e3281a-977c-4c91-a7ba-25f81d95471e"
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   "outputs": [
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       "      border-right-color: var(--fill-color);\n",
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       "\n",
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       "    async function quickchart(key) {\n",
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       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
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       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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       "    (() => {\n",
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       "      quickchartButtonEl.style.display =\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "        Class                                             Review  \\\n",
       "10    Neutral  dress runs small esp zipper area runs ordered ...   \n",
       "22   Negative  first pullover styling side zipper purchased k...   \n",
       "32   Positive  pants even better person downside need dry cle...   \n",
       "103  Negative  fabric felt cheap find flattering top referenc...   \n",
       "116  Positive  prior reviewer nailed summary dress definitely...   \n",
       "\n",
       "                                                Scores  Compound Sentiment  \n",
       "10   {'neg': 0.084, 'neu': 0.782, 'pos': 0.135, 'co...    0.2263   Neutral  \n",
       "22   {'neg': 0.112, 'neu': 0.765, 'pos': 0.122, 'co...    0.1027   Neutral  \n",
       "32   {'neg': 0.183, 'neu': 0.55, 'pos': 0.266, 'com...    0.2263   Neutral  \n",
       "103  {'neg': 0.0, 'neu': 0.687, 'pos': 0.313, 'comp...    0.4767   Neutral  \n",
       "116  {'neg': 0.0, 'neu': 0.828, 'pos': 0.172, 'comp...    0.4019   Neutral  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.loc[reviews['Sentiment'] == 'Neutral'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xQytgxy1soAu"
   },
   "source": [
    "Comparing with ranking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 368,
     "status": "ok",
     "timestamp": 1738350399412,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "bkxwBqW-soAv",
    "outputId": "eff618f5-1502-4f47-ba78-bea357bd45f5"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7609204540435494"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(reviews['Class'],reviews['Sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1020,
     "status": "ok",
     "timestamp": 1738350404951,
     "user": {
      "displayName": "Anna Staśkiewicz",
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    "id": "mZgmgVetsoAw",
    "outputId": "4b34e3c2-3876-433f-dfd7-cb786f9163d2"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.48      0.15      0.23      2370\n",
      "     Neutral       0.23      0.12      0.16      2823\n",
      "    Positive       0.81      0.95      0.87     17448\n",
      "\n",
      "    accuracy                           0.76     22641\n",
      "   macro avg       0.51      0.41      0.42     22641\n",
      "weighted avg       0.70      0.76      0.72     22641\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(reviews['Class'],reviews['Sentiment']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ibu5HCvIsoAx"
   },
   "source": [
    "After I cleaned the data received the 76% accuracy. The result does not change much in comparison to previous model and slightly worsened. In this case I received even more assigned positive reviews in relation to the original ranking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nZynnPswsoAz"
   },
   "source": [
    "## TextBlob sentiment\n",
    "\n",
    "In the second approach I using TextBlob model. The sentiment function of textblob returns two properties, polarity, and subjectivity.\n",
    "\n",
    "**Polarity** is the emotion expressed in the sentence. It can be positive, neagtive and neutral.The polarity score is a float within the range [-1.0, 1.0]  where 1 means positive statement and -1 means a negative statement. Subjective sentences generally refer to personal opinion or emotion whereas objective refers to factual information.\n",
    "\n",
    "**Subjectivity** is also a float which lies in the range of [0,1]."
   ]
  },
  {
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    "executionInfo": {
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     "timestamp": 1738350541344,
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      "displayName": "Anna Staśkiewicz",
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    },
    "id": "pXxicISsxBez",
    "outputId": "775704a3-7560-4c59-ed78-a27b144cb2c4"
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       "summary": "{\n  \"name\": \"data\",\n  \"rows\": 22641,\n  \"fields\": [\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          \"Got so many complements! really comfy but runs big! i'm usually a large and i wish i had gotten the small.\"\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          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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      "text/plain": [
       "                                              Review     Class\n",
       "0  Absolutely wonderful - silky and sexy and comf...  Positive\n",
       "1  Love this dress!  it's sooo pretty.  i happene...  Positive\n",
       "2  I had such high hopes for this dress and reall...   Neutral\n",
       "3  I love, love, love this jumpsuit. it's fun, fl...  Positive\n",
       "4  This shirt is very flattering to all due to th...  Positive"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = text[['Review', 'Class']]\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 347,
     "status": "ok",
     "timestamp": 1738350557236,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
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    },
    "id": "2ZuxX1BExBva",
    "outputId": "c5d95780-b99d-4774-b15d-743b7d14ce04"
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   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Absolutely wonderful - silky and sexy and comfortable'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blob_text = data['Review'][0]\n",
    "blob_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 346,
     "status": "ok",
     "timestamp": 1738350560838,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "bH3cokQIsoBi",
    "outputId": "3d7f8724-4bdd-4caf-842a-74bedf35a35b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentiment(polarity=0.6333333333333333, subjectivity=0.9333333333333332)\n"
     ]
    }
   ],
   "source": [
    "blob = TextBlob(blob_text)\n",
    "print(format(blob.sentiment))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "olEhd8TtsoBk"
   },
   "source": [
    "The above result is a tuple representing the polarization and subjectivity of review. The polarity of the sentence is 0.63, indicating that the sentiment is positive. The subjectivity of the text which is 0.93 in our example. The value closer to 1 indicates that the sentence is mostly a public opinion and not a factual piece of information and vice versa.\n",
    "\n",
    "For this case I only extract the polarity and apply it to all the observations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
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    },
    "executionInfo": {
     "elapsed": 15542,
     "status": "ok",
     "timestamp": 1738350590883,
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      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
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    },
    "id": "A9xpj1yAsoBl",
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       "      <td>Love this dress!  it's sooo pretty.  i happene...</td>\n",
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       "      <td>This shirt is very flattering to all due to th...</td>\n",
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       "      <td>I love tracy reese dresses, but this one is no...</td>\n",
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       "      <td>0.178750</td>\n",
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       "      <td>I aded this in my basket at hte last mintue to...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.133750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I ordered this in carbon for store pick up, an...</td>\n",
       "      <td>Positive</td>\n",
       "      <td>0.171635</td>\n",
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      ],
      "text/plain": [
       "                                              Review     Class  Polarity\n",
       "0  Absolutely wonderful - silky and sexy and comf...  Positive  0.633333\n",
       "1  Love this dress!  it's sooo pretty.  i happene...  Positive  0.339583\n",
       "2  I had such high hopes for this dress and reall...   Neutral  0.073675\n",
       "3  I love, love, love this jumpsuit. it's fun, fl...  Positive  0.550000\n",
       "4  This shirt is very flattering to all due to th...  Positive  0.512891\n",
       "5  I love tracy reese dresses, but this one is no...  Negative  0.178750\n",
       "6  I aded this in my basket at hte last mintue to...  Positive  0.133750\n",
       "7  I ordered this in carbon for store pick up, an...  Positive  0.171635\n",
       "8  I love this dress. i usually get an xs but it ...  Positive  0.002500\n",
       "9  I'm 5\"5' and 125 lbs. i ordered the s petite t...  Positive  0.204200"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Polarity'] = data['Review'].apply(lambda x: TextBlob(x).sentiment[0])\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UrpMNW6_soBm"
   },
   "source": [
    "Function to assign sentiment to all sentences:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "executionInfo": {
     "elapsed": 349,
     "status": "ok",
     "timestamp": 1738350601593,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "6DvrL8PvsoBn"
   },
   "outputs": [],
   "source": [
    "def sentiment_score(polarity):\n",
    "    if polarity < 0:\n",
    "        return \"Negative\"\n",
    "    elif polarity > 0:\n",
    "        return \"Positive\"\n",
    "    else:\n",
    "        return \"Neutral\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "executionInfo": {
     "elapsed": 357,
     "status": "ok",
     "timestamp": 1738350606452,
     "user": {
      "displayName": "Anna Staśkiewicz",
      "userId": "13490438346564972102"
     },
     "user_tz": -60
    },
    "id": "bk3korMQsoBo",
    "outputId": "3b4ea030-e30c-4b7b-e93a-9bd261193cad"
   },
   "outputs": [
    {
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       "summary": "{\n  \"name\": \"data\",\n  \"rows\": 22641,\n  \"fields\": [\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          \"Got so many complements! really comfy but runs big! i'm usually a large and i wish i had gotten the small.\"\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          \"Negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Polarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1766794488009754,\n        \"min\": -0.9750000000000001,\n        \"max\": 1.0,\n        \"num_unique_values\": 14239,\n        \"samples\": [\n          0.38375,\n          0.3017573696145125,\n          0.7791666666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Positive\",\n          \"Negative\",\n          \"Neutral\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "            title=\"Suggest charts\"\n",
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       "\n",
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       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
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       "      --disabled-fill-color: #AAA;\n",
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       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "      --hover-bg-color: #434B5C;\n",
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       "      --disabled-fill-color: #666;\n",
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       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
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       "\n",
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       "    background-color: var(--hover-bg-color);\n",
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       "    fill: var(--button-hover-fill-color);\n",
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       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
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       "  .colab-df-spinner {\n",
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       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
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       "\n",
       "  @keyframes spin {\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "\n",
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       "        document.querySelector('#' + key + ' button');\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "                                              Review     Class  Polarity  \\\n",
       "0  Absolutely wonderful - silky and sexy and comf...  Positive  0.633333   \n",
       "1  Love this dress!  it's sooo pretty.  i happene...  Positive  0.339583   \n",
       "2  I had such high hopes for this dress and reall...   Neutral  0.073675   \n",
       "3  I love, love, love this jumpsuit. it's fun, fl...  Positive  0.550000   \n",
       "4  This shirt is very flattering to all due to th...  Positive  0.512891   \n",
       "5  I love tracy reese dresses, but this one is no...  Negative  0.178750   \n",
       "6  I aded this in my basket at hte last mintue to...  Positive  0.133750   \n",
       "7  I ordered this in carbon for store pick up, an...  Positive  0.171635   \n",
       "8  I love this dress. i usually get an xs but it ...  Positive  0.002500   \n",
       "9  I'm 5\"5' and 125 lbs. i ordered the s petite t...  Positive  0.204200   \n",
       "\n",
       "  Sentiment  \n",
       "0  Positive  \n",
       "1  Positive  \n",
       "2  Positive  \n",
       "3  Positive  \n",
       "4  Positive  \n",
       "5  Positive  \n",
       "6  Positive  \n",
       "7  Positive  \n",
       "8  Positive  \n",
       "9  Positive  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Sentiment'] = data['Polarity'].apply(sentiment_score)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JI0Xn0iEsoBp"
   },
   "source": [
    "Distributed of sentiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "executionInfo": {
     "elapsed": 3626,
     "status": "ok",
     "timestamp": 1738350614785,
     "user": {
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    "id": "TnpDKJcjsoBq",
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    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
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       "Sentiment\n",
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       "Negative     1322\n",
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    "Examples of results:"
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    "id": "_eTCE7QSsoBt",
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       "                                               Review     Class  Polarity  \\\n",
       "10  Dress runs small esp where the zipper area run...   Neutral -0.097149   \n",
       "14  This is a nice choice for holiday gatherings. ...   Neutral -0.057143   \n",
       "22  First of all, this is not pullover styling. th...  Negative -0.045595   \n",
       "44  Tried this on today at my local retailer and h...  Positive -0.046374   \n",
       "72  I have a short torso and this works well for m...  Positive -0.079365   \n",
       "\n",
       "   Sentiment  \n",
       "10  Negative  \n",
       "14  Negative  \n",
       "22  Negative  \n",
       "44  Negative  \n",
       "72  Negative  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
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    }
   ],
   "source": [
    "data.loc[data['Sentiment'] == 'Negative'].head()"
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       "                                                Review     Class  Polarity  \\\n",
       "28   I have several of goodhyouman shirts and i get...  Positive       0.0   \n",
       "104  Runs big and looked unflattering. i am petite,...  Negative       0.0   \n",
       "751  Can be a bit itchy sometimes, i guess dependin...  Positive       0.0   \n",
       "897  I like the skirt. that said it is going to the...  Positive       0.0   \n",
       "976            I was initially attracted to the colors  Positive       0.0   \n",
       "\n",
       "    Sentiment  \n",
       "28    Neutral  \n",
       "104   Neutral  \n",
       "751   Neutral  \n",
       "897   Neutral  \n",
       "976   Neutral  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data['Sentiment'] == 'Neutral'].head()"
   ]
  },
  {
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    "id": "vlpRPzPfsoBv"
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   "source": [
    "### Assessment of received sentiment\n",
    "\n",
    "Now I compare them with originally ranking (Class):"
   ]
  },
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      "displayName": "Anna Staśkiewicz",
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    },
    "id": "Mw_iMBovsoBw",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7731990636455986"
      ]
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     "execution_count": 57,
     "metadata": {},
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   "source": [
    "accuracy_score(data['Class'],data['Sentiment'])"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "colab": {
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    "executionInfo": {
     "elapsed": 532,
     "status": "ok",
     "timestamp": 1738350644212,
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      "displayName": "Anna Staśkiewicz",
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     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.41      0.23      0.29      2370\n",
      "     Neutral       0.16      0.01      0.01      2823\n",
      "    Positive       0.80      0.97      0.88     17448\n",
      "\n",
      "    accuracy                           0.77     22641\n",
      "   macro avg       0.46      0.40      0.39     22641\n",
      "weighted avg       0.68      0.77      0.71     22641\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(data['Class'],data['Sentiment']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p-lMY3UnsoBy"
   },
   "source": [
    "By the TextBlob analysis received results show different sentiment distribution than in Vader method. One can see that fit has achieved 77% accuracy and is slighty better than previous analysis. The TextBlob method assigned even more positive reviews than Vader one and the amount of neutral and negative reviews reduced."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QmSGJ_UusoBz"
   },
   "source": [
    "## Summary\n",
    "\n",
    "This project was aimed sentiment analysis with Vader and TextBlob methods to assigned the sentiment based on reviews and compare their relevance. I have started on Vader model to find a sentiment class and then compared it with original assigned . Based on the results of model one can see that the method assigned more positive reviews than original rating so the amount of neutral and negative reviews reduced. I applied also cleaning data but he results does not change much in comparison to previous model. Next I tested TextBlob model. The received achievments show different sentiment distribution than in Vader method. This method assigned even more positive reviews than Vader one and the amount of neutral and negative reviews reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HPakUKLIsoB0"
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   "outputs": [],
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