Sentiment_analysis.ipynb

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      "source": [
        "\n",
        "import pandas as pd\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from gensim.models import FastText\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
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        "id": "D-8aJNKz1JPx"
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      "execution_count": null,
      "outputs": []
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      "metadata": {
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      "source": [
        "df = pd.read_csv('WCDataset.csv')"
      ]
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      "source": [
        "df.head()"
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              "   Unnamed: 0  Clothing ID  Age                    Title  \\\n",
              "0           0          767   33                      NaN   \n",
              "1           1         1080   34                      NaN   \n",
              "2           2         1077   60  Some major design flaws   \n",
              "3           3         1049   50         My favorite buy!   \n",
              "4           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  "
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        "#Creating a backup dataframe\n",
        "df_backup = df.copy()"
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      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
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        "# Step 4: Dropping unnecessary columns\n",
        "columns_to_drop = ['Unnamed: 0', 'Clothing ID', 'Title']\n",
        "df = df.drop(columns=columns_to_drop)"
      ],
      "metadata": {
        "id": "_0tU1BpCBXJn"
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        "df.head()"
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          "height": 206
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            "text/plain": [
              "   Age                                        Review Text  Rating  \\\n",
              "0   33  Absolutely wonderful - silky and sexy and comf...       4   \n",
              "1   34  Love this dress!  it's sooo pretty.  i happene...       5   \n",
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              "   Recommended IND  Positive Feedback Count   Division Name Department Name  \\\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 23486,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 18,\n        \"max\": 99,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          47,\n          62,\n          41\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review Text\",\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\": \"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 IND\",\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\": \"Positive Feedback Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 122,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          21,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Division Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Initmates\",\n          \"General\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Department Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Intimate\",\n          \"Dresses\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Intimates\",\n          \"Layering\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Handle missing values\n",
        "# Dropping rows with missing values in the 'Review Text' column\n",
        "df = df.dropna(subset=['Review Text'])"
      ],
      "metadata": {
        "id": "Yj-IG0hcCKj8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "oFSCYhSvC3ID",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03340cc1-9331-42c9-ca1c-64ec6e82dae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                         0\n",
              "Review Text                 0\n",
              "Rating                      0\n",
              "Recommended IND             0\n",
              "Positive Feedback Count     0\n",
              "Division Name              13\n",
              "Department Name            13\n",
              "Class Name                 13\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()"
      ],
      "metadata": {
        "id": "1d_MF4UHDCoA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "L_UdxfnYDVHS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eea20a8f-bc7a-4519-ec72-622baee5dc33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 22628 entries, 0 to 23485\n",
            "Data columns (total 8 columns):\n",
            " #   Column                   Non-Null Count  Dtype \n",
            "---  ------                   --------------  ----- \n",
            " 0   Age                      22628 non-null  int64 \n",
            " 1   Review Text              22628 non-null  object\n",
            " 2   Rating                   22628 non-null  int64 \n",
            " 3   Recommended IND          22628 non-null  int64 \n",
            " 4   Positive Feedback Count  22628 non-null  int64 \n",
            " 5   Division Name            22628 non-null  object\n",
            " 6   Department Name          22628 non-null  object\n",
            " 7   Class Name               22628 non-null  object\n",
            "dtypes: int64(4), object(4)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "#Storing the encoded variables in a different variable names so that We can retain the original\n",
        "# Initialize LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Apply LabelEncoder to each categorical column\n",
        "df['Division Name Encoded'] = label_encoder.fit_transform(df['Division Name'])\n",
        "df['Department Name Encoded'] = label_encoder.fit_transform(df['Department Name'])\n",
        "df['Class Name Encoded'] = label_encoder.fit_transform(df['Class Name'])\n",
        "\n",
        "# Display the first few rows of the dataframe after label encoding\n",
        "print(\"Label Encoded DataFrame:\")\n",
        "print(df.head())"
      ],
      "metadata": {
        "id": "HhCJdYCaEJdL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87b4bbab-cfdf-4e4b-f8cf-8e4eba4d033c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label Encoded DataFrame:\n",
            "   Age                                        Review Text  Rating  \\\n",
            "0   33  Absolutely wonderful - silky and sexy and comf...       4   \n",
            "1   34  Love this dress!  it's sooo pretty.  i happene...       5   \n",
            "2   60  I had such high hopes for this dress and reall...       3   \n",
            "3   50  I love, love, love this jumpsuit. it's fun, fl...       5   \n",
            "4   47  This shirt is very flattering to all due to th...       5   \n",
            "\n",
            "   Recommended IND  Positive Feedback Count   Division Name Department Name  \\\n",
            "0                1                        0       Initmates        Intimate   \n",
            "1                1                        4         General         Dresses   \n",
            "2                0                        0         General         Dresses   \n",
            "3                1                        0  General Petite         Bottoms   \n",
            "4                1                        6         General            Tops   \n",
            "\n",
            "  Class Name  Division Name Encoded  Department Name Encoded  \\\n",
            "0  Intimates                      2                        2   \n",
            "1    Dresses                      0                        1   \n",
            "2    Dresses                      0                        1   \n",
            "3      Pants                      1                        0   \n",
            "4    Blouses                      0                        4   \n",
            "\n",
            "   Class Name Encoded  \n",
            "0                   5  \n",
            "1                   3  \n",
            "2                   3  \n",
            "3                  13  \n",
            "4                   0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "hOSQN9F0Ff5R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "726a7603-26ac-4ec9-dfbd-eecae014d975"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 22628 entries, 0 to 23485\n",
            "Data columns (total 11 columns):\n",
            " #   Column                   Non-Null Count  Dtype \n",
            "---  ------                   --------------  ----- \n",
            " 0   Age                      22628 non-null  int64 \n",
            " 1   Review Text              22628 non-null  object\n",
            " 2   Rating                   22628 non-null  int64 \n",
            " 3   Recommended IND          22628 non-null  int64 \n",
            " 4   Positive Feedback Count  22628 non-null  int64 \n",
            " 5   Division Name            22628 non-null  object\n",
            " 6   Department Name          22628 non-null  object\n",
            " 7   Class Name               22628 non-null  object\n",
            " 8   Division Name Encoded    22628 non-null  int64 \n",
            " 9   Department Name Encoded  22628 non-null  int64 \n",
            " 10  Class Name Encoded       22628 non-null  int64 \n",
            "dtypes: int64(7), object(4)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Preprocessing the ReviewText\n",
        "\n",
        "import re\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "\n",
        "def clean_text(text):\n",
        "   # Lowercase the text\n",
        "    text = text.lower()\n",
        "\n",
        "    # Remove digits\n",
        "    text = re.sub(r'\\d+', '', text)\n",
        "\n",
        "    # Remove punctuation\n",
        "    PUNCT_TO_REMOVE = string.punctuation\n",
        "    text = ' '.join(text.translate(str.maketrans('', '', PUNCT_TO_REMOVE)).split())\n",
        "\n",
        "    # Remove stopwords\n",
        "    STOPWORDS = set(stopwords.words('english'))\n",
        "    text = ' '.join([word for word in str(text).split() if word not in STOPWORDS])\n",
        "\n",
        "    # Stem words\n",
        "    stemmer = PorterStemmer()\n",
        "    text = \" \".join([stemmer.stem(word) for word in text.split()])\n",
        "\n",
        "    # Lemmatize words\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    text = \" \".join([lemmatizer.lemmatize(word) for word in text.split()])\n",
        "\n",
        "    return text.strip()"
      ],
      "metadata": {
        "id": "7cQZnhDFFprR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5416b0de-c9fd-4e67-a5ad-8a8064a04b04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cleaned Review Text'] = df['Review Text'].apply(clean_text)"
      ],
      "metadata": {
        "id": "98hHaMNvJvcW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df[['Cleaned Review Text']].head())"
      ],
      "metadata": {
        "id": "PUtfOzBcJwtJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a270eb8-9f82-41d5-a3ae-dd65c212bffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 Cleaned Review Text\n",
            "0                  absolut wonder silki sexi comfort\n",
            "1  love dress sooo pretti happen find store im gl...\n",
            "2  high hope dress realli want work initi order p...\n",
            "3  love love love jumpsuit fun flirti fabul everi...\n",
            "4  shirt flatter due adjust front tie perfect len...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# Assuming 'df' is your existing dataframe\n",
        "df['Keywords'] = ''\n",
        "\n",
        "def extract_keywords(text):\n",
        "    # Create TF-IDF vectorizer\n",
        "    tfidf_vectorizer = TfidfVectorizer(max_features=10)  # Set max_features to the number of keywords you want\n",
        "\n",
        "    # Fit-transform the preprocessed text\n",
        "    tfidf_matrix = tfidf_vectorizer.fit_transform([text])\n",
        "\n",
        "    # Get feature names (keywords)\n",
        "    feature_names = tfidf_vectorizer.get_feature_names_out()\n",
        "\n",
        "    return feature_names\n",
        "\n",
        "# Apply keyword extraction to each row in the dataframe\n",
        "for index, row in df.iterrows():\n",
        "    keywords = extract_keywords(row['Cleaned Review Text'])\n",
        "    df.at[index, 'Keywords'] = ', '.join(keywords)\n",
        "\n"
      ],
      "metadata": {
        "id": "1n_evhcFKuJS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the dataframe with extracted keywords\n",
        "print(df.head())"
      ],
      "metadata": {
        "id": "RhTVV5GvNoPh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b7cbc037-4139-4321-b182-f778b5755146"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Age                                        Review Text  Rating  \\\n",
            "0   33  Absolutely wonderful - silky and sexy and comf...       4   \n",
            "1   34  Love this dress!  it's sooo pretty.  i happene...       5   \n",
            "2   60  I had such high hopes for this dress and reall...       3   \n",
            "3   50  I love, love, love this jumpsuit. it's fun, fl...       5   \n",
            "4   47  This shirt is very flattering to all due to th...       5   \n",
            "\n",
            "   Recommended IND  Positive Feedback Count   Division Name Department Name  \\\n",
            "0                1                        0       Initmates        Intimate   \n",
            "1                1                        4         General         Dresses   \n",
            "2                0                        0         General         Dresses   \n",
            "3                1                        0  General Petite         Bottoms   \n",
            "4                1                        6         General            Tops   \n",
            "\n",
            "  Class Name  Division Name Encoded  Department Name Encoded  \\\n",
            "0  Intimates                      2                        2   \n",
            "1    Dresses                      0                        1   \n",
            "2    Dresses                      0                        1   \n",
            "3      Pants                      1                        0   \n",
            "4    Blouses                      0                        4   \n",
            "\n",
            "   Class Name Encoded                                Cleaned Review Text  \\\n",
            "0                   5                  absolut wonder silki sexi comfort   \n",
            "1                   3  love dress sooo pretti happen find store im gl...   \n",
            "2                   3  high hope dress realli want work initi order p...   \n",
            "3                  13  love love love jumpsuit fun flirti fabul everi...   \n",
            "4                   0  shirt flatter due adjust front tie perfect len...   \n",
            "\n",
            "                                            Keywords  \n",
            "0              absolut, comfort, sexi, silki, wonder  \n",
            "1  bc, bought, definit, find, glad, hit, im, love...  \n",
            "2  bottom, cheap, comfort, fact, fit, half, layer...  \n",
            "3  compliment, everi, fabul, flirti, fun, get, gr...  \n",
            "4  adjust, cardigan, due, flatter, front, leg, le...  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna(subset=['Sentiment'])\n"
      ],
      "metadata": {
        "id": "H8ir9gqRkGmu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "from gensim.models import FastText\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(df['Cleaned Review Text'], df['Sentiment'], test_size=0.2, random_state=42)\n",
        "\n",
        "# Train FastText model\n",
        "ft_model = FastText(sentences=X_train, vector_size=100, window=5, min_count=1, workers=4, sg=1)\n",
        "\n",
        "# Get sentence embeddings\n",
        "def get_sentence_embedding(sentence, model):\n",
        "    words = sentence.split()\n",
        "    embeddings = [model.wv[word] for word in words if word in model.wv]\n",
        "    if embeddings:\n",
        "        return np.mean(embeddings, axis=0)\n",
        "    else:\n",
        "        return np.zeros(model.vector_size)\n",
        "\n",
        "# Generate embeddings for training and testing data\n",
        "X_train_ft = np.array([get_sentence_embedding(sentence, ft_model) for sentence in X_train])\n",
        "X_test_ft = np.array([get_sentence_embedding(sentence, ft_model) for sentence in X_test])\n",
        "\n",
        "# Use any classification algorithm (e.g., Logistic Regression) for sentiment classification\n",
        "lr_model = LogisticRegression(max_iter=1000)\n",
        "lr_model.fit(X_train_ft, y_train)\n",
        "\n",
        "# Predict sentiment on the test data\n",
        "y_pred = lr_model.predict(X_test_ft)\n",
        "\n",
        "# Calculate accuracy and print classification report\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "# Add the predicted sentiment column to the DataFrame\n",
        "df['Predicted Sentiment'] = lr_model.predict(np.array([get_sentence_embedding(sentence, ft_model) for sentence in df['Cleaned Review Text']]))\n",
        "\n",
        "# Display the updated DataFrame\n",
        "print(df.head())\n"
      ],
      "metadata": {
        "id": "tft0c6apQNMu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67bcb892-e1a0-499e-b7d2-d9b126e9441d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:gensim.models.word2vec:Each 'sentences' item should be a list of words (usually unicode strings). First item here is instead plain <class 'str'>.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.999448275862069\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.00      0.00      0.00         2\n",
            "         1.0       1.00      1.00      1.00      3623\n",
            "\n",
            "    accuracy                           1.00      3625\n",
            "   macro avg       0.50      0.50      0.50      3625\n",
            "weighted avg       1.00      1.00      1.00      3625\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Age                                        Review Text  Rating  \\\n",
            "0   33  Absolutely wonderful - silky and sexy and comf...       4   \n",
            "1   34  Love this dress!  it's sooo pretty.  i happene...       5   \n",
            "2   60  I had such high hopes for this dress and reall...       3   \n",
            "3   50  I love, love, love this jumpsuit. it's fun, fl...       5   \n",
            "4   47  This shirt is very flattering to all due to th...       5   \n",
            "\n",
            "   Recommended IND  Positive Feedback Count   Division Name Department Name  \\\n",
            "0                1                        0       Initmates        Intimate   \n",
            "1                1                        4         General         Dresses   \n",
            "2                0                        0         General         Dresses   \n",
            "3                1                        0  General Petite         Bottoms   \n",
            "4                1                        6         General            Tops   \n",
            "\n",
            "  Class Name  Division Name Encoded  Department Name Encoded  \\\n",
            "0  Intimates                      2                        2   \n",
            "1    Dresses                      0                        1   \n",
            "2    Dresses                      0                        1   \n",
            "3      Pants                      1                        0   \n",
            "4    Blouses                      0                        4   \n",
            "\n",
            "   Class Name Encoded                                Cleaned Review Text  \\\n",
            "0                   5                  absolut wonder silki sexi comfort   \n",
            "1                   3  love dress sooo pretti happen find store im gl...   \n",
            "2                   3  high hope dress realli want work initi order p...   \n",
            "3                  13  love love love jumpsuit fun flirti fabul everi...   \n",
            "4                   0  shirt flatter due adjust front tie perfect len...   \n",
            "\n",
            "                                            Keywords  Sentiment  \\\n",
            "0              absolut, comfort, sexi, silki, wonder        1.0   \n",
            "1  bc, bought, definit, find, glad, hit, im, love...        1.0   \n",
            "2  bottom, cheap, comfort, fact, fit, half, layer...        1.0   \n",
            "3  compliment, everi, fabul, flirti, fun, get, gr...        1.0   \n",
            "4  adjust, cardigan, due, flatter, front, leg, le...        1.0   \n",
            "\n",
            "   Predicted Sentiment  \n",
            "0                  1.0  \n",
            "1                  1.0  \n",
            "2                  1.0  \n",
            "3                  1.0  \n",
            "4                  1.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_drop = ['Division Name','Department Name','Class Name','Review Text','Predicted Sentiment']\n",
        "\n",
        "df = df.drop(columns=columns_to_drop)"
      ],
      "metadata": {
        "id": "WNkw2EImjafE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYraXIg3lSlB",
        "outputId": "9d6c0b67-29b7-4274-8d71-4ae552fd1e03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 18122 entries, 0 to 18805\n",
            "Data columns (total 10 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   Age                      18122 non-null  int64  \n",
            " 1   Rating                   18122 non-null  int64  \n",
            " 2   Recommended IND          18122 non-null  int64  \n",
            " 3   Positive Feedback Count  18122 non-null  int64  \n",
            " 4   Division Name Encoded    18122 non-null  int64  \n",
            " 5   Department Name Encoded  18122 non-null  int64  \n",
            " 6   Class Name Encoded       18122 non-null  int64  \n",
            " 7   Cleaned Review Text      18122 non-null  object \n",
            " 8   Keywords                 18122 non-null  object \n",
            " 9   Sentiment                18122 non-null  float64\n",
            "dtypes: float64(1), int64(7), object(2)\n",
            "memory usage: 1.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming df is your DataFrame containing sentiment data with columns 'Class Name Encoded' and 'Sentiment'\n",
        "# Assuming you have already defined the get_sentiment_label function\n",
        "\n",
        "# Group by product (Class Name Encoded) and calculate mean sentiment\n",
        "product_sentiment = df.groupby('Class Name Encoded')['Sentiment'].mean().reset_index()\n",
        "\n",
        "# Define sentiment labels\n",
        "def get_sentiment_label(sentiment):\n",
        "    if 0.9 <= sentiment < 0.99:\n",
        "        return 'Neutral'\n",
        "    elif sentiment >= 0.99:\n",
        "        return 'Good'\n",
        "    else:\n",
        "        return 'Bad'\n",
        "\n",
        "product_sentiment['Sentiment_Label'] = product_sentiment['Sentiment'].apply(get_sentiment_label)\n",
        "\n",
        "# Create scatter plot\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.scatterplot(x='Class Name Encoded', y='Sentiment', data=product_sentiment, hue='Sentiment_Label', palette='viridis')\n",
        "plt.xlabel('Product (Class Name Encoded)')\n",
        "plt.ylabel('Mean Sentiment')\n",
        "plt.title('Mean Sentiment for Each Product')\n",
        "plt.legend(title='Sentiment Label', loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "bjCvHocHvqa-",
        "outputId": "923fc8fb-74a5-493f-ae9f-018ceba466c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Group by age and calculate mean sentiment\n",
        "age_sentiment = df.groupby('Age')['Sentiment'].mean().reset_index()\n",
        "\n",
        "# Define sentiment labels\n",
        "def get_sentiment_label(sentiment):\n",
        "    if 0.9 <= sentiment < 0.99:\n",
        "        return 'Bad'\n",
        "    elif sentiment == 1:\n",
        "        return 'Good'\n",
        "    else:\n",
        "        return 'Other'\n",
        "\n",
        "age_sentiment['Sentiment_Label'] = age_sentiment['Sentiment'].apply(get_sentiment_label)\n",
        "\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x='Age', y='Sentiment', data=age_sentiment, hue='Sentiment_Label', palette={'Bad': 'red', 'Good': 'green', 'Other': 'blue'})\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Mean Sentiment')\n",
        "plt.title('Mean Sentiment for Each Age Group')\n",
        "plt.legend(title='Sentiment Label', loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "gFh33zKKx9mK",
        "outputId": "659277a6-a733-4dc0-b963-100b7bb525f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the age bins\n",
        "bins = [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100]\n",
        "\n",
        "# Create age groups\n",
        "df['Age Group'] = pd.cut(df['Age'], bins=bins, right=False)\n",
        "\n",
        "# Group by age group and calculate mean sentiment\n",
        "age_group_sentiment = df.groupby('Age Group')['Sentiment'].mean().reset_index()\n",
        "\n",
        "# Define sentiment labels\n",
        "def get_sentiment_label(sentiment):\n",
        "    if 0.9 <= sentiment < 0.99:\n",
        "        return 'Bad'\n",
        "    elif sentiment == 1:\n",
        "        return 'Good'\n",
        "    else:\n",
        "        return 'Other'\n",
        "\n",
        "age_group_sentiment['Sentiment_Label'] = age_group_sentiment['Sentiment'].apply(get_sentiment_label)\n",
        "\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x='Age Group', y='Sentiment', data=age_group_sentiment, hue='Sentiment_Label', palette={'Bad': 'red', 'Good': 'green', 'Other': 'blue'})\n",
        "plt.xlabel('Age Group')\n",
        "plt.ylabel('Mean Sentiment')\n",
        "plt.title('Mean Sentiment for Each Age Group')\n",
        "plt.xticks(rotation=45)\n",
        "plt.legend(title='Sentiment Label', loc='upper left')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "id": "THvrqTR7yeJr",
        "outputId": "b0a98d7a-1c0f-4685-f29d-8d94d54936e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Assuming df is your DataFrame containing sentiment data with columns 'Rating' and 'Sentiment'\n",
        "\n",
        "# Group by rating and calculate mean sentiment\n",
        "rating_sentiment = df.groupby('Rating')['Sentiment'].mean().reset_index()\n",
        "\n",
        "# Visualize the data\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x='Rating', y='Sentiment', data=rating_sentiment, palette='viridis')\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Mean Sentiment')\n",
        "plt.title('Mean Sentiment for Each Rating')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "id": "umhLl4phy1LW",
        "outputId": "005a92d4-bffb-420d-a8fb-37f894e171db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-b242ee69b1df>:12: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='Rating', y='Sentiment', data=rating_sentiment, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example for 'Division Name Encoded' column\n",
        "encoded_values = df['Division Name Encoded'].unique()\n",
        "decoded_values = label_encoder.inverse_transform(encoded_values)\n",
        "\n",
        "# Create a dictionary to map encoded values to decoded values\n",
        "encoded_decoded_map = dict(zip(encoded_values, decoded_values))\n",
        "\n",
        "# Print the mapping\n",
        "print(\"Encoded to Decoded Mapping for 'Division Name Encoded':\")\n",
        "print(encoded_decoded_map)\n"
      ],
      "metadata": {
        "id": "8eV2zgvzzXoP",
        "outputId": "f5a392a8-d5fb-40b9-9cd6-dc84ca96f5e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Encoded to Decoded Mapping for 'Division Name Encoded':\n",
            "{2: 'Chemises', 0: 'Blouses', 1: 'Casual bottoms'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DdACdLO-6HMG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}