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How to Do Preprocessing for Text Data Using TF-IDF Vectorizer in Python

TF-IDF Vectorizer Preprocessing

Condition for TF-IDF Vectorizer Preprocessing

  • Description:
    TF-IDF (Term Frequency-Inverse Document Frequency) is a method for transforming text data into numerical features. The process involves:

    Term Frequency (TF): Measures how frequently a term appears in a document.
    Inverse Document Frequency (IDF): Measures how important a term is across all documents.
    TF-IDF: The product of TF and IDF gives us a score that represents the importance of each word in a document relative to the entire dataset.
Step-by-Step Process
  • Import Required Libraries:
    Import TfidfVectorizer from scikit-learn to handle the transformation.
  • Prepare the Dataset:
    Create or load the text dataset (a collection of documents or sentences).
  • Initialize the TF-IDF Vectorizer:
    Configure the vectorizer with parameters like max_features, stop_words, etc.
  • Fit and Transform the Data:
    Fit the vectorizer to the text data and transform the text into a matrix of TF-IDF features.
  • Use the Transformed Data:
    The output is a sparse matrix representing the documents with their corresponding TF-IDF values.
Sample Source Code
  • # Code for text preprocessing using tf-idf vectorizer

    import pandas as pd
    from sklearn.feature_extraction.text import TfidfVectorizer

    documents = [
    "The cat sat on the mat.",
    "The dog sat on the log.",
    "The cat and the dog played together.",
    "Cats are very clever animals.",
    "Dogs are loyal companions."
    ]

    vectorizer = TfidfVectorizer(stop_words='english')

    X = vectorizer.fit_transform(documents)

    df_tfidf = pd.DataFrame(X.toarray(), columns=vectorizer.get_feature_names_out())

    print(df_tfidf)
Screenshots
  • TF-IDF Vectorizer Output

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