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Why Text Needs to Be Converted into Numbers

Why Text Needs to Be Converted into Numbers

Machine learning algorithms cannot directly understand text. They can only process numerical data. Therefore, before training a model, we must convert the review text into numerical features.

There are several techniques for converting text into numbers, such as:

  • Bag of Words (BoW)
  • TF-IDF (Term Frequency-Inverse Document Frequency)
  • Word2Vec
  • GloVe
  • BERT Embeddings

In this project, we will use TF-IDF, one of the most popular and effective techniques for text classification.

Why Use TF-IDF?

TF-IDF helps identify the importance of words in a review by assigning higher weights to words that are frequent in a specific review but less common across all reviews.

Advantages of TF-IDF

  • Reduces the importance of common words.
  • Highlights meaningful words.
  • Improves classification accuracy.
  • Efficient for text classification tasks.
  • Widely used in Natural Language Processing.