Text Classification

Text Classification is a Supervised Learning algorithm to group and assign predefined categories or labels to text documents or sentences or documents based on their content.

Text Classification predicts classes based on a numerical feature representation. to transform words into numerical features, Word Embedding Techniques are used.


  • Binary classification (text only belongs to one class)
  • Multi-class classification (text can belong to multiple classes)




  • The goal of Text Classification is to assign predefined categories or labels to each document in a Corpus.
  • Feature Representation: Text data is transformed into numerical feature vectors, often using techniques such as Bag of Words (BoW), TF-IDF, Word Embedding, or n-Grams, to represent the content in a machine-readable format.
  • Evaluation Metrics for text classification models include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC).
  • Imbalanced Data: Text classification tasks may encounter class imbalance, where certain categories have significantly fewer instances than others. Handling imbalanced data requires techniques such as oversampling, undersampling, or using evaluation metrics that are robust to imbalanced classes.