Recent Text classification algorithms

 Deep Learning-Based Approaches

• Transformer-Based Models:
  • BERT (Bidirectional Encoder Representations from Transformers)
  • RoBERTa (Robustly Optimized BERT Pretraining Approach) 
  • XLNet
  • GPT-3
  • DistilBERT
• Recurrent Neural Networks (RNNs):
  • Long Short-Term Memory (LSTM), BiLSTM
  • Gated Recurrent Unit (GRU)
• Convolutional Neural Networks (CNNs):
  • CNN
  • TextCNN

• Traditional Machine Learning

  1. Naïve Bayes (NB): Probabilistic; effective for high-dimensional text.
  2. Support Vector Machines (SVM): Strong for sparse data; uses margins to separate classes.
  3. Logistic Regression: Simple and interpretable for binary/multi-class tasks.
  4. k-Nearest Neighbors (k-NN): Uses proximity; expensive for large datasets.
  5. Random Forests: Ensemble-based; reduces overfitting.