What are the differences between various text classification models?

What are the differences between various text classification models?

There are several text classification models, including Naive Bayes, SVM, and deep learning models like BERT. Each has its strengths and weaknesses.

Can you explain how Naive Bayes works?

Naive Bayes is a probabilistic model that applies Bayes' theorem with the assumption of independence among predictors. It's simple and effective for text classification.

What about SVM? How does it compare to Naive Bayes?

Support Vector Machines (SVM) are more complex and can handle non-linear boundaries better than Naive Bayes. They often perform better on high-dimensional data.

And what about deep learning models like BERT?

BERT uses transformers and is pre-trained on large datasets, allowing it to understand context better. It's generally more accurate but requires more computational resources.