AI NLP

To convert words into numerical formats that computers can process

To translate languages automatically

To store large amounts of text data efficiently

To create images from text

Common everywhere

Rare or unique

Repeated

Translated

It counts how often each word appears in a text.

It analyzes the meaning of each word.

It assigns colors to words based on frequency.

It converts words into sounds.

Bag of Words only counts word frequency, while TF-IDF considers both frequency and importance.

Bag of Words uses word order, while TF-IDF ignores it.

TF-IDF only works for English text, while Bag of Words works for all languages.

Bag of Words is used for images, while TF-IDF is used for text.

High

Low

Average

Zero

Bag of Words

TF-IDF

Tokenization

Lemmatization

So computers can process and compare words

To change language

To make it shorter

To hide data

Bag of Words considers word frequency only, while TF-IDF also considers how unique a word is across documents.

Bag of Words uses neural networks, while TF-IDF does not.

TF-IDF ignores word frequency, while Bag of Words does not.

Bag of Words is only used for images, while TF-IDF is used for text.