If you were to feed the term frequencies directly to a classifier, those very frequent tokens would shadow the frequencies of rarer yet more interesting tokens. According to Zipf’s law, common words like “the”, “a”, and “to” are almost always the terms/tokens with the highest frequency in the document.

Albeit simple, term frequencies are not necessarily the best corpus representation. The number of occurrences of tokens is called term frequency (tf). Count the occurrences of tokens in each document.The token separators could be white spaces and punctuation. Tokenize each document and give an integer id for each token.One of the most intuitive features to create is the number of times each word appears in a document.

Word2Vec ∘ The Basics ∘ Creating Train Data ∘ Continuous Bag of Words & Skip-Gram ∘ Advantages & Limitations.

Bag of Words ∘ The Basics ∘ Example on Data ∘ Advantages & Limitations.

This is called feature extraction.įrom now on, we will call a single observation of text by document and a collection of documents by corpus. So, you need a way to somehow extract meaningful numerical feature vectors from texts. Deep learning models only work on numbers, not sequences of symbols like texts. While image data is straightforward to be used by deep learning models (RGB value as the input), this is not the case for text data.

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