We now turn to the consideration of cases of working specifically with text data. The goal is to identify the relevant information within the text, such as words or phrases, and convert them into a format that can be easily understood by a computer. This process involves various techniques such as tokenization, stopword removal, stemming, and vectorization. The resulting features can be used to build predictive models for various natural language processing (NLP) tasks, such as sentiment analysis, topic modeling, and text classification.

There are several methods for feature extraction from text, but some of the most commonly used ones include:

1. Bag of words (BoW) - a method that represents text as a set of unique words, ignoring the order of words and the grammar of the sentences. It works by counting the frequency of each word in the text and creating a vector of those frequencies.
2. Term frequency-inverse document frequency (TF-IDF) - a method that takes into account the importance of each word in the text by calculating the frequency of a word in a document (term frequency) and the frequency of that word in the entire corpus of documents (inverse document frequency). This results in a vector of importance scores for each word in the text.
3. Word embeddings - a method that represents words in a continuous vector space, capturing the semantic relationships between words. This is achieved by training a neural network on a large corpus of text to predict the context in which a word appears.
4. Latent Dirichlet allocation (LDA) - a method for topic modeling, which represents each document as a mixture of topics, where each topic is represented by a distribution of words. LDA can be used to extract features from the text by identifying the most relevant topics for a given document or corpus.

Now we will not delve into the mathematical theory of each method but only mention that the most efficient text representation at the moment is performed based on the word embeddings method.