© Akshay Kulkarni and Adarsha Shivananda 2019
Akshay Kulkarni and Adarsha ShivanandaNatural Language Processing Recipeshttps://doi.org/10.1007/978-1-4842-4267-4_3

3. Converting Text to Features

Akshay Kulkarni1  and Adarsha Shivananda1
(1)
Bangalore, Karnataka, India
 
In this chapter, we are going to cover basic to advanced feature engineering (text to features) methods. By the end of this chapter, you will be comfortable with the following recipes:
  • Recipe 1. One Hot encoding

  • Recipe 2. Count vectorizer

  • Recipe 3. N-grams

  • Recipe 4. Co-occurrence matrix

  • Recipe 5. Hash vectorizer

  • Recipe 6. Term Frequency-Inverse Document Frequency (TF-IDF)

  • Recipe 7. Word embedding

  • Recipe 8. Implementing fastText

Now that all the text preprocessing steps are discussed, let’s ...

Get Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.